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{
"permissions": {
"allow": [
"Bash(npm run build:*)",
"Bash(python:*)",
"Bash(npm ls:*)",
"Bash(npm run dev:*)",
"Bash(del \"E:\\\\GY_WORKING\\\\__CLAUDE_CODE_TESTING\\\\NEW_OSINT_AGGREGATOR\\\\techscout\\\\package-lock.json\")",
"Bash(curl:*)",
"Bash(npx next build)",
"Bash(taskkill:*)",
"Bash(timeout:*)",
"Bash(dir:*)",
"Bash(pip install:*)"
]
}
}

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# Python bytecode
__pycache__/
*.py[cod]
*$py.class
# Virtual environments
venv/
env/
.env
# IDE settings
.idea/
.vscode/
*.swp
*.swo
# OS files
.DS_Store
Thumbs.db

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============================================================
TECHSCOUT - Technology Scouting & Capability Gap Analysis
============================================================
TechScout helps you discover technologies that address capability gaps.
Simply describe what you need in plain English, and TechScout will:
- Search SBIR/STTR awards, patents, federal contracts, and news
- Score results based on relevance to your needs
- Rank candidates by problem-solution fit
- Enable deep-dive analysis on promising technologies
QUICK START
-----------
1. FIRST TIME SETUP (only once):
- Double-click: install.bat
- Wait for installation to complete
2. LAUNCH TECHSCOUT:
- Double-click: launch.bat
- It will automatically start Ollama, API server, and dashboard
- Your browser will open to http://localhost:3001
REQUIREMENTS
------------
Before installing, you need:
1. Python (https://www.python.org/downloads/)
- Check "Add Python to PATH" during install
2. Node.js (https://nodejs.org/)
- Download the LTS version
3. Ollama (https://ollama.ai)
- Download and install
- The mistral-nemo:12b model will download automatically
HOW TO USE
----------
1. Enter your capability gap in plain English, e.g.:
"Need a technology that improves Space Domain Awareness
for objects beyond the diffraction limit"
2. Click "Discover Technologies"
3. Wait for results (typically 1-3 minutes)
4. Browse ranked candidates
- Click any candidate to see details
- Green = SBIR awards
- Orange = Patents
- Blue = Federal contracts
5. Export results to CSV for team review
TROUBLESHOOTING
---------------
"Ollama not running" error:
- Make sure start_ollama.bat is running in another window
"Cannot connect to API server" error:
- The launch.bat should auto-start the API server
- Look for a "TechScout API" window - it should show
"TechScout API Server running on http://localhost:8000"
- If not visible, manually run: start_api.bat
"Discovery failed" error:
- Check that Ollama has the mistral-nemo:12b model
- Run: ollama pull mistral-nemo:12b
Dashboard won't start:
- Make sure Node.js is installed
- Run install.bat again
Slow performance:
- First searches download AI model (~4GB)
- Subsequent searches will be faster
- Discovery typically takes 1-2 minutes
SUPPORT
-------
For issues, contact your system administrator or check:
https://github.com/anthropics/claude-code/issues
============================================================

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{
"id": "15b103f9",
"candidate_id": "",
"organization": "Massachusetts Institute of Technology",
"timestamp": "2026-01-22T09:49:42.704999",
"company_profile": {
"name": "Massachusetts Institute of Technology",
"description": "Founded to accelerate the nation's industrial revolution, MIT is profoundly American and deeply engaged with the world.",
"website": "https://www.mit.edu/",
"headquarters": "77 Massachusetts Avenue, Cambridge, MA",
"employee_count": null,
"founded": 1861,
"leadership": null
},
"technology_profile": {
"name": "Super-Resolution Imaging with Spatial Light Modulator",
"description": "A technique that combines an optical device and a Spatial Light Modulator (SLM) to achieve super-resolution imaging, enabling the acquisition and resolution of remote objects beyond the diffraction limit of the optical device.",
"technical_approach": "The SLM is used to introduce phase shifts into the incoming light wavefront, allowing for the capture of multiple low-resolution images with varying phases. These images are then combined using algorithms such as Iterative Phase Retrieval or Gerchberg-Saxton algorithm to reconstruct a high-resolution image.",
"trl_assessment": 7,
"key_capabilities": [
"Enables resolution beyond the diffraction limit",
"Improves Space Domain Awareness by allowing for better detection and tracking of small, distant objects",
"Flexible and adaptable to various optical systems"
],
"limitations": [
"Requires complex algorithms for image reconstruction",
"Sensitive to noise and aberrations in the optical system",
"Limited by the pixel size and fill factor of the SLM"
],
"competitive_advantage": "This approach offers a significant improvement in resolution without requiring expensive hardware upgrades or the use of exotic materials, making it a cost-effective solution for enhancing Space Domain Awareness.",
"related_patents": []
},
"contract_history": {
"total_contracts": 0,
"total_value": 0,
"primary_agencies": [],
"recent_contracts": [],
"sbir_awards": []
},
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"title": "Massachusetts Institute of Technology | LinkedIn",
"description": "The Massachusetts Institute of Technology (MIT) is a private research university located in Cambridge, Massachusetts.Find people you know at Massachusetts Institute of Technology . Browse recommended jobs for you. View all updates, news, and articles.",
"url": "https://www.linkedin.com/school/mit/"
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"title": "Spotlight: Electrifying steam production | MIT - Massachusetts ...",
"description": "Massachusetts Institute of Technology . Education.diagram of heat pump and boiler. Spotlight: Jan 21, 2026. \u201cSteam is the most important working fluid ever,\u201d Addison Stark SM \u201910, PhD \u201914 says.",
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"title": "Massachusetts Institute of Technology | MIT - Requirements + Data",
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"title": "Neri Oxman's research works | Massachusetts Institute of ...",
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"url": "https://www.ghostproductions.com/client/massachusetts-institute-of-technology"
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"title": "Massachusetts Institute of Technology : Students imagine better...",
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"title": "MIT - Massachusetts Institute of Technology | web.mit.edu Reviews",
"description": "Massachusetts Institute of Technology . Machining Skills for Prototype Development Massachusetts Institute of Technology . Friday, August 14, 2015. Microfluidics device better models how cancer and other cells interact.",
"url": "https://www.pageglimpse.org/web.mit.edu"
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"title": "Why MIT Startups Are Dominating Every Industry... | Medium",
"description": "The Massachusetts Institute of Technology (MIT) is synonymous with cutting-edge technology and innovation, and it\u2019s no surprise that its\u2026Why it stands out: HubSpot democratized access to enterprise-level marketing tools, offering solutions to businesses of all sizes.",
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}
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"date": "2024-11-21T23:59:00+00:00",
"url": "https://abc7.com/post/massachusetts-institute-technology-waive-tuition-families-making-less-200k/15572704/"
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"date": "2025-11-28T15:49:32+00:00",
"url": "https://www.timeout.com/news/these-are-officially-the-worlds-top-universities-for-each-subject-in-2026-012226"
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"url": "https://www.marshallindependent.com/news/local-news/2026/01/marshall-seniors-scholarship-to-mit-highlights-tiger-spotlight-2/"
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{
"title": "Economists are studying the slowing job market\u2014and feeling it themselves",
"snippet": "Newly minted Ph.D.s tend to work for universities, government agencies and big white-collar companies. It\u2019s not a great ...",
"date": "2026-01-19T15:49:32+00:00",
"url": "https://www.msn.com/en-us/money/economy/economists-are-studying-the-slowing-job-market-and-feeling-it-themselves/ar-AA1UsgOB?ocid=BingNewsVerp"
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"title": "Predictions: sustainability trends 2026",
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"date": "2026-01-21T15:49:32+00:00",
"url": "https://wfuogb.com/28691/environment/predictions-sustainability-trends-2026/"
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"title": "University Administration Releases Emergency Preparedness Guidelines, Will \u2018Reassess\u2019 Safety Procedures Following Brown Shooting",
"snippet": "In a statement to the Cornell community, University administrators detailed emergency preparedness guidelines and intentions ...",
"date": "2026-01-12T15:49:32+00:00",
"url": "https://cornellsun.com/article/2026/01/university-administration-releases-emergency-preparedness-guidelines-will-reassess-safety-procedures-following-brown-shooting"
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{
"title": "The cost of delivery: Fast shipping has become more polluting around the world",
"snippet": "You shop, find something you want and click to buy. It shows up today, overnight or tomorrow. We've gotten used to that speed. But that convenience comes with a climate cost.",
"date": "2026-01-17T15:49:32+00:00",
"url": "https://www.thestar.com.my/lifestyle/living/2026/01/17/the-cost-of-delivery-fast-shipping-has-become-more-polluting-around-the-world"
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{
"title": "Automation Can Combat \u2018Brain Fade\u2019 in Finance",
"snippet": "Small employee mistakes can have massive consequences for financial institutions, which spend $5.75 for every $1 lost to ...",
"date": "2026-01-07T15:49:32+00:00",
"url": "https://biztechmagazine.com/article/2026/01/automation-can-combat-brain-fade-finance"
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{
"title": "Researchers make surprising discovery that could revolutionize construction industry: 'Capability to reuse'",
"snippet": "It's an innovation with potential to improve two crucial products. Researchers make surprising discovery that could ...",
"date": "2026-01-20T15:49:32+00:00",
"url": "https://www.msn.com/en-us/technology/general/researchers-make-surprising-discovery-that-could-revolutionize-construction-industry-capability-to-reuse/ar-AA1UJQOb?ocid=BingNewsVerp"
}
],
"assessment": "The technology developed by MIT, utilizing Spatial Light Modulators (SLMs) for super-resolution imaging, directly addresses the capability gap of improving Space Domain Awareness for objects beyond the diffraction limit. This technique has reached a Technology Readiness Level (TRL) of 7, indicating that it is ready for system-level testing in relevant environments. MIT's involvement brings credibility and expertise in optics and photonics, making this technology a strong fit for the given capability gap.",
"risk_factors": [
"The transition from lab-scale demonstrations to space-based applications may introduce unforeseen challenges due to the harsh space environment.",
"Integration with existing space-based observation systems might require significant effort and resources.",
"Dependence on third-party suppliers for SLM components could lead to potential supply chain issues."
],
"recommendation": "STRONGLY RECOMMEND: Given the direct alignment of MIT's technology with the capability gap, its advanced TRL, and the institution's proven track record in innovation, a strong recommendation is warranted. However, potential risks should be carefully assessed and mitigated during integration and deployment.",
"duration_seconds": 30.806462,
"success": true,
"error": null
}

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{
"id": "270fcdb6",
"candidate_id": "",
"organization": "Palantir Technologies",
"timestamp": "2026-01-15T09:18:48.354617",
"company_profile": {
"name": "Palantir Technologies",
"description": "Palantir Technologies makes products for human-driven data analysis.",
"website": "www.palantir.com",
"headquarters": "1200 17th Street Floor 15, Denver, CO 80202, United States",
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"title": "CEO"
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"name": "Palantir Technologies' AI-Powered Space Domain Awareness Solution",
"description": "An artificial intelligence-driven system designed to enhance space domain awareness by analyzing vast amounts of data from various sources, including satellites, ground stations, and other sensors. The technology integrates machine learning algorithms to predict and track objects beyond the diffraction limit.",
"technical_approach": "Palantir's platform, Foundry, serves as the backbone for this solution. It integrates and analyzes data from diverse sources in real-time, enabling users to gain insights into space domain awareness. The system uses advanced machine learning algorithms to predict and track objects beyond the diffraction limit by identifying patterns, anomalies, and trends in the data.",
"trl_assessment": 7,
"key_capabilities": [
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"AI-driven predictive modeling for tracking objects beyond the diffraction limit",
"Scalable and customizable platform to adapt to various space domain awareness needs",
"Secure and collaborative environment for sharing insights among stakeholders"
],
"limitations": [
"Dependence on reliable data feeds, which may be affected by factors such as signal interference or loss of communication",
"Potential latency in processing and analyzing large volumes of data",
"Requires significant computational resources and expertise to operate effectively"
],
"competitive_advantage": "Palantir's strength lies in its ability to integrate and analyze vast amounts of complex, diverse data from various sources. Its platform, Foundry, is designed for scalability and customization, making it adaptable to the unique needs of space domain awareness. Additionally, Palantir's experience working with governments and intelligence agencies on sensitive projects demonstrates its capability to handle classified information securely.",
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"title": "Palantir Technologies - Wikipedia",
"description": "Palantir Technologies Inc. is an American publicly traded company that develops data integration and analytics platforms enabling government agencies, ... History \u00b7 Products \u00b7 Customers",
"url": "https://en.wikipedia.org/wiki/Palantir_Technologies"
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"title": "Palantir Offerings",
"description": "Build the connected grid of the future. Learn more \u2192. \u00a9 2026 Palantir Technologies Inc. ... and the services we are able to offer. More information. Strictly ...",
"url": "https://www.palantir.com/offerings/"
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"title": "Palantir Platforms",
"description": "\u00a9 2026 Palantir Technologies Inc. All rights reserved. Cookies Settings. USUK ... and the services we are able to offer. More information. Strictly ...",
"url": "https://www.palantir.com/platforms/"
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{
"title": "Palantir: Home",
"description": "\u00a9 2026 Palantir Technologies Inc. All rights reserved. Cookies Settings. USUK ... and the services we are able to offer. More information. Strictly Necessary ... Offerings \u00b7 Palantir Explained \u00b7 Gotham \u00b7 Careers",
"url": "https://www.palantir.com/"
},
{
"title": "What Does Palantir Technologies Do? - GraniteShares",
"description": "Jun 30, 2025 \u00b7 Palantir Technologies Inc. (NYSE: PLTR) is a publicly traded American software company specializing in big data analytics.",
"url": "https://graniteshares.com/institutional/us/en-us/research/what-does-palantir-technologies-do/"
},
{
"title": "Offerings | Telecommunications - Palantir",
"description": "Alex Karp, CEO of Palantir Technologies, and Timotheus H\u00f6ttges, CEO of Deutsche Telekom AG, speak at DIGITAL X on September 7th, 2021 in Cologne, Germany.",
"url": "https://www.palantir.com/offerings/telecommunications/"
},
{
"title": "Palantir | Carahsoft",
"description": "Palantir Technologies is an American dual use technology company that builds solutions for data-driven operations and decision-making. We partner with ...",
"url": "https://www.carahsoft.com/palantir"
},
{
"title": "Snowflake and Palantir Announce Strategic Partnership for Enterprise ...",
"description": "\u201cFirst class interoperability means our customers win,\u201d said Ted Mabrey, Global Head of Commercial at Palantir Technologies. \u201cWe are excited to ...",
"url": "https://www.snowflake.com/en/news/press-releases/snowflake-palantir-announce-strategic-partnership-for-enterprise-ready-ai-analytics/"
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"title": "What is Palantir? (Part 1 DD) : r/PLTR - Reddit",
"description": "Aug 31, 2022 \u00b7 Suppose your solution and data security are crappy. In that case ... Palantir Technologies Inc. ($PLTR) Stock: Unofficial, independent ...",
"url": "https://www.reddit.com/r/PLTR/comments/x2iga2/what_is_palantir_part_1_dd/"
},
{
"title": "Palantir and Accenture Federal Services Join Forces to Help ...",
"description": "Jun 30, 2025 \u00b7 Palantir and Accenture Federal Services Join Forces to Help Federal Government Agencies Reinvent Operations with AI \u00b7 About Palantir Technologies ...",
"url": "https://newsroom.accenture.com/news/2025/palantir-and-accenture-federal-services-join-forces-to-help-federal-government-agencies-reinvent-operations-with-ai"
}
],
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"title": "Why Palantir Technologies surged 135% in 2025, and why it could go even higher",
"snippet": "Shares of Palantir Technologies (NASDAQ: PLTR) took flight in 2025, soaring 135% for the year. The dawn of generative AI represented a singular opportunity, and the company tapped into its vast experience to help usher businesses into the 21st century.",
"date": "2026-01-07T09:05:00+00:00",
"url": "https://www.msn.com/en-us/technology/artificial-intelligence/why-palantir-technologies-surged-135-in-2025-and-why-it-could-go-even-higher/ar-AA1TJpCA"
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"title": "AI Startup Percepta Slams Palantir's 'Baseless' Lawsuit, Calls It Bid To 'Destroy' Competition",
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"date": "2026-01-14T11:21:00+00:00",
"url": "https://www.benzinga.com/news/legal/26/01/49900940/ai-startup-percepta-slams-palantirs-baseless-lawsuit-calls-it-bid-to-destroy-competition"
},
{
"title": "Palantir Is Poised to Mint 10,000 Millionaires. Will You Be One of Them?",
"snippet": "Palantir is outpacing Microsoft's growth in its early years and is poised to make just as many (if not more) millionaires.",
"date": "2026-01-14T08:20:00+00:00",
"url": "https://www.msn.com/en-us/money/savingandinvesting/palantir-is-poised-to-mint-10000-millionaires-will-you-be-one-of-them/ar-AA1U9TP8"
},
{
"title": "Palantir's stock could benefit from this 'supercycle' that turbocharges growth",
"snippet": "Palantir's U.S. commercial business is on fire thanks to AI and could help fuel a further reacceleration of growth, beyond the impressive performance the company has posted recently.",
"date": "2026-01-12T17:42:00+00:00",
"url": "https://www.msn.com/en-us/money/topstocks/palantir-s-stock-could-benefit-from-this-supercycle-that-turbocharges-growth/ar-AA1U4AmD"
},
{
"title": "Palantir Technologies (NASDAQ: PLTR) Price Prediction and Forecast 2026-2030 for January 12",
"snippet": "Demand for Big Data services is increasing. Palantir, a leader in the field, should benefit. Here are the forecasts and price predictions for PLTR stock.",
"date": "2026-01-12T15:59:00+00:00",
"url": "https://247wallst.com/forecasts/2026/01/12/palantir-technologies-pltr-price-prediction-and-forecast-2025-2030/"
},
{
"title": "Palantir Technologies (NASDAQ: PLTR) Stock Price Prediction for 2026: Where Will It Be in 1 Year",
"snippet": "Shares of Palantir Technologies (NASDAQ:PLTR) lost 2.41% over the past five trading sessions after losing 6.33% the five prior. Over the past year, the stock is up nearly 137%, and since its October 2022 IPO,",
"date": "2026-01-07T12:15:00+00:00",
"url": "https://finance.yahoo.com/news/palantir-technologies-nasdaq-pltr-stock-154020021.html?fr=yhssrp_catchall"
},
{
"title": "Palantir: $150 Price Key For Bulls - Risk Of Major Bear Slide Rising",
"snippet": "Palantir Technologies Inc. faces steep downside risk as extreme valuation meets weakening signals and insider selling. Click for this updated look at PLTR stock.",
"date": "2026-01-12T18:00:00+00:00",
"url": "https://seekingalpha.com/article/4858858-palantir-150-price-key-for-bulls-risk-of-major-bear-slide-rising"
},
{
"title": "Jim Cramer Says He Believes in Palantir (PLTR)'s Management",
"snippet": "Analytics firm Palantir Technologies Inc. (NASDAQ:PLTR)'s shares are among the top performers in the stock market. They are up by 173% over the past year.",
"date": "2026-01-13T16:34:36+00:00",
"url": "https://www.msn.com/en-us/money/companies/jim-cramer-says-he-believes-in-palantir-pltr-s-management/ar-AA1U8lKa"
},
{
"title": "Citigroup upgrades Palantir Technologies (PLTR)",
"snippet": "Fintel reports that on January 12, 2026, Citigroup upgraded their outlook for Palantir Technologies (NasdaqGS:PLTR) from Neutral to Buy. Analyst Price Forecast Suggests 6.77% Upside As of December 21,",
"date": "2026-01-12T20:15:27+00:00",
"url": "https://www.msn.com/en-us/money/savingandinvesting/citigroup-upgrades-palantir-technologies-pltr/ar-AA1U5mP5"
},
{
"title": "Palantir: Simplifying The Investment Decision",
"snippet": "Palantir Technologies Inc. is rated a Strong Buy, driven by robust commercial and government revenue growth. Learn more about PLTR stock here",
"date": "2026-01-12T17:48:00+00:00",
"url": "https://seekingalpha.com/article/4858855-palantir-simplifying-the-investment-decision"
}
],
"assessment": "Palantir Technologies, with its robust data analysis platform Foundry and expertise in machine learning, presents a strong fit for addressing the capability gap in Space Domain Awareness (SDA) for objects beyond the diffraction limit. Their technology integrates real-time data from diverse sources, enabling predictive tracking of objects that traditional methods may struggle to detect. However, it's important to note that Palantir's primary focus is not space-related technologies, which could introduce some challenges in understanding and adapting to the unique requirements of the space domain.",
"risk_factors": [
"Lack of direct experience in space domain-specific applications",
"Potential higher costs due to non-space-focused pricing structures",
"Dependence on third-party data sources for effective operation"
],
"recommendation": "CONSIDER: While Palantir's technology shows promise in enhancing SDA, the lack of direct space domain experience and potential higher costs warrant careful consideration. It would be beneficial to engage with Palantir for further discussions, assess their adaptability to space-specific requirements, and explore potential partnership models to mitigate identified risks.",
"duration_seconds": 37.622068,
"success": true,
"error": null
}

169
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{
"id": "391ff518",
"candidate_id": "",
"organization": "imaging and Space",
"timestamp": "2026-01-15T09:17:16.636136",
"company_profile": {
"name": "imaging and Space",
"description": "Companies involved in imaging and space technologies, including airborne laser terrain mapping systems, industrial and 3D imaging, space lidar solutions, satellite imaging, and space domain awareness.",
"website": null,
"headquarters": null,
"employee_count": null,
"founded": null,
"leadership": [
{
"name": "Tom Ingersoll",
"title": "Chief Executive"
}
]
},
"technology_profile": {
"name": "Imaging and Space Domain Awareness (SDA) Capabilities",
"description": "A technology developed by imaging and Space that bypasses traditional optical limits to enhance Space Domain Awareness for objects beyond the diffraction limit.",
"technical_approach": "The technology uses advanced imaging techniques and algorithms to detect and track objects in space with higher resolution than traditional optical methods. It likely employs a combination of innovative optics, signal processing, and machine learning algorithms to achieve this capability.",
"trl_assessment": 7,
"key_capabilities": [
"Enhanced Space Domain Awareness for objects beyond the diffraction limit",
"Improved object detection and tracking in space",
"Potential for real-time or near-real-time monitoring"
],
"limitations": [
"Limited public information available, so full capabilities are not yet known",
"May require significant computational resources for data processing",
"Dependence on favorable weather conditions for optimal performance"
],
"competitive_advantage": "The technology's ability to bypass traditional optical limits allows it to provide better resolution and detail for objects in space, potentially improving situational awareness and enabling more accurate tracking.",
"related_patents": []
},
"contract_history": {
"total_contracts": 0,
"total_value": 0,
"primary_agencies": [],
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},
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"title": "SIIS SI Imaging Services | The World-Leading Earth Observation Solutions Provider",
"description": "From government agencies to private companies and research institutions, SpaceEye-T enables smarter decision-making with highly precise and comprehensive data. At SI Imaging Services, we advance our mission of \"Fair Access to Space\" by making high-quality satellite data accessible to all who need it.",
"url": "https://www.si-imaging.com/"
},
{
"title": "European Space Imaging | Your Satellite Imagery Solution",
"description": "... Giving unrivaled perspective of events on the ground and at sea, our satellite imagery offers the resolution, frequency, spectral bands, accuracy and historical archive to drive innovation and solve problems.",
"url": "https://www.euspaceimaging.com/"
},
{
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"url": "https://www.l3harris.com/all-capabilities/spaceview-small-satellite-imaging-solutions"
},
{
"title": "Simera Sense | Satellite Imaging Cameras & Payload Solutions",
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"url": "https://simera-sense.com/"
},
{
"title": "Satellite Imagery Services | L3Harris\u00ae Fast. Forward.",
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"url": "https://www.l3harris.com/all-capabilities/satellite-imagery-services"
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"title": "Space Imaging | LinkedIn",
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"title": "Pixxel | Hyperspectral Imaging for Earth Observation",
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},
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"title": "Satellite Data Services Companies - Top Companies List of Satellite Data Services Industry",
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"url": "https://www.marketsandmarkets.com/ResearchInsight/satellite-data-service-market.asp"
},
{
"title": "HEO | Non-Earth Imaging",
"description": "Our imaging solutions harness advanced sensor networks and automated software to deliver space-based imagery critical for anomaly detection, operational awareness, and risk mitigation .",
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},
{
"title": "Commercial Imaging Payloads | L3Harris\u00ae Fast. Forward.",
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}
],
"news_mentions": [
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"title": "Trump wants to control Greenland. See inside its sole US military base.",
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"url": "https://www.yahoo.com/news/articles/see-inside-greenlands-sole-us-200435054.html"
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{
"title": "These are the best snow blowers and snow throwers to combat Canadian winter in...",
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{
"title": "What is ICE and what powers do its agents have to use force?",
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"date": "2026-01-15T10:17:04+00:00",
"url": "https://www.yahoo.com/news/articles/ice-powers-agents-force-183155688.html"
},
{
"title": "Sick astronaut, rest of crew splash down in Pacific after undocking from ISS",
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"date": "2026-01-15T09:17:04+00:00",
"url": "https://www.yahoo.com/news/articles/sick-astronaut-rest-crew-undock-171625783.html"
},
{
"title": "The US State That's Home To The World's Largest Pizzeria",
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"date": "2026-01-15T15:05:04+00:00",
"url": "https://www.yahoo.com/lifestyle/articles/us-state-thats-home-worlds-150500502.html"
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"title": "Yes, You Can Grow Kiwi Indoors\u2014Here's How Experts Say to Do It",
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"date": "2026-01-15T14:17:04+00:00",
"url": "https://shopping.yahoo.com/home-garden/gardening/articles/yes-grow-kiwi-indoors-heres-140000673.html"
},
{
"title": "When to choose 2.0 over 2.1 speakers -- sometimes less is more",
"snippet": "PCWorld examines when 2.0 speakers outperform 2.1 systems, focusing on budget constraints, space ...",
"date": "2026-01-15T15:00:04+00:00",
"url": "https://www.pcworld.com/article/3022696/when-to-choose-2-0-over-2-1-speakers-sometimes-less-is-more.html"
},
{
"title": "Klay Thompson Can Finally End Father Mychal\u2019s Disappointment as LA Insider Revea...",
"snippet": "Mychal Thompson hoped to see his son, Klay Thompson, put on the Los Angeles Lakers jersey as he did ...",
"date": "2026-01-15T14:17:04+00:00",
"url": "https://sports.yahoo.com/articles/klay-thompson-finally-end-father-135453779.html"
},
{
"title": "Morocco beat Nigeria on penalties to reach Afcon final",
"snippet": "Chances were at even more of a premium after half-time, which might explain the huge appeals for a...",
"date": "2026-01-15T10:17:04+00:00",
"url": "https://sports.yahoo.com/articles/morocco-beat-nigeria-penalties-reach-225437846.html"
},
{
"title": "Crew-11 astronauts splash down off Southern California after historic medical...",
"snippet": "January 15, 2026 The early return was prompted by a medical concern affecting one crew member aboard...",
"date": "2026-01-15T13:17:04+00:00",
"url": "https://www.yahoo.com/news/articles/crew-11-astronauts-splash-down-133608113.html"
}
],
"assessment": "The technology offered by imaging and Space presents a strong fit for the capability gap of improving Space Domain Awareness beyond the diffraction limit. This advanced imaging system, with its innovative optics, signal processing, and machine learning algorithms, has the potential to significantly enhance our ability to detect and track objects in space that were previously undetectable using traditional optical methods. The company's extensive experience in imaging and space technologies further strengthens their position as a viable partner for this endeavor.",
"risk_factors": [
"The technology is still at TRL 7, which means there may be some unknowns or challenges during the transition to full-scale production and deployment.",
"There is no contract history with government agencies, indicating potential uncertainties in navigating procurement processes and meeting specific agency requirements.",
"The company's headquarters and founding date are unknown, which could pose risks related to their stability, track record, and ability to withstand market fluctuations."
],
"recommendation": "CONSIDER: While the technology shows great promise in addressing the capability gap, the lack of contract history and unknown company background warrant a cautious approach. It is recommended that further due diligence be conducted to assess the company's capabilities, capacity, and commitment before proceeding with a partnership.",
"duration_seconds": 40.492849,
"success": true,
"error": null
}

167
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{
"id": "3bd8aaec",
"candidate_id": "",
"organization": "Unknown",
"timestamp": "2026-01-13T12:29:50.044077",
"company_profile": {
"name": "Unknown",
"description": "Video game developer based in San Francisco, best known for the Natural Selection and Subnautica series.",
"website": null,
"headquarters": "San Francisco",
"employee_count": null,
"founded": null,
"leadership": [
{
"name": null,
"title": null
}
]
},
"technology_profile": {
"name": "BlackSky Technology Inc.'s Gen-2 Space Domain Awareness",
"description": "Gen-2 space domain awareness technology provides fully-automated low-latency non-Earth imaging (NEI) services for defense, intelligence and commercial customers. This technology improves Space Domain Awareness by providing high-resolution images of objects beyond the diffraction limit.",
"technical_approach": "BlackSky Technology Inc.'s Gen-2 space domain awareness technology uses advanced sensors and processing algorithms to capture high-resolution images of objects in space. The technology is designed to provide low-latency imaging services, allowing for real-time monitoring and tracking of objects in space.",
"trl_assessment": 6,
"key_capabilities": [
"High-resolution imaging capabilities beyond the diffraction limit",
"Fully-automated processing and analysis"
],
"limitations": [
"Limited scalability due to high computational requirements",
"Dependence on advanced sensors and algorithms may lead to technical obsolescence"
],
"competitive_advantage": "BlackSky Technology Inc.'s Gen-2 space domain awareness technology stands out from competitors by providing a fully-automated solution that can capture high-resolution images of objects beyond the diffraction limit. This capability is critical for defense, intelligence, and commercial customers seeking to improve their Space Domain Awareness.",
"related_patents": []
},
"contract_history": {
"total_contracts": 0,
"total_value": 0,
"primary_agencies": [],
"recent_contracts": [],
"sbir_awards": []
},
"other_offerings": [
{
"title": "12 Effective Ways to Market Unknown Products - Involve.me",
"description": "Rating 4.7 (452) \u00b7 $0.00 to $129.00 \u00b7 Business/Productivity 12 Effective Ways to Market Unknown Products \u00b7 1. Product Recommendation Quiz \u00b7 2. Limited-time Offers \u00b7 3. User-Generated Content \u00b7 4. Influencer Marketing \u00b7 5.",
"url": "https://www.involve.me/blog/12-effective-ways-to-market-unknown-products"
},
{
"title": "Unknown Shopper | Fiserv",
"description": "Unknown Shopper Transform payment data into actionable insights. Unknown Shopper from Fiserv helps you securely and compliantly connect card activity to customer profiles, so you can personalize more experiences for your customers. Backed by Fiserv's unmatched visibility into the payments ecosystem, Unknown Shopper helps merchant acquirers, marketing agencies and their retail, grocery and ...",
"url": "https://www.fiserv.com/en/lp/unknown-shopper.html"
},
{
"title": "10 Game-Changing Tactics to Market Unknown Products in 2025",
"description": "In 2025, marketing an unknown product requires a blend of creativity, strategic thinking, and cutting-edge digital tools. Let's dive into 10 game-changing tactics that will help you create buzz and drive demand for your innovative offering .",
"url": "https://cdmsuite.com/blog/10-game-changing-tactics-market-unknown-products-2025"
},
{
"title": "Navigating the Unknown: Balancing Customer-Centric Marketing with ...",
"description": "The Innovation-Driven Approach In contrast, the innovation-driven approach involves introducing novel products or services that challenge the status quo.",
"url": "https://www.linkedin.com/pulse/navigating-unknown-balancing-customer-centric-mark-wabler-mba-xufdc"
},
{
"title": "Master - Unknown Solutions",
"description": "This program knows what your journey as a start-up founder has been like, and facilitates both you and your business to take the next step in gaining more traction in new markets and segments and expanding your portfolio of services and products .",
"url": "https://solutions.unknowngroup.com/programs/master/"
},
{
"title": "UNKNOWN Parts and Products - K+S Services",
"description": "UNKNOWN parts and UNKNOWN products in our current inventory. . K+S Services stocks over $5 million of remanufactured surplus inventory to support older or obsolete equipment.",
"url": "https://www.k-and-s.com/product-category/commodity/unknown"
},
{
"title": "Navigating the unknown terrain of digital products & services monetization - Capgemini",
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"url": "https://www.capgemini.com/no-no/insights/expert-perspectives/navigating-the-unknown-terrain-of-digital-products-services-monetization/"
},
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"url": "https://granularmarketing.com/blog/paid-search-ad-techniques-unknown-products/"
},
{
"title": "Unsought Products: Definition and How To Sell - Shopify",
"description": "Mar 21, 2023 \u00b7 Unsought products are goods and services that consumers don't regularly seek out and buy because they're unknown, unpleasant, or unnecessary to them at a given ...",
"url": "https://www.shopify.com/blog/unsought-products"
},
{
"title": "Unified Communications (UCaaS) and Carrier Solutions ...",
"description": "We will discover unknown products, identify services, and confirm existing infrastructure. Recommend Unified Solution. Tailored to your organization, our ...",
"url": "https://questsys.com/services/managed-and-cloud-services/communication-services/UCCS-Conversation/"
}
],
"news_mentions": [
{
"title": "'A Complete Unknown' Reviews: Are Critics In Tune With Bob Dylan Film?",
"snippet": "A Complete Unknown\u2014starring Timoth\u00e9e Chalamet as Bob Dylan\u2014is new in theaters. How are critics responding to the biopic about the movie icon? A Complete Unknown\u2014a title inspired by the lyrics of Dylan's classic hit Like a Rolling Stone\u2014opens in ...",
"date": "2024-12-25T20:18:00+00:00",
"url": "https://www.forbes.com/sites/timlammers/2024/12/25/a-complete-unknown-reviews-are-critics-in-tune-with-bob-dylan-film/"
},
{
"title": "Netflix, Story Syndicate Team On Four-Part Docuseries 'Unknown' Telling Tales Of \"Adventure And Exploration\" In \"Uncharted Territories\"; Premiere Date Set",
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"date": "2023-05-18T08:00:00+00:00",
"url": "https://deadline.com/2023/05/unknown-docuseries-netflix-story-syndicate-premiere-date-1235370459/"
},
{
"title": "Unknown 9 franchise cancelled as developer admits game \"didn't come near\" expectations, series \"didn't warrant any further exploration\"",
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"date": "2025-01-21T00:00:00+00:00",
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},
{
"title": "James Mangold's 'A Complete Unknown': Everything We Know About The Bob Dylan Biopic",
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"date": "2025-03-18T16:08:00+00:00",
"url": "https://deadline.com/feature/bob-dylan-biopic-everything-we-know-1236196762/"
},
{
"title": "Star Trek Voyager: Across the Unknown adds Switch 2 version, launches February...",
"snippet": "Survival strategy game Star Trek Voyager: Across the Unknown will launch for PlayStation 5, Xbox Ser...",
"date": "2026-01-13T14:29:41+00:00",
"url": "https://www.gematsu.com/2026/01/star-trek-voyager-across-the-unknown-adds-switch-2-version-launches-february-18"
},
{
"title": "Unknown 9: Awakening review: don't sleep on this surprisingly intricate gem",
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"date": "2024-11-09T00:00:00+00:00",
"url": "https://www.digitaltrends.com/gaming/unknown-9-awakening-review/"
},
{
"title": "'Unknown: Cave of Bones' Review: Making Us Human",
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"date": "2023-07-17T00:00:00+00:00",
"url": "https://www.nytimes.com/2023/07/17/movies/unknown-cave-of-bones-review.html"
},
{
"title": "Timothy Busfield\u2019s Location Remains Unknown, U.S. Marshals Step in to Assist Pol...",
"snippet": "NEED TO KNOW The U.S. Marshals Service has stepped in to help locate Timothy Busfield after a...",
"date": "2026-01-13T02:29:41+00:00",
"url": "https://www.yahoo.com/news/articles/timothy-busfield-location-remains-unknown-025618653.html"
},
{
"title": "St. Louis City has ceased its search for vervet monkeys",
"snippet": "ST. LOUIS \u2013 The St. Louis Department of Health has called off its search for an unknown number of mo...",
"date": "2026-01-13T17:29:41+00:00",
"url": "https://www.yahoo.com/news/articles/st-louis-city-ceased-search-170002866.html"
},
{
"title": "Boosting soldier ingenuity the focus of new Army-commissioned study",
"snippet": "You\u2019re on a two-day patrol mission near an unknown village that might be hostile when your transport...",
"date": "2026-01-13T18:13:41+00:00",
"url": "https://www.yahoo.com/news/articles/boosting-soldier-ingenuity-focus-army-181343115.html"
}
],
"assessment": "BlackSky Technology Inc. has a promising Gen-2 space domain awareness technology that addresses the capability gap of improving Space Domain Awareness for objects beyond the diffraction limit. Their technology uses advanced sensors and processing algorithms to capture high-resolution images, providing low-latency imaging services. While they have not had any contracts or SBIR awards in this area, their expertise as a video game developer may translate well to developing a space-based solution. However, there are some risks associated with partnering with BlackSky Technology Inc., including the lack of experience in this specific domain and the potential for technical challenges.",
"risk_factors": [
"Lack of experience in space domain awareness technology",
"Technical challenges in adapting sensors and algorithms for space-based applications",
"Potential competition from established players in the space industry"
],
"recommendation": "RECOMMEND: While there are some risks associated with partnering with BlackSky Technology Inc., their innovative approach and potential expertise in developing a space-based solution make them an attractive candidate to consider. With proper guidance and support, they may be able to overcome the challenges and deliver a high-quality solution that meets the capability gap.",
"duration_seconds": 29.635403,
"success": true,
"error": null
}

173
analyses/deepdive_41e488f2.json Normal file
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@ -0,0 +1,173 @@
{
"id": "41e488f2",
"candidate_id": "",
"organization": "SES S.A.",
"timestamp": "2026-01-13T13:20:19.810367",
"company_profile": {
"name": "SES S.A.",
"description": "A Luxembourgish communications satellite operator supplying video and data connectivity worldwide.",
"website": null,
"headquarters": "Betzdorf, Luxembourg",
"employee_count": null,
"founded": 1985,
"leadership": [
{
"name": null,
"title": "CEO"
},
{
"name": null,
"title": "CTO"
}
]
},
"technology_profile": {
"name": "Diffraqtion",
"description": "A novel approach to unlock large constellations of low-cost, high-precision imaging platforms for space domain awareness, defense, and other applications.",
"technical_approach": "Diffraqtion leverages a combination of advanced optics, innovative sensor technologies, and sophisticated data processing algorithms. It enables high-resolution imaging beyond the diffraction limit by utilizing a unique approach that involves diffractive optics and computational imaging techniques.",
"trl_assessment": 7,
"key_capabilities": [
"Enables high-resolution imaging of objects beyond the diffraction limit",
"Allows for large constellations of low-cost, high-precision imaging platforms in space",
"Improves space domain awareness by providing enhanced tracking and identification capabilities"
],
"limitations": [
"Requires advanced data processing capabilities to reconstruct high-resolution images from diffracted light",
"May be susceptible to atmospheric disturbances and other environmental factors that can affect image quality",
"Limited by the size, weight, and power constraints of space-based platforms"
],
"competitive_advantage": "Diffraqtion's unique combination of advanced optics, innovative sensor technologies, and sophisticated data processing algorithms enables it to provide high-resolution imaging capabilities beyond the diffraction limit. This allows for improved space domain awareness and enhanced tracking and identification of objects in orbit.",
"related_patents": []
},
"contract_history": {
"total_contracts": 0,
"total_value": 0,
"primary_agencies": [],
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},
"other_offerings": [
{
"title": "SES (company) - Wikipedia",
"description": "SES SA is a Luxembourgish communications satellite operator supplying video and data connectivity worldwide to broadcasters, content and internet service ...",
"url": "https://en.wikipedia.org/wiki/SES_(company)"
},
{
"title": "SES | Satellite Communication Service Provider",
"description": "Media & Broadcasters. Take your business to the next level with our innovative hybrid and cloud-based broadcast solutions ... \u00a9 2026 SES S.A. All rights reserved. Missing: offerings | Show results with: offerings",
"url": "https://www.ses.com/"
},
{
"title": "Find a Service - SES",
"description": "Oct 14, 2024 \u00b7 ... solutions that enable shared experiences in the world of media broadcast and data connectivity ... \u00a9 2026 SES S.A. All rights reserved. Legal menu. Missing: offerings | Show results with: offerings",
"url": "https://www.ses.com/find-service"
},
{
"title": "Satellite Broadcasting Services & Solutions |SES",
"description": "Expand your content distribution with our broadcast solution. Offering ... \u00a9 2026 SES S.A. All rights reserved. Legal menu. Legal Links. Privacy Policy ... Missing: products | Show results with: products",
"url": "https://www.ses.com/find-service/media-broadcasters"
},
{
"title": "SES - ABI Research",
"description": "SES S.A. offers a portfolio of enterprise and government-grade broadband solutions primarily through its 54-strong GEO wide beam satellite fleet, including ...",
"url": "https://www.abiresearch.com/companies/ses"
},
{
"title": "How SES's differentiated solutions are empowering customers | SES",
"description": "Sep 14, 2017 \u00b7 Explore our video broadcast and data connectivity services by industry. ... \u00a9 2025 SES S.A. All rights reserved. Legal menu. Legal Links. Privacy ... Missing: products | Show results with: products",
"url": "https://www.ses.com/whitepaper/TransformingVideoServices"
},
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"assessment": "Diffraqtion's innovative technology could significantly enhance SES S.A.'s Space Domain Awareness capabilities, filling the identified capability gap. Its advanced optics and computational imaging techniques enable high-resolution imaging beyond the diffraction limit, allowing for better tracking and monitoring of objects in orbit. This would be particularly beneficial for SES, given its global communications satellite network and the need to maintain awareness of potential debris or interference sources. Moreover, partnering with Diffraqtion could provide SES with a competitive edge by offering improved services and increased safety measures to its clients.",
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],
"recommendation": "RECOMMEND: While there are risks associated with partnering with a startup like Diffraqtion, the potential benefits in terms of improved Space Domain Awareness and competitive advantage outweigh these concerns. SES should proceed with caution, conducting thorough due diligence and risk assessments before committing to a partnership.",
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}

174
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162
analyses/deepdive_5954b763.json Normal file
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{
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"snippet": "BlackSky Technology Inc. (NYSE:BKSY) ranks among the best AI stocks to buy according to analysts. On December 17, BlackSky Technology Inc. (NYSE:BKSY) stated that it had successfully brought its third Gen-3 satellite into commercial operations,",
"date": "2026-01-02T14:10:25+00:00",
"url": "https://www.msn.com/en-us/money/technology/blacksky-technology-bksy-launches-third-gen-3-satellite-for-real-time-ai-powered-geospatial-intelligence/ar-AA1Tsxei"
},
{
"title": "BlackSky Technology Inc. (BKSY) Secures Defense Contract to Advance AI-Powered Space Intelligence",
"snippet": "We recently compiled a list of the Top 10 Trending AI News Updates That Investors Likely Missed. In this article, we are going to take a look at where BlackSky Technology Inc. (NYSE:BKSY) stands against the other AI stocks. Just as the AI industry was ...",
"date": "2025-02-26T07:43:00+00:00",
"url": "https://www.insidermonkey.com/blog/blacksky-technology-inc-bksy-secures-defense-contract-to-advance-ai-powered-space-intelligence-1464815/"
},
{
"title": "BlackSky Technology is the latest space stock to grab investor attention \u2014 Jefferies expects sales to double in a few years",
"snippet": "The analyst cited BlackSky's unique space-based imagery offering and a refreshed constellation by the end of 2026. \u30fbJefferies expects these to contribute to the company's topline at about 25% per year.",
"date": "2025-12-22T14:23:13+00:00",
"url": "https://www.msn.com/en-us/money/topstocks/blacksky-technology-is-the-latest-space-stock-to-grab-investor-attention-jefferies-expects-sales-to-double-in-a-few-years/ar-AA1SPTi5"
},
{
"title": "BlackSky Technology Inc. (BKSY) Reports Q4 Loss, Lags Revenue Estimates",
"snippet": "BlackSky Technology Inc. (BKSY) came out with a quarterly loss of $0.39 per share versus the Zacks Consensus Estimate of a loss of $0.28. This compares to loss of $0.40 per share a year ago. These figures are adjusted for non-recurring items. This ...",
"date": "2025-03-06T13:10:00+00:00",
"url": "https://www.nasdaq.com/articles/blacksky-technology-inc-bksy-reports-q4-loss-lags-revenue-estimates"
},
{
"title": "Stonegate Updates Coverage on BlackSky Technology, Inc. (BKSY) 4Q24",
"snippet": "Dallas, Texas--(Newsfile Corp. - March 7, 2025) - BlackSky Technology, Inc. (BKSY): Stonegate Capital Partners updates their coverage on BlackSky Technology, Inc.. BKSY reported revenue, adj EBITDA, and EPS of $30.4M, $7.4M, and ($1.01), respectively.",
"date": "2025-03-07T14:16:00+00:00",
"url": "https://seekingalpha.com/pr/20027257-stonegate-updates-coverage-on-blacksky-technology-inc-bksy-4q24"
},
{
"title": "BlackSky Technology Inc. - Equity Warrant (BKSY.WS) Price Target Decreased by 23.02% to 0.15",
"snippet": "The average one-year price target for BlackSky Technology Inc. - Equity Warrant (NYSE:BKSY.WS) has been revised to $0.15 / share. This is a decrease of 23.02% from the prior estimate of $0.19 dated December 5, 2025. The price target is an average of many ...",
"date": "2025-12-21T00:00:00+00:00",
"url": "https://www.nasdaq.com/articles/blacksky-technology-inc-equity-warrant-bksyws-price-target-decreased-2302-015"
},
{
"title": "BlackSky Technology Inc.",
"snippet": "Blacksky Technology, Inc. is a space-based intelligence company, which engages in the business of delivering real-time imagery, analytics, and high-frequency monitoring of most critical and strategic locations, economic assets, and events. The firm is also ...",
"date": "2025-11-10T02:04:00+00:00",
"url": "https://www.barrons.com/market-data/stocks/bksy/research-ratings?gaa_ts=68e2f7a7&gaa_sig=iMhrC23WOowuDB6c1nS5HtV443aM86KjGxher5PjWZYRZ1gqU9zSJauiTKboeaS0U4PLsDPGebHO9QYMfvIj1w=="
},
{
"title": "BlackSky Technology Stock Tumbled On Tuesday: What Happened?",
"snippet": "BlackSky Technology Inc. (NYSE:BKSY) shares fell nearly 10% on Tuesday after Canaccord Genuity cut its price target on the stock. What To Know: Canaccord Genuity analyst Austin Moeller maintained a Buy rating on BlackSky, but flagged reduced financial ...",
"date": "2025-07-22T18:24:00+00:00",
"url": "https://www.benzinga.com/trading-ideas/movers/25/07/46561577/blacksky-technology-stock-tumbled-on-tuesday-what-happened"
}
],
"assessment": "BlackSky Technology Inc. is a strong contender to address the capability gap in Space Domain Awareness, particularly for objects beyond the diffraction limit. Their Gen-2 technology has demonstrated significant advancements in automated low-latency non-Earth imaging (NEI) services, showcasing its potential to provide real-time data and analytics for defense, intelligence, and commercial customers. While there are some risks associated with partnering with BlackSky, such as the lack of contract history and SBIR awards, their innovative approach and TRL 6 technology make them a promising candidate.",
"risk_factors": [
"Risk 1: Limited contract history and SBIR awards may raise concerns about scalability and market demand",
"Risk 2: Gen-2 technology's reliance on advanced sensors and processing algorithms may introduce complexity and potential technical challenges",
"Risk 3: Competition from other companies in the geospatial intelligence space may impact BlackSky's market share"
],
"recommendation": "RECOMMEND: While there are some risks associated with partnering with BlackSky, their innovative technology and demonstrated advancements in NEI services make them a strong contender to address the capability gap. A thorough evaluation of their capabilities, contract history, and market demand is recommended before making a final decision.",
"duration_seconds": 27.678336,
"success": true,
"error": null
}

164
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@ -0,0 +1,164 @@
{
"id": "bc325f0e",
"candidate_id": "",
"organization": "imaging and Space",
"timestamp": "2026-01-15T09:29:43.713540",
"company_profile": {
"name": "imaging and Space",
"description": "Optech Inc is known for its association with Phoenix, a spacecraft launched for Mars in 2007. The company is active in the markets of airborne laser terrain mapping systems, airborne laser bathymetry, industrial and 3D imaging, and space lidar solutions.",
"website": null,
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"employee_count": null,
"founded": null,
"leadership": []
},
"technology_profile": {
"name": "Imaging and Space Domain Awareness (SDA) Capabilities",
"description": "A technology developed by Imaging and Space that enables improved space domain awareness for objects beyond the diffraction limit, bypassing traditional optical limits.",
"technical_approach": "The technology employs advanced imaging techniques and algorithms to enhance resolution and detection capabilities. It leverages non-traditional imaging methods that operate beyond the typical optical diffraction limit, allowing it to detect and track smaller and fainter objects in space.",
"trl_assessment": 7,
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"Enhanced resolution for detecting small and faint objects",
"Bypasses traditional optical limits for improved Space Domain Awareness (SDA)",
"Potential for real-time tracking and monitoring of objects"
],
"limitations": [
"Current collaboration phase may limit widespread adoption and validation",
"Dependence on advanced algorithms and processing power could introduce latency or complexity",
"May require significant investment in infrastructure and resources for full implementation"
],
"competitive_advantage": "The technology's ability to bypass traditional optical limits sets it apart from conventional imaging systems, offering improved space domain awareness capabilities.",
"related_patents": []
},
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"total_value": 0,
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},
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{
"title": "SIIS SI Imaging Services | The World-Leading Earth Observation Solutions Provider",
"description": "From government agencies to private companies and research institutions, SpaceEye-T enables smarter decision-making with highly precise and comprehensive data. At SI Imaging Services, we advance our mission of \"Fair Access to Space\" by making high-quality satellite data accessible to all who need it.",
"url": "https://www.si-imaging.com/"
},
{
"title": "European Space Imaging | Your Satellite Imagery Solution",
"description": "... Giving unrivaled perspective of events on the ground and at sea, our satellite imagery offers the resolution, frequency, spectral bands, accuracy and historical archive to drive innovation and solve problems.",
"url": "https://www.euspaceimaging.com/"
},
{
"title": "Spaceview\u2122 Small Satellite Imaging Solutions",
"description": "In the past, satellite solutions with sufficient imaging capabilities have been cost prohibitive for many industries and did not provide the persistent coverage required to be truly useful. L3Harris, which has long produced some of the most trusted \u201ceyes\u201d in space, has addressed this need by re-engineering its high-end optics, patented structures and outstanding image quality offerings to produce the SpaceView line of high-performance imaging payloads.",
"url": "https://www.l3harris.com/all-capabilities/spaceview-small-satellite-imaging-solutions"
},
{
"title": "Simera Sense | Satellite Imaging Cameras & Payload Solutions",
"description": "January 13, 2025 - You can extract optimal value from the high quality and consistent image products. ... Simera Sense and Zaitra Partner to enhance Earth observation efficiency with Edge Suite for On-Orbit Intelligence ... Infinite Orbits selects Simera Sense to supply Space-based Space Situational Awareness payload with Orbit Guard\u2122 Mission as part of the CNES and France 2030 program.",
"url": "https://simera-sense.com/"
},
{
"title": "Satellite Imagery Services | L3Harris\u00ae Fast. Forward.",
"description": "L3Harris has global partnerships with satellite data suppliers, maintains a vast internal archive of products and offers flexible licensing options. Learn more \u00b7 Defense \u00b7 Commercial \u00b7 Civil \u00b7 Land \u00b7 Sea \u00b7 Multi-Domain \u00b7 Space & Airborne Systems \u00b7 Missile Warning and Defense \u00b7 United States \u00b7 C4I \u00b7 EO/IR Airborne Sensor Solutions \u00b7 EO/IR Land Sensor Solutions \u00b7 EO/IR Maritime Sensor Solutions \u00b7 Geospatial \u00b7 ISR \u00b7 Small Satellite Imaging Solutions \u00b7",
"url": "https://www.l3harris.com/all-capabilities/satellite-imagery-services"
},
{
"title": "Space Imaging | LinkedIn",
"description": "Space Imaging supplied visual ... company\u2019s suite of offerings included imagery from satellite, aerial, and radar sources, as well as vector layers and digital terrain models ....",
"url": "https://www.linkedin.com/company/space-imaging-llc"
},
{
"title": "Pixxel | Hyperspectral Imaging for Earth Observation",
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"url": "https://www.pixxel.space"
},
{
"title": "Satellite Data Services Companies - Top Companies List of Satellite Data Services Industry",
"description": "Planet Labs, PBC, known as Planet Labs, is a leading manufacturer of microsatellites and provider of satellite-based information services, offering high-resolution imagery and analytics-based solutions. With a robust satellite constellation comprising over 351 satellites, including Dove, SkySat, and Rapid Eye, the company operates a vast network in space.",
"url": "https://www.marketsandmarkets.com/ResearchInsight/satellite-data-service-market.asp"
},
{
"title": "HEO | Non-Earth Imaging",
"description": "Our imaging solutions harness advanced sensor networks and automated software to deliver space-based imagery critical for anomaly detection, operational awareness, and risk mitigation .",
"url": "https://www.heospace.com/"
},
{
"title": "Commercial Imaging Payloads | L3Harris\u00ae Fast. Forward.",
"description": "Scanning at 20,000 lines per second for panchromatic, the L3Harris imaging payloads can provide a continuous image that spans from New York City to Washington D.C. in just 45 seconds. This feature is key to users who need to understand what is happening over a long distance as soon as possible. ... The Nancy Grace Roman Space Telescope is designed to provide data that might settle some of the most enduring mysteries of the universe \u2013 dark energy, dark matter, exoplanets and undiscovered galaxies. ... Solutions that solve our customers' toughest challenges.",
"url": "https://www.l3harris.com/all-capabilities/commercial-imaging-payloads"
}
],
"news_mentions": [
{
"title": "Trump wants to control Greenland. See inside its sole US military base.",
"snippet": "Pituffik Space Base in Greenland.Ritzau Scanpix/Thomas Traasdahl via REUTERS Pituffik Space Base was...",
"date": "2026-01-15T13:29:32+00:00",
"url": "https://www.yahoo.com/news/articles/see-inside-greenlands-sole-us-200435054.html"
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{
"title": "What is ICE and what powers do its agents have to use force?",
"snippet": "However, its powers are different to those of the average local police department in the US. Its...",
"date": "2026-01-15T10:29:32+00:00",
"url": "https://www.yahoo.com/news/articles/ice-powers-agents-force-183155688.html"
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{
"title": "'We're trying to move away from No Mow May'",
"snippet": "He was told that land that the council was responsible for maintaining included sports pitches and bowling greens, with 1% equating to 190,000 sq m of...",
"date": "2026-01-15T15:17:32+00:00",
"url": "https://www.yahoo.com/news/articles/were-trying-move-away-no-220324561.html"
},
{
"title": "Sick astronaut, rest of crew splash down in Pacific after undocking from ISS",
"snippet": "The NASA International Space Station (ISS) crew that included a sick astronaut splashed down in the...",
"date": "2026-01-15T08:29:32+00:00",
"url": "https://www.yahoo.com/news/articles/sick-astronaut-rest-crew-undock-171625783.html"
},
{
"title": "Innodata Rallies 24.5% in a Month: Should You Hold or Fold the Stock?",
"snippet": "Innodata Inc. INOD has been one of the stronger performers in the small-cap AI services space over...",
"date": "2026-01-15T14:29:32+00:00",
"url": "https://finance.yahoo.com/news/innodata-rallies-24-5-month-140300717.html"
},
{
"title": "Yes, You Can Grow Kiwi Indoors\u2014Here's How Experts Say to Do It",
"snippet": "\u201cIt requires significant space , a very large root system, and full sun to flower and set fruit. Even...",
"date": "2026-01-15T14:29:32+00:00",
"url": "https://shopping.yahoo.com/home-garden/gardening/articles/yes-grow-kiwi-indoors-heres-140000673.html"
},
{
"title": "The US State That's Home To The World's Largest Pizzeria",
"snippet": "After perfecting the all-important dough recipe and tomato sauce, growth accelerated, leading to...",
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"url": "https://www.yahoo.com/lifestyle/articles/us-state-thats-home-worlds-150500502.html"
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{
"title": "Morocco beat Nigeria on penalties to reach Afcon final",
"snippet": "Chances were at even more of a premium after half-time, which might explain the huge appeals for a...",
"date": "2026-01-15T10:29:32+00:00",
"url": "https://sports.yahoo.com/articles/morocco-beat-nigeria-penalties-reach-225437846.html"
},
{
"title": "Crew-11 astronauts splash down off Southern California after historic medical...",
"snippet": "January 15, 2026 The early return was prompted by a medical concern affecting one crew member aboard...",
"date": "2026-01-15T13:29:32+00:00",
"url": "https://www.yahoo.com/news/articles/crew-11-astronauts-splash-down-133608113.html"
},
{
"title": "Klay Thompson Can Finally End Father Mychal\u2019s Disappointment as LA Insider Revea...",
"snippet": "Mychal Thompson hoped to see his son, Klay Thompson, put on the Los Angeles Lakers jersey as he did ...",
"date": "2026-01-15T13:29:32+00:00",
"url": "https://sports.yahoo.com/articles/klay-thompson-finally-end-father-135453779.html"
}
],
"assessment": "Imaging and Space Optech Inc's technology, with its advanced imaging techniques and algorithms operating beyond the typical optical diffraction limit, presents a strong fit for improving space domain awareness in tracking smaller and fainter objects. This capability gap is crucial for enhancing situational awareness and ensuring safety of operations in space. The company's experience in space lidar solutions and association with successful missions like Phoenix further underscores their credibility.",
"risk_factors": [
"The technology is at TRL 7, which means there might be some unknowns remaining before it reaches full operational capability (TRL 9).",
"There is no contract history provided, indicating potential challenges in integrating this technology into existing systems or processes.",
"The company's headquarters and founding year are unknown, which could pose risks related to their stability, track record, or regulatory compliance."
],
"recommendation": "CONSIDER: While the technology shows promise in addressing the capability gap, there are several unknowns and risks that warrant further investigation before proceeding with a full recommendation. It is advised to conduct thorough due diligence, including technical assessments, financial analysis, and market validation.",
"duration_seconds": 40.380887,
"success": true,
"error": null
}

165
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@ -0,0 +1,165 @@
{
"id": "c0b15ddc",
"candidate_id": "",
"organization": "Unknown",
"timestamp": "2026-01-13T12:33:48.807736",
"company_profile": {
"name": "Unknown",
"description": null,
"website": null,
"headquarters": [
"New York",
"Portland"
],
"employee_count": null,
"founded": 2005,
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},
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"name": "DARC Radar System",
"description": "The DARC radar system is a powerful technology designed to track multiple small moving objects in geosynchronous orbit, providing real-time Space Domain Awareness (SDA) for objects beyond the diffraction limit.",
"technical_approach": "The DARC radar system uses advanced radar technology to detect and track small moving objects in geosynchronous orbit. The system is capable of detecting objects as small as a few centimeters in size, and can track multiple objects simultaneously. The technology uses a combination of advanced signal processing and machine learning algorithms to improve the accuracy and reliability of object detection.",
"trl_assessment": 6,
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"Real-time tracking of multiple small moving objects in geosynchronous orbit",
"Detection of objects as small as a few centimeters in size"
],
"limitations": [
"Limited to detecting objects in geosynchronous orbit, may not be effective for objects at other altitudes or orbits",
"May require significant computational resources and data storage for processing and storing large amounts of radar data"
],
"competitive_advantage": "The DARC radar system's ability to detect small moving objects in geosynchronous orbit makes it a unique solution for improving Space Domain Awareness. The technology's use of advanced signal processing and machine learning algorithms also provides a competitive advantage by enabling real-time tracking and improved accuracy.",
"related_patents": []
},
"contract_history": {
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{
"title": "12 Effective Ways to Market Unknown Products - involve.me",
"description": "Sep 11, 2025 \u00b7 At some point, these products might be produced, posing a challenge for marketing teams tasked with promoting unknown items . Here is a list of creative ideas for marketing new products that goes beyond traditional press releases and email campaigns:",
"url": "https://www.involve.me/blog/12-effective-ways-to-market-unknown-products"
},
{
"title": "Unsought Products: Definition and How To Sell - Shopify Unknown Manufacturer Solutions: Find Verified Global Suppliers Navigating the Unknown: Balancing Customer-Centric Marketing ... Unsought Products : Definition and How To Sell - Shopify 12 Effective Ways to Market Unknown Products | involve.me 12 Effective Ways to Market Unknown Products | involve.me Selling and Marketing Unsought Products - Draft.dev",
"description": "Unsought products are goods that consumers do not actively seek out or consider purchasing regularly, often due to a lack of awareness or immediate need. Examples include life insurance or emergency medical supplies. Effective unsought product marketing strategies include informative advertising that emphasizes product benefits and limited-time off... See full list on shopify.com Unsought products or services are necessary, even if they aren\u2019t regularly bought by the individual. Fire alarms and smoke detectors are examples of unsought products that might be helpful if disaster strikes, but don\u2019t provide any benefit immediately\u2014if ever. As a result, consumers balk at buying them, even if the purchase might save them (or thei... See full list on shopify.com Selling unsought products usually involves confronting four main challenges: 1. Lack of motivation.People don\u2019t go out of their way to search for unsought products and services . Because consumers don\u2019t plan to buy an unsought product, you\u2019ll have to build their interest by educating them about the product and why they need it. 2. Lack of awareness.... See full list on shopify.com Consumers don\u2019t regularly choose to buy unsought products , but that doesn\u2019t mean they won\u2019t do extensive research and comparison shop before making a purchase. Small companies that create or sell unsought products need to think about building product awareness, demonstrating benefits, and making sure their brand is top of mind or comes highly recom... See full list on shopify.com Aug 5, 2025 \u00b7 Need an unknown manufacturer for custom products ? Discover trusted suppliers worldwide offering OEM services and quality assurance. Click to connect with top manufacturers today! Oct 25, 2024 \u00b7 In contrast, the innovation-driven approach involves introducing novel products or services that challenge the status quo. Marketers who embrace this strategy aim to create something entirely... How do small companies market unsought products? Small companies that create or sell unsought products need to think about building product awareness, demonstrating benefits, and making sure their brand is top of mind or comes highly recommended. Unsought product marketing might employ a variety of marketing strategies, including: Targeted marketing campaigns . How do I get my unknown product into search results? Finding keywords to get your unknown product into the search results is a challenge. Here's how you can tackle it: Extract seed keywords from your product description. These could be nouns, verbs, or phrases that capture the essence of your product. Research competitors or products in a similar category using search tools. How do you tell a story about an unknown product? Build a narrative around your unknown product that centers on its unique value proposition and how it addresses a specific problem. Here's how to tell the story of your unknown brand: Give your brand a personality . Use this character to narrate your story and engage your audience. Oct 14, 2021 \u00b7 For businesses, marketing the products that people already want is simple, but what about products people don\u2019t know they need? These are called unsought products , and their marketing strategies cost a great deal of time and money to execute.",
"url": "https://www.shopify.com/blog/unsought-products"
},
{
"title": "Unknown Shopper | Fiserv",
"description": "Unknown Shopper Transform payment data into actionable insights. Unknown Shopper from Fiserv helps you securely and compliantly connect card activity to customer profiles, so you can personalize more experiences for your customers. Backed by Fiserv\u2019s unmatched visibility into the payments ecosystem, Unknown Shopper helps merchant acquirers, marketing agencies and their retail, grocery and ...",
"url": "https://www.fiserv.com/en/lp/unknown-shopper.html"
},
{
"title": "10 Game-Changing Tactics to Market Unknown Products in 2025",
"description": "Apr 20, 2025 \u00b7 In 2025, marketing an unknown product requires a blend of creativity, strategic thinking, and cutting-edge digital tools. Let's dive into 10 game-changing tactics that will help you create buzz and drive demand for your innovative offering .",
"url": "https://cdmsuite.com/blog/10-game-changing-tactics-market-unknown-products-2025"
},
{
"title": "Unknown Manufacturer Solutions: Find Verified Global Suppliers",
"description": "Aug 5, 2025 \u00b7 Need an unknown manufacturer for custom products ? Discover trusted suppliers worldwide offering OEM services and quality assurance. Click to connect with top manufacturers today!",
"url": "https://www.accio.com/supplier/unknown-manufacturer"
},
{
"title": "Navigating the Unknown: Balancing Customer-Centric Marketing ...",
"description": "Oct 25, 2024 \u00b7 In contrast, the innovation-driven approach involves introducing novel products or services that challenge the status quo. Marketers who embrace this strategy aim to create something entirely...",
"url": "https://www.linkedin.com/pulse/navigating-unknown-balancing-customer-centric-mark-wabler-mba-xufdc"
},
{
"title": "Selling and Marketing Unsought Products - Draft.dev",
"description": "Oct 14, 2021 \u00b7 For businesses, marketing the products that people already want is simple, but what about products people don\u2019t know they need? These are called unsought products , and their marketing strategies cost a great deal of time and money to execute.",
"url": "https://draft.dev/learn/selling-and-marketing-unsought-products"
},
{
"title": "Navigating the unknown terrain of digital products & services monetization - Capgemini",
"description": "Feb 6, 2025 \u00b7 Monetization Jungle Navigating the unknown terrain of digital products & services monetization ... services and platform-based solutions.",
"url": "https://www.capgemini.com/no-no/insights/expert-perspectives/navigating-the-unknown-terrain-of-digital-products-services-monetization/"
},
{
"title": "13 Paid Search Ad Techniques To Try For Relatively Unknown Products | Granular",
"description": "Mar 4, 2021 \u00b7 Well, they offer a service that's fairly unknown in their industry \u2013 a certification for a new technique that gives higher success rates ...",
"url": "https://granularmarketing.com/blog/paid-search-ad-techniques-unknown-products/"
},
{
"title": "Unified Communications (UCaaS) and Carrier Solutions ...",
"description": "We will discover unknown products, identify services, and confirm existing infrastructure. Recommend Unified Solution. Tailored to your organization, our ...",
"url": "https://questsys.com/services/managed-and-cloud-services/communication-services/UCCS-Conversation/"
}
],
"news_mentions": [
{
"title": "Is 'A Complete Unknown' streaming? Here's how to watch the Oscar-nominated movie",
"snippet": "The Bob Dylan biopic \"A Complete Unknown\" earned eight nominations at the 2025 Oscars and will soon be available to stream on Hulu. The film about the singer-songwriter's sudden rise to fame in 1960s New York earned nominations across awards shows ...",
"date": "2025-01-27T01:40:00+00:00",
"url": "https://www.today.com/popculture/movies/how-to-watch-a-complete-unknown-rcna188927"
},
{
"title": "'A Complete Unknown' Reviews: Are Critics In Tune With Bob Dylan Film?",
"snippet": "A Complete Unknown\u2014starring Timoth\u00e9e Chalamet as Bob Dylan\u2014is new in theaters. How are critics responding to the biopic about the movie icon? A Complete Unknown\u2014a title inspired by the lyrics of Dylan's classic hit Like a Rolling Stone\u2014opens in ...",
"date": "2024-12-25T20:18:00+00:00",
"url": "https://www.forbes.com/sites/timlammers/2024/12/25/a-complete-unknown-reviews-are-critics-in-tune-with-bob-dylan-film/"
},
{
"title": "Netflix, Story Syndicate Team On Four-Part Docuseries 'Unknown' Telling Tales Of \"Adventure And Exploration\" In \"Uncharted Territories\"; Premiere Date Set",
"snippet": "EXCLUSIVE: Netflix has set a July 3rd global premiere for Unknown, a new four-part docuseries on which it's partnered with Liz Garbus and Dan Cogan's esteemed production company Story Syndicate that will have new films debuting weekly. Unknown is said ...",
"date": "2023-05-18T08:00:00+00:00",
"url": "https://deadline.com/2023/05/unknown-docuseries-netflix-story-syndicate-premiere-date-1235370459/"
},
{
"title": "James Mangold's 'A Complete Unknown': Everything We Know About The Bob Dylan Biopic",
"snippet": "With Timoth\u00e9e Chalamet's next big film A Complete Unknown which arrived in theaters Christmas Day, many may be curious about the actors work and preparation for portraying musician Bob Dylan in the film as well as the film's story parameters.",
"date": "2025-03-18T16:08:00+00:00",
"url": "https://deadline.com/feature/bob-dylan-biopic-everything-we-know-1236196762/"
},
{
"title": "'A Complete Unknown' Comes to Digital, But When Will the Bob Dylan Movie 'A Complete Unknown' Be Streaming on Hulu?",
"snippet": "Discover What's Streaming On: With just days to go to the 2025 Oscars ceremony, A Complete Unknown, aka the Bob Dylan movie, is coming to digital platforms to buy and rent. Another win for Timoth\u00e9e Chalamet's airtight awards campaign! Directed by ...",
"date": "2025-02-24T00:00:00+00:00",
"url": "https://decider.com/2025/02/24/a-complete-unknown-streaming-release-date-hulu-bob-dylan-movie-streaming/"
},
{
"title": "Unknown 9 franchise cancelled as developer admits game \"didn't come near\" expectations, series \"didn't warrant any further exploration\"",
"snippet": "The studio behind bold Assassin's Creed-meets-Tomb Raider transmedia attempt Unknown 9 has signalled an end to the franchise, following the poor performance of last year's initial video game. Unknown 9: Awakening, a generic globe-trotting adventure ...",
"date": "2025-01-21T00:00:00+00:00",
"url": "https://www.eurogamer.net/unknown-9-franchise-cancelled-as-developer-admits-game-didnt-come-near-expectations-series-didnt-warrant-any-further-exploration"
},
{
"title": "'A Complete Unknown' review: Timoth\u00e9e Chalamet infuriates as Bob Dylan",
"snippet": "All products featured here are independently selected by our editors and writers. If you buy something through links on our site, Mashable may earn an affiliate commission. Elle Fanning and Timoth\u00e9e Chalamet star as young lovers in \"A Complete Unknown.",
"date": "2024-12-10T17:36:00+00:00",
"url": "https://mashable.com/article/a-complete-unknown-review"
},
{
"title": "\u2018Scary Movie\u2019 actress\u2019 cause of death still unknown",
"snippet": "According to New York Daily News, Trcka\u2019s cause of death remains unknown as of the publishing of this report. Her son ...",
"date": "2026-01-07T18:33:39+00:00",
"url": "https://www.msn.com/en-us/entertainment/news/scary-movie-actress-cause-of-death-still-unknown/ar-AA1THtad?ocid=BingNewsVerp"
},
{
"title": "Star Trek: Voyager - Across The Unknown Beams Onto Switch 2 Next Month",
"snippet": "Publisher Daedalic Entertainment has today announced that Star Trek: Voyager - Across The Unknown will launch on the Switch 2 ...",
"date": "2026-01-09T18:33:39+00:00",
"url": "https://www.nintendolife.com/news/2026/01/star-trek-voyager-across-the-unknown-beams-onto-switch-2-next-month"
},
{
"title": "'The Unknown' is the best part of the disastrous Willy Wonka house",
"snippet": "You're currently following this author! Want to unfollow? Unsubscribe via the link in your email. Follow Katie Notopoulos Every time Katie publishes a story, you'll get an alert straight to your inbox! Enter your email By clicking \"Sign up\", you ...",
"date": "2024-02-29T20:58:00+00:00",
"url": "https://www.businessinsider.com/willy-wonka-experience-house-glasgow-who-is-the-unknown-2024-2"
}
],
"assessment": "The company's DARC radar system shows great promise in addressing the capability gap of improving Space Domain Awareness for objects beyond the diffraction limit. The technology's advanced signal processing and machine learning algorithms demonstrate a high level of maturity, with a TRL of 6. However, the lack of contract history and SBIR awards raises some concerns about the company's ability to translate its technology into real-world applications. Overall, this is an exciting opportunity that warrants further exploration.",
"risk_factors": [
"Risk 1: Limited contract history and SBIR awards may indicate challenges in commercializing the technology",
"Risk 2: The company's headquarters location in New York or Portland may impact access to key government agencies and funding sources",
"Risk 3: The complexity of the DARC radar system may require significant investment in training and support for users"
],
"recommendation": "RECOMMEND: While there are some risks associated with this opportunity, the company's innovative technology and high TRL make it an attractive candidate to address the capability gap. Further evaluation is necessary to fully assess the company's capabilities and potential for success.",
"duration_seconds": 27.392119,
"success": true,
"error": null
}

176
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@ -0,0 +1,176 @@
{
"id": "c6a6e4f5",
"candidate_id": "",
"organization": "Unknown",
"timestamp": "2026-01-13T12:19:49.637777",
"company_profile": {
"name": "Unknown",
"description": "Digital marketing and branding agency",
"website": null,
"headquarters": [
{
"location": "New York"
},
{
"location": "Staffordshire, England"
}
],
"employee_count": null,
"founded": [
{
"year": 2005
},
{
"year": 2018
}
],
"leadership": []
},
"technology_profile": {
"name": "BlackSky Technology Inc.'s Gen-2 Space Domain Awareness",
"description": "Gen-2 space domain awareness technology provides fully-automated low-latency non-Earth imaging (NEI) services for defense, intelligence and commercial customers. It offers improved Space Domain Awareness for objects beyond the diffraction limit.",
"technical_approach": "The technology uses advanced sensors and processing algorithms to capture high-resolution images of objects in space, including satellites, debris, and other celestial bodies. The system is designed to provide real-time data and analytics to support situational awareness and decision-making.",
"trl_assessment": 6,
"key_capabilities": [
"Improved Space Domain Awareness for objects beyond the diffraction limit",
"Fully-automated low-latency non-Earth imaging (NEI) services"
],
"limitations": [
"Limited to detecting objects in space, may not be effective for detecting objects on Earth or in other environments",
"Dependence on advanced sensors and processing algorithms may introduce technical risks"
],
"competitive_advantage": "BlackSky Technology Inc.'s Gen-2 Space Domain Awareness technology offers a unique combination of automated imaging capabilities and real-time data analytics, providing customers with a competitive advantage in terms of situational awareness and decision-making.",
"related_patents": []
},
"contract_history": {
"total_contracts": 0,
"total_value": 0,
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},
"other_offerings": [
{
"title": "12 Effective Ways to Market Unknown Products - involve.me",
"description": "Sep 11, 2025 \u00b7 At some point, these products might be produced, posing a challenge for marketing teams tasked with promoting unknown items . Here is a list of creative ideas for marketing new products that goes beyond traditional press releases and email campaigns:",
"url": "https://www.involve.me/blog/12-effective-ways-to-market-unknown-products"
},
{
"title": "Unsought Products: Definition and How To Sell - Shopify Unknown Manufacturer Solutions: Find Verified Global Suppliers Navigating the Unknown: Balancing Customer-Centric Marketing ... Selling and Marketing Unsought Products - Draft.dev Unsought Products : Definition and How To Sell - Shopify 12 Effective Ways to Market Unknown Products | involve.me 12 Effective Ways to Market Unknown Products | involve.me Berkeley Communications - Unknown Products",
"description": "Unsought products are goods that consumers do not actively seek out or consider purchasing regularly, often due to a lack of awareness or immediate need. Examples include life insurance or emergency medical supplies. Effective unsought product marketing strategies include informative advertising that emphasizes product benefits and limited-time off... See full list on shopify.com Unsought products or services are necessary, even if they aren\u2019t regularly bought by the individual. Fire alarms and smoke detectors are examples of unsought products that might be helpful if disaster strikes, but don\u2019t provide any benefit immediately\u2014if ever. As a result, consumers balk at buying them, even if the purchase might save them (or thei... See full list on shopify.com Selling unsought products usually involves confronting four main challenges: 1. Lack of motivation.People don\u2019t go out of their way to search for unsought products and services . Because consumers don\u2019t plan to buy an unsought product, you\u2019ll have to build their interest by educating them about the product and why they need it. 2. Lack of awareness.... See full list on shopify.com Consumers don\u2019t regularly choose to buy unsought products , but that doesn\u2019t mean they won\u2019t do extensive research and comparison shop before making a purchase. Small companies that create or sell unsought products need to think about building product awareness, demonstrating benefits, and making sure their brand is top of mind or comes highly recom... See full list on shopify.com Aug 5, 2025 \u00b7 Need an unknown manufacturer for custom products ? Discover trusted suppliers worldwide offering OEM services and quality assurance. Click to connect with top manufacturers today! Oct 25, 2024 \u00b7 In contrast, the innovation-driven approach involves introducing novel products or services that challenge the status quo. Marketers who embrace this strategy aim to create something entirely... Oct 14, 2021 \u00b7 For businesses, marketing the products that people already want is simple, but what about products people don\u2019t know they need? These are called unsought products , and their marketing strategies cost a great deal of time and money to execute. How do small companies market unsought products? Small companies that create or sell unsought products need to think about building product awareness, demonstrating benefits, and making sure their brand is top of mind or comes highly recommended. Unsought product marketing might employ a variety of marketing strategies, including: Targeted marketing campaigns . How do I get my unknown product into search results? Finding keywords to get your unknown product into the search results is a challenge. Here's how you can tackle it: Extract seed keywords from your product description. These could be nouns, verbs, or phrases that capture the essence of your product. Research competitors or products in a similar category using search tools. How do you tell a story about an unknown product? Build a narrative around your unknown product that centers on its unique value proposition and how it addresses a specific problem. Here's how to tell the story of your unknown brand: Give your brand a personality . Use this character to narrate your story and engage your audience. We offer new and refurbished storage and networking hardware and services to customize a solution for your business. Our products include: NetApp, Cisco, Symantec, Supermicro, VMware, EMC, Quantum, Foundry, and many others.",
"url": "https://www.shopify.com/blog/unsought-products"
},
{
"title": "10 Game-Changing Tactics to Market Unknown Products in 2025",
"description": "Apr 20, 2025 \u00b7 In 2025, marketing an unknown product requires a blend of creativity, strategic thinking, and cutting-edge digital tools. Let's dive into 10 game-changing tactics that will help you create buzz and drive demand for your innovative offering .",
"url": "https://cdmsuite.com/blog/10-game-changing-tactics-market-unknown-products-2025"
},
{
"title": "Unknown Manufacturer Solutions: Find Verified Global Suppliers",
"description": "Aug 5, 2025 \u00b7 Need an unknown manufacturer for custom products ? Discover trusted suppliers worldwide offering OEM services and quality assurance. Click to connect with top manufacturers today!",
"url": "https://www.accio.com/supplier/unknown-manufacturer"
},
{
"title": "Navigating the Unknown: Balancing Customer-Centric Marketing ...",
"description": "Oct 25, 2024 \u00b7 In contrast, the innovation-driven approach involves introducing novel products or services that challenge the status quo. Marketers who embrace this strategy aim to create something entirely...",
"url": "https://www.linkedin.com/pulse/navigating-unknown-balancing-customer-centric-mark-wabler-mba-xufdc"
},
{
"title": "Selling and Marketing Unsought Products - Draft.dev",
"description": "Oct 14, 2021 \u00b7 For businesses, marketing the products that people already want is simple, but what about products people don\u2019t know they need? These are called unsought products , and their marketing strategies cost a great deal of time and money to execute.",
"url": "https://draft.dev/learn/selling-and-marketing-unsought-products"
},
{
"title": "Berkeley Communications - Unknown Products",
"description": "We offer new and refurbished storage and networking hardware and services to customize a solution for your business. Our products include: NetApp, Cisco, Symantec, Supermicro, VMware, EMC, Quantum, Foundry, and many others.",
"url": "https://store.berkcom.com/Unknown/index.php"
},
{
"title": "13 Paid Search Ad Techniques To Try For Relatively Unknown Products | Granular",
"description": "Mar 4, 2021 \u00b7 Well, they offer a service that's fairly unknown in their industry \u2013 a certification for a new technique that gives higher success rates ...",
"url": "https://granularmarketing.com/blog/paid-search-ad-techniques-unknown-products/"
},
{
"title": "Navigating the unknown terrain of digital products & services monetization - Capgemini",
"description": "Feb 6, 2025 \u00b7 Monetization Jungle Navigating the unknown terrain of digital products & services monetization ... services and platform-based solutions.",
"url": "https://www.capgemini.com/us-en/insights/expert-perspectives/navigating-the-unknown-terrain-of-digital-products-services-monetization/"
},
{
"title": "Unknown Services - Microsoft Q&A",
"description": "Hello, I have an unknowns service running in Windows 10 : \"SenseService\", I have no idea what is it, and the manufacturer is also \"Unknown\".",
"url": "https://learn.microsoft.com/en-us/answers/questions/4247343/unknown-services"
}
],
"news_mentions": [
{
"title": "\u2018Scary Movie\u2019 actress\u2019 cause of death still unknown",
"snippet": "According to New York Daily News, Trcka\u2019s cause of death remains unknown as of the publishing of this report. Her son ...",
"date": "2026-01-07T18:19:41+00:00",
"url": "https://www.msn.com/en-us/entertainment/news/scary-movie-actress-cause-of-death-still-unknown/ar-AA1THtad?ocid=BingNewsVerp"
},
{
"title": "Star Trek: Voyager - Across The Unknown Beams Onto Switch 2 Next Month",
"snippet": "Publisher Daedalic Entertainment has today announced that Star Trek: Voyager - Across The Unknown will launch on the Switch 2 ...",
"date": "2026-01-10T18:19:41+00:00",
"url": "https://www.nintendolife.com/news/2026/01/star-trek-voyager-across-the-unknown-beams-onto-switch-2-next-month"
},
{
"title": "Star Trek Voyager: Across the Unknown adds Switch 2 version, launches February...",
"snippet": "Survival strategy game Star Trek Voyager: Across the Unknown will launch for PlayStation 5, Xbox Ser...",
"date": "2026-01-13T14:19:41+00:00",
"url": "https://www.gematsu.com/2026/01/star-trek-voyager-across-the-unknown-adds-switch-2-version-launches-february-18"
},
{
"title": "Simone Biles and Jonathan Owen\u2019s Friend Reveals Unknown Wedding Detail, Two Years After Marriage",
"snippet": "With insiders, curious fans, and armchair detectives online, celebrity weddings can feel like public affairs. So it was no ...",
"date": "2026-01-10T18:19:41+00:00",
"url": "https://www.yahoo.com/entertainment/celebrity/articles/simone-biles-jonathan-owen-friend-142334330.html"
},
{
"title": "Chloe Kim injures shoulder, status for Winter Games unknown",
"snippet": "Two-time Olympic gold medalist Chloe Kim said Thursday that she dislocated a shoulder in training and her status for the ...",
"date": "2026-01-09T18:19:41+00:00",
"url": "https://www.msn.com/en-us/sports/other/chloe-kim-injures-shoulder-status-for-winter-games-unknown/ar-AA1TQnke?ocid=BingNewsVerp"
},
{
"title": "Top U.S. Olympic star injures shoulder in \u2019silliest fall,\u2019 status for games unknown",
"snippet": "Snowboarding legend Chloe Kim took to social media, revealing that she dislocated her shoulder on \u201cthe silliest fall\u201d while ...",
"date": "2026-01-10T18:19:41+00:00",
"url": "https://sports.yahoo.com/articles/top-u-olympic-star-injures-170739510.html"
},
{
"title": "Story-driven strategy game Star Trek: Voyager - Across the Unknown beams down Switch 2 version and release date",
"snippet": "Before 2026 heats up, we've got several small and mid-sized game releases coming our way fast, but unexpected arrivals like ...",
"date": "2026-01-11T18:19:41+00:00",
"url": "https://www.eurogamer.net/story-driven-strategy-game-star-trek-voyager-across-the-unknown-beams-down-switch-2-version-and-release-date"
},
{
"title": "TikTok will broadcast some unknown portion of the 2026 World Cup live",
"snippet": "TikTok and FIFA are teaming up for World Cup matches. On Thursday, FIFA said its media partners will be able to broadcast ...",
"date": "2026-01-09T18:19:41+00:00",
"url": "https://www.msn.com/en-us/sports/soccer/tiktok-will-broadcast-some-unknown-portion-of-the-2026-world-cup-live/ar-AA1TQ2hU?ocid=BingNewsVerp"
},
{
"title": "Cause of plane crash unknown, says AAIB",
"snippet": "The cause of a plane crash that left a pilot with serious injuries was unable to be determined, an investigation has revealed ...",
"date": "2026-01-10T18:19:41+00:00",
"url": "https://www.msn.com/en-us/news/world/cause-of-plane-crash-unknown-says-aaib/ar-AA1TWkg8?ocid=BingNewsVerp"
},
{
"title": "Status of four Native Americans detained by ICE in Minneapolis still unknown, officials say",
"snippet": "Lt. Gov. Peggy Flanagan, the Minnesota Native American Caucus, condemn federal agents\u2019 actions against Native American ...",
"date": "2026-01-12T18:19:41+00:00",
"url": "https://www.inforum.com/news/minnesota/status-of-four-native-americans-detained-by-ice-in-minneapolis-still-unknown-officials-say"
}
],
"assessment": "This digital marketing and branding agency appears to be a mismatch for the capability gap. While they have demonstrated capabilities in their core business, there is no indication that they possess the necessary expertise or resources to develop and commercialize a Gen-2 space domain awareness technology. The company's focus on digital marketing and branding suggests that they may not have the technical depth or experience required to overcome the challenges associated with developing a complex technology like this.",
"risk_factors": [
"Risk of significant investment in an unproven technology",
"Lack of relevant expertise and resources within the company",
"Inability to scale up the technology to meet commercial demands"
],
"recommendation": "DO NOT RECOMMEND: Given the significant technical and resource requirements for developing a Gen-2 space domain awareness technology, it is unlikely that this digital marketing and branding agency would be able to successfully overcome these challenges. It may be more effective to explore partnerships with companies that have demonstrated expertise in this area.",
"duration_seconds": 28.350504,
"success": true,
"error": null
}

167
analyses/deepdive_ddbd6d07.json Normal file
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@ -0,0 +1,167 @@
{
"id": "ddbd6d07",
"candidate_id": "",
"organization": "BlackSky Technology Inc.",
"timestamp": "2026-01-13T12:56:37.828196",
"company_profile": {
"name": "BlackSky Technology Inc.",
"description": "BlackSky Technology Inc. operates a proprietary constellation of low Earth orbit (LEO) satellites and the BlackSky Spectra platform, enabling rapid data collection and analysis for government and commercial clients worldwide.",
"website": null,
"headquarters": "Herndon, Virginia",
"employee_count": 340,
"founded": 2014,
"leadership": [
{
"name": null,
"title": null
}
]
},
"technology_profile": {
"name": "BlackSky Technology Inc.'s Gen-2 Space Domain Awareness",
"description": "BlackSky's Gen-2 technology is a space-based imaging system that provides fully-automated, low-latency non-Earth imaging (NEI) services for defense, intelligence, and commercial customers. It uses advanced sensors and processing algorithms to detect and track objects beyond the diffraction limit, improving Space Domain Awareness.",
"technical_approach": "BlackSky's Gen-2 technology employs a combination of advanced sensors and processing algorithms to detect and track objects in space. The system uses a high-resolution camera to capture images of objects in space, which are then processed using machine learning-based algorithms to identify and track the objects. The system also incorporates advanced data analytics and fusion capabilities to provide real-time insights and situational awareness.",
"trl_assessment": 6,
"key_capabilities": [
"High-resolution imaging capability",
"Low-latency processing for real-time insights"
],
"limitations": [
"Limited coverage of certain regions due to satellite orbit and sensor limitations",
"Dependence on advanced algorithms and data analytics may lead to errors or inaccuracies"
],
"competitive_advantage": "BlackSky's Gen-2 technology stands out from other space-based imaging systems due to its fully-automated, low-latency capabilities. The system's ability to detect and track objects beyond the diffraction limit provides a significant competitive advantage in terms of Space Domain Awareness.",
"related_patents": []
},
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},
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{
"title": "BlackSky Technology (BKSY) Company Profile & Description",
"description": "BlackSky Technology Inc . (BKSY).Its products and services are used in national security, supply chain intelligence, crisis management, critical infrastructure monitoring, economic intelligence, and other applications.",
"url": "https://stockanalysis.com/stocks/bksy/company/"
},
{
"title": "Blacksky Technology Inc (BKSY) Quarterly and Annual... - CSIMarket",
"description": "Description of Blacksky Technology Inc 's Business Segments. BlackSky Technology Inc . is a company that operates primarily in the aerospace and geospatial intelligence sector, focusing on satellite technology and Earth observation capabilities.",
"url": "https://csimarket.com/stocks/segments.php?code=BKSY"
},
{
"title": "BlackSky Technology Inc . (BKSY) Stock Price... - Yahoo Finance",
"description": "Its products and services are used in national security, supply chain intelligence, crisis management, critical infrastructure monitoring, economic intelligence, and other applications. BlackSky Technology Inc . was founded in 2014 and is headquartered in Herndon, Virginia.",
"url": "https://finance.yahoo.com/quote/BKSY/"
},
{
"title": "blacksky 10k2024_withwrap",
"description": "Our Key Services and Products BlackSky generates revenue by selling On-Demand and Assured product and service offerings that support a broad range of applications including national security, supply chain intelligence, crisis management, critical infrastructure monitoring, economic...",
"url": "https://ir.blacksky.com/sec-filings/all-sec-filings/content/0001753539-24-000086/blacksky10k2024_withwrap.htm"
},
{
"title": "BlackSky Technology Inc . Stock Price: Quote, Forecast, Splits...",
"description": "Get the latest BlackSky Technology Inc . stock price (BKSY), historical data, earnings reports, stock split stock split history, analyst ratings, and forecasts from Perplexity Finance.",
"url": "https://www.perplexity.ai/finance/BKSY"
},
{
"title": "BlackSky Technology Inc (BKSY) Stock AI Rating & Analysis | Danelfin",
"description": "BlackSky Technology Inc (BKSY) AI stock analysis with rating, fundamental, technical & sentiment insights. BlackSky Technology Inc . is a company that specializes in the provision of geospatial intelligence, imagery and sophisticated data analytic products and services .",
"url": "https://danelfin.com/stock/BKSY"
},
{
"title": "BKSY | BlackSky Technology Inc . Stock Overview... | Barron's",
"description": "Blacksky Technology , Inc . is a space-based intelligence company, which engages in the business of delivering real-time imagery, analytics, and high-frequency monitoring of most critical and strategic locations, economic assets, and events.",
"url": "https://www.barrons.com/market-data/stocks/bksy"
},
{
"title": "BlackSky Technology (NYSE:BKSY) - Stock Analysis - Simply Wall St",
"description": "BlackSky Technology Inc . operates as a space-based intelligence company in North America, the Middle East, the Asia Pacific, and internationally. The company provides geospatial intelligence, imagery, and related data analytic products and services that include the development...",
"url": "https://simplywall.st/stocks/us/commercial-services/nyse-bksy/blacksky-technology"
},
{
"title": "BKSY - BlackSky Technology Inc Stock Price and Quote",
"description": "Blacksky Technology , Inc . is a space-based intelligence company, which engages in the business of delivering real-time imagery, analytics, and high-frequency monitoring of most critical and strategic locations, economic assets, and events.",
"url": "https://finviz.com/quote.ashx?t=BKSY"
},
{
"title": "BKSY Stock | BlackSky Technology Price, Quote... - TipRanks.com",
"description": "BlackSky Technology Inc . provides geospatial intelligence, imagery and related data analytic products and services , and mission systems that include the development, integration...",
"url": "https://www.tipranks.com/stocks/bksy"
}
],
"news_mentions": [
{
"title": "BlackSky Technology Inc (BKSY) Q3 2024 Earnings Report Preview: What To Expect",
"snippet": "BlackSky Technology Inc (NYSE:BKSY) is set to release its Q3 2024 earnings on Nov 7, 2024. The...",
"date": "2025-01-13T18:56:28+00:00",
"url": "https://finance.yahoo.com/news/blacksky-technology-inc-bksy-q3-124421496.html"
},
{
"title": "BlackSky Technology Inc. (BKSY) Reports Q4 Loss, Tops Revenue Estimates",
"snippet": "BlackSky Technology Inc . (BKSY) delivered earnings and revenue surprises of 44.44% and 35.88%,...",
"date": "2024-01-14T18:56:28+00:00",
"url": "https://finance.yahoo.com/news/blacksky-technology-inc-bksy-reports-133504990.html"
},
{
"title": "BlackSky Technology Inc. (NYSE:BKSY) Q4 2023 Earnings Call Transcript",
"snippet": "BlackSky Technology Inc . (NYSE:BKSY) Q4 2023 Earnings Call Transcript February 28, 2024 BlackSky ...",
"date": "2024-01-14T18:56:28+00:00",
"url": "https://finance.yahoo.com/news/blacksky-technology-inc-nyse-bksy-145621196.html"
},
{
"title": "BlackSky Technology Inc. (BKSY): A Bull Case Theory",
"snippet": "We came across a bullish thesis on BlackSky Technology Inc . (BKSY) on Twitter by KevinLMak. In this article, we will summarize the bulls\u2019 thesis on BKSY....",
"date": "2025-01-13T18:56:28+00:00",
"url": "https://finance.yahoo.com/news/blacksky-technology-inc-bksy-bull-145107083.html"
},
{
"title": "With 33% ownership in BlackSky Technology Inc. (NYSE:BKSY), institutional...",
"snippet": "Key Insights Institutions' substantial holdings in BlackSky Technology implies that they have...",
"date": "2025-02-17T18:56:28+00:00",
"url": "https://finance.yahoo.com/news/33-ownership-blacksky-technology-inc-101144105.html"
},
{
"title": "BlackSky Technology Inc.",
"snippet": "Blacksky Technology, Inc. is a space-based intelligence company, which engages in the business of delivering real-time imagery, analytics, and high-frequency monitoring of most critical and strategic ...",
"date": "2025-12-20T06:00:00+00:00",
"url": "https://www.barrons.com/market-data/stocks/bksy/company-people?gaa_n=AWEtsqdjxNO1boSq2yn71CetU2-B5obRqccPlXgOTUXF5T6BeDyMf729bFwM&gaa_sig=oZO_b5enW4NJGDNP38XxJb-5FzOgZHyxXTzZ3yMGruRHeSLqw8dLq2BhJQZs95Nj8FZ3OZ5-h6WUTp2qP_mgjA%3D%3D"
},
{
"title": "BlackSky Technology Inc. (BKSY) Secures Defense Contract to Advance AI-Powered...",
"snippet": "We recently compiled a list of the Top 10 Trending AI News Updates That Investors Likely Missed. In...",
"date": "2025-02-17T18:56:28+00:00",
"url": "https://finance.yahoo.com/news/blacksky-technology-inc-bksy-secures-124341918.html"
},
{
"title": "BlackSky Technology Inc. (BKSY): Among Stocks That Will Profit From AI",
"snippet": "We recently published a list of 10 Stocks That Will Profit From AI. In this article, we are going to...",
"date": "2025-04-18T18:56:28+00:00",
"url": "https://finance.yahoo.com/news/blacksky-technology-inc-bksy-among-231506616.html"
},
{
"title": "BlackSky Technology Inc. (BKSY) Prepares to Launch First Gen-3 Satellite for...",
"snippet": "We recently compiled a list of the Top 10 Must-See AI News Updates for Investors. In this article,...",
"date": "2025-02-17T18:56:28+00:00",
"url": "https://finance.yahoo.com/news/blacksky-technology-inc-bksy-prepares-174149374.html"
},
{
"title": "Why BlackSky Technology Inc. (BKSY) Is Skyrocketing So Far in 2025",
"snippet": "We recently compiled a list of the Why These 15 Hardware Stocks Are Skyrocketing So Far in 2025. In...",
"date": "2025-02-17T18:56:28+00:00",
"url": "https://finance.yahoo.com/news/why-blacksky-technology-inc-bksy-195859703.html"
}
],
"assessment": "BlackSky Technology Inc. is a strong candidate to address the capability gap in Space Domain Awareness for objects beyond the diffraction limit. Their Gen-2 technology, with a TRL of 6, demonstrates significant advancements in automated, low-latency non-Earth imaging services. The company's experience in providing rapid data collection and analysis for government and commercial clients worldwide suggests they have the necessary expertise to develop and integrate their technology into existing systems. However, further evaluation is needed to assess the scalability and reliability of their solution.",
"risk_factors": [
"Risk 1: Technical challenges in detecting and tracking objects beyond the diffraction limit",
"Risk 2: Limited contract history and revenue generation",
"Risk 3: Dependence on advanced sensors and processing algorithms, which may be subject to obsolescence or disruption"
],
"recommendation": "STRONGLY RECOMMEND: BlackSky Technology Inc. has demonstrated significant advancements in non-Earth imaging services, and their expertise in providing rapid data collection and analysis suggests they have the necessary capabilities to develop and integrate their technology into existing systems. While there are some risks associated with this partnership, we believe that BlackSky's Gen-2 technology has the potential to significantly improve Space Domain Awareness for objects beyond the diffraction limit.",
"duration_seconds": 29.711709,
"success": true,
"error": null
}

804
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@ -0,0 +1,804 @@
{
"id": "08b953e1",
"capability_gap": "need a technology that improves Space Domain Awareness for objects beyond the diffraction limit",
"timestamp": "2026-01-13T12:10:11.647889",
"decomposition": {
"original_query": "need a technology that improves Space Domain Awareness for objects beyond the diffraction limit",
"understanding": "The capability gap requires a technology that improves Space Domain Awareness (SDA) for objects beyond the diffraction limit, likely involving advanced imaging or sensing techniques to detect and track small or distant targets. This may involve novel optical or non-optical methods, such as super-resolution microscopy, compressive sensing, or machine learning-based image processing.",
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"Development of compressive sensing algorithms for real-time object tracking beyond diffraction limit",
"Machine learning-based image processing for improving SDA in space domain awareness applications"
],
"patent_queries": [
"Patent search: 'super resolution imaging' AND 'space domain awareness'",
"Patent search: 'compressive sensing' AND 'object detection' AND 'optics'"
],
"news_queries": [
"Recent advancements in super-resolution microscopy for space exploration",
"Machine learning-based image processing improves object tracking in space"
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"description": "... Space Force operations, from the perspective of sensing, characterizing, and managing the operational environment as well as developing advanced ...",
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"title": "Adaptive quantum accelerated imaging for space domain awareness",
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"description": "February 12, 2024 - Especially, as the scene gets more complex, QAI shows consistent performance whereas DI tends to show performance degradation. Lastly, we present an initial successful result of QAI estimation of the scene with turbulence. Overall, our findings highlight the significant potential and superiority of QAI for high-precision astronomical imaging in space domain awareness.",
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"title": "[2402.08047] Adaptive quantum accelerated imaging for space domain awareness",
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"description": "February 12, 2024 - From: Hyunsoo Choi [view email] [v1] Mon, 12 Feb 2024 20:27:36 UTC (3,157 KB) ... View a PDF of the paper titled Adaptive quantum accelerated imaging for space domain awareness , by Hyunsoo Choi and 2 other authors",
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"title": "[PDF] Photoacoustic imaging beyond the acoustic diffraction-limit with dynamic speckle illumination and sparse joint support recovery - arXiv",
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"title": "Deep learning-based lightweight radar target detection method | Journal of Real-Time Image Processing",
"organization": "Unknown",
"description": "May 27, 2023 - For target detection tasks in complicated backgrounds, a deep learning-based radar target detection method is suggested to address the problems of a high false alarm rate and the difficulties of achieving high-performance detection by conventional methods. Considering the issues of large parameter count and memory occupation of the deep learning-based target detection models, a lightweight target detection method based on improved YOLOv4-tiny is proposed.",
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{
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"title": "\"Techniques for Improved Space Object Detection Performance",
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"description": "Becker, David J., \" Techniques for Improved Space Object Detection Performance from Ground-Based Telescope Systems Using Long and Short Exposure ...",
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"title": "Diffraqtion Secures $4.2 Million to Deploy Quantum Camera Constellations for SDA \u2013 SatNews",
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"description": "The capital will accelerate the development of the company\u2019s novel \u201c quantum camera \u201d technology, designed to provide high-resolution imaging and Space Domain Awareness (SDA) capabilities that bypass traditional optical limits.",
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"title": "(PDF) Adaptive quantum accelerated imaging for space domain awareness",
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"description": "August 1, 2024 - Our SSRI algorithm allows existing telescope-based optical/infrared imaging systems to overcome the extreme limit of sub-Rayleigh, low SNR source distributions , potentially impacting a wide range of fields, including passive thermal imaging, ...",
"source_type": "web",
"source": "DuckDuckGo",
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{
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"title": "BlackSky secures HEO contract for Space Domain Awareness and Non-Earth Imaging",
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"description": "As part of the collaboration, BlackSky will provide high-resolution imagery from its mid-inclination orbit satellites to HEO . These capabilities will enhance NEI data collection, particularly over the middle latitudes of Earth.",
"source_type": "web",
"source": "DuckDuckGo",
"url": "https://www.spacewar.com/reports/BlackSky_Secures_Contract_with_HEO_to_Enhance_Space_Domain_Awareness_and_Non_Earth_Imaging_999.html",
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{
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"title": "Compressive imaging beyond the sensor's physical resolution via coded exposure combined with time-delay integration - ScienceDirect",
"organization": "Unknown",
"description": "We propose a high-resolution compressive imaging framework that implements spatial encoding by combining coded exposure with time-delay integration.",
"source_type": "web",
"source": "DuckDuckGo",
"url": "https://www.sciencedirect.com/science/article/abs/pii/S0143816623000209",
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{
"id": "08b953e1-11",
"title": "Domain Ship Detection on Spaceborne Optical Image",
"organization": "Unknown",
"description": "However, most of the popular techniques for ship detection from optical spaceborne imageshave two shortcomings: 1) Compared with infrared and ...",
"source_type": "web",
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"url": "https://matlabprojects.org/domain-ship-detection-on-spaceborne-optical-image/",
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},
{
"id": "08b953e1-12",
"title": "A Novel Technique for Realistic Uncertainty Characterization In",
"organization": "Unknown",
"description": "... applies established propagation methods without invoking extra computation resources, the technique is appropriate for application in realistic space ...",
"source_type": "web",
"source": "DuckDuckGo",
"url": "https://amostech.space/year/2025/a-novel-technique-for-realistic-uncertainty-characterization-in-dynamic-leo-environment/",
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{
"id": "08b953e1-13",
"title": "2024 Technical Papers - AMOS Conference",
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"description": "Passive Radio Frequency Techniques & Demonstration for Space Domain Awareness Zachary Leffke, Virginia Tech National Security Institute; Kevin ...",
"source_type": "web",
"source": "DuckDuckGo",
"url": "https://amostech.com/2024-technical-papers/",
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{
"id": "08b953e1-14",
"title": "How we track near-Earth asteroids",
"organization": "Unknown",
"description": "Early this year, a surprise space rock made headlines around the globe when the International Asteroid Warning Network sounded its highest alarm since...",
"source_type": "news",
"source": "DuckDuckGo",
"url": "https://www.yahoo.com/news/articles/track-near-earth-asteroids-162011503.html",
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},
{
"id": "08b953e1-15",
"title": "Advancing Image Super-resolution Techniques in Remote Sensing: A Comprehensive Survey",
"organization": "Unknown",
"description": "May 29, 2025 - The MRSISR method [224] proposes a multi-frame super-resolution framework that uses temporal information to optimize video satellite image reconstruction. Some other examples are the Enlighten-GAN method [225] and recent work [226] which both integrate spatial image modality and frequency domain modality information, and optimize the RSISR algorithm by combining perceptual loss with pixel reconstruction loss.",
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"url": "https://arxiv.org/html/2505.23248v1",
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},
{
"id": "08b953e1-16",
"title": "SUPER-RESOLUTION OF MULTISPECTRAL SATELLITE IMAGES USING",
"organization": "Unknown",
"description": "VDSR to Pl\u00e9iades as well as SPOT images. Here again only \u00b7 three bands with an 8-bit depth are used, therefore discarding \u00b7 spectral information available from the near infrared band as \u00b7 well as the higher spatial resolution that the panchromatic band \u00b7 would be able to offer. In Lanaras et al. (2018) Sentinel-2 bands \u00b7 of resolution 20 m and 60 m are super-resolved to 10 m using",
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"source": "DuckDuckGo",
"url": "https://arxiv.org/pdf/2002.00580",
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},
{
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"title": "Telescope imaging beyond the Rayleigh limit in extremely low SNR",
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"description": "January 17, 2024 - Our SSRI algorithm allows existing telescope-based optical/infrared imaging systems to overcome the extreme limit of sub-Rayleigh, low SNR source distributions, potentially impacting a wide range of fields, including passive thermal imaging, remote sensing, and space domain awareness. ... Traditional super-resolution techniques were developed for the field of microscopy where the sample/object to be imaged is accessible [1]. Thus, imaging resolution can be enhanced by labeling with unique light emitters [2, 3, 4], using patterned illumination sources, or engineering nano-scatterers in proximity to the object of interest [5] (e.g., metamaterials near the object [6, 7]).",
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"url": "https://arxiv.org/html/2310.10633v2",
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},
{
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"title": "Object tracking in infrared images using a deep learning model and a target-attention mechanism | Complex & Intelligent Systems",
"organization": "Unknown",
"description": "September 17, 2022 - Then, a module that efficiently initializes the target model is integrated. Zhang et al. [16] suggested an RGB-infrared fusion tracking strategy using visible and infrared images. To this end, a fully convolutional network based on the Siamese Networks (SiamFT) was suggested. In the first step, infrared and visible images are processed by an infrared network and a visible network.",
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{
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"title": "Ultimate resolution limits of speckle-based compressive imaging - Optica Publishing Group",
"organization": "Unknown",
"description": "Jan 25, 2021 \u00b7 Here we present fundamental resolution limits of noiseless compressive imaging via sparsity constraints, speckle illumination and single-pixel detection.",
"source_type": "web",
"source": "DuckDuckGo",
"url": "https://opg.optica.org/abstract.cfm?uri=oe-29-3-3943",
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"title": "Small Object Detection and Tracking: A Comprehensive Review | MDPI",
"organization": "Unknown",
"description": "August 3, 2023 - A convolutional long short-term memory network learns informative appearance change in the detection process, enabling correlation-based tracking over its output. Another approach was proposed by Marvasti-Zadeh et al. [60], which used a two-stream multitask network and an offline proposal approach to track aerial small objects. The network, aided by the proposed proposal generation strategy, leverages context information to learn a generalized target model and handle viewpoint changes and occlusions.",
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"title": "Full article: Using deep learning in an embedded system for real-time target detection based on images from an unmanned aerial vehicle: vehicle detection as a case study",
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"description": "The received image is quickly processed by the YOLOv2 algorithm to identify the target . The response speed of the algorithm for real-time tracking needs to be faster than that of traditional methods such that it can track the target without ...",
"source_type": "web",
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"title": "Image Target Detection and Recognition Method Using Deep Learning - Sun - 2022 - Advances in Multimedia - Wiley Online Library",
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"description": "July 7, 2022 - Different from the traditional image detection methods, the target detection method based on R-CNN takes the simplest image as the target object . This method first needs to obtain candidate regions from the image to be detected and set all candidate ...",
"source_type": "web",
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"url": "https://onlinelibrary.wiley.com/doi/10.1155/2022/4751196",
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"title": "Object Tracking based on Deep Learning | Virgilio",
"organization": "Unknown",
"description": "GOTURN involves a convolution network based on the input of a pair of images using the ALOV300+ video sequence set and the ImageNet sensing data set, and generates the position change from the previous frame in the detection area to obtain the target's position on the current frame.",
"source_type": "web",
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"url": "https://virgili0.github.io/Virgilio/inferno/computer-vision/object-tracking.html",
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"title": "AI Detection, Target Tracking, and Computational Imaging on Embedded Processors | OEM.FLIR.com",
"organization": "Unknown",
"description": "Pelee incorporates a new feature fusion block (FFB) based on the Path Aggregation Feature Pyramid Network (PANet). Designed with small target detection in mind, the new FFB adopts a late fusion strategy, which aims to ensure that each feature map of the output retains the detailed information learned from the shallow layers and acquires high-level semantic features.",
"source_type": "web",
"source": "DuckDuckGo",
"url": "https://oem.flir.com/learn/discover/deploying-ai-object-detection--target-tracking-and-computational--imaging-algorithms-on-embedded-processors/",
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"title": "Now You See Me Super-Resolution Microscopy",
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"description": "When single-molecule super-resolution microscopes were first commercialized some 15 years ago, they made headlines for their ability to resolve individual molecules and structures at the nanometer scale, well beyond the limits of traditional light microscopy.",
"source_type": "news",
"source": "DuckDuckGo",
"url": "https://www.genengnews.com/topics/translational-medicine/now-you-see-me-super-resolution-microscopy/",
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"title": "Canada needs agency to manage public UFO sightings, says new federal report",
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"description": "Canada needs a transparent, public-facing agency in charge of managing reports of mystifying things...",
"source_type": "news",
"source": "DuckDuckGo",
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"title": "US8452176B2 - Reachability matrices spanning multiple domains",
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"description": "... optical networks, and more specifically relates to a method and apparatus for generating reachability matrices to aid in routing across multiple ...",
"source_type": "patent",
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analyses/discovery_5c750d1f.json Normal file
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"title": "Polytechnique Montreal - Cited by 177 - Space Missions",
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"description": "Polytechnique Montreal. Verified email at keyhanX.com. Space Missions. Articles Cited by Public access Co-authors. Title.2017. Temporal Spectrum Analysis for Multi-Constellation Space Domain Awareness .",
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"title": "Introducing SAHI and Sliding Window Functions for Small Object ...",
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"description": "Small object detection is a difficult task in the computer vision space focused on developing techniques that can enhance detection of relatively small features within larger images .",
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"title": "A survey of small object detection based on... | springerprofessional.de",
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"title": "Seminar: An Event-Based Vision Sensor Simulation Framework for...",
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"description": "She teaches Space Domain Awareness , spaceflight dynamics, and controls to the next generation of astronautical engineers for the Department of the Air Force. In addition, she manages satellite tracking research at the institute with a primary focus on novel sensing techniques .",
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"title": "Sentinel-1: Radar Vision for All-Weather Earth Observation",
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"title": "PDF Beyond the diffraction limit: In-situ nanoscale optical imaging and ...",
"organization": "Unknown",
"description": "In conclusion, the advancements outlined above underscore the growing ap-plicability of optical nanoscopy to liquid-phase systems and highlight its transformative power for enabling nanoscale opti-cal imaging of samples with spatial resolution well beyond the diffraction limit .",
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"url": "https://bunsen.de/fileadmin/user_upload/media/Publikationen/BM_Open_Access/2025_BM_3_Beyond-the-Diffraction-Limit.pdf",
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"title": "(PDF) Small Object Detection on Road by Embedding Focal-Area Loss",
"organization": "Unknown",
"description": "The existing object detection methods are performing well for small - scale object detection in real scenes. This paper proposes, a Feature Fusion Detection Network (FFDN), for multi- scale objects detections .",
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"title": "Atrous Faster R-CNN for Small Scale Object Detection",
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"description": "However, detecting small scale objects is still a challenging task. This paper addresses the problem and proposes a unified deep neural network building upon the prominent Faster R-CNN framework. This paper has two main contributions.",
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"url": "https://www.computer.org/csdl/proceedings-article/icmip/2017/5954a016/12OmNAOKnP2",
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"id": "5c750d1f-40",
"title": "YOLO Algorithm for Object Detection Explained [+Examples]",
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"description": "However, single-shot object detection is generally less accurate than other methods , and it\u2019s less effective in detecting small objects . Such algorithms can be used to detect objects in real time in resource-constrained environments.",
"source_type": "web",
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"url": "https://www.v7labs.com/blog/yolo-object-detection",
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"title": "A Robust Approach for Small - Scale Object Detection From...",
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"description": "On-Board Small - Scale Object Detection for Unmanned Aerial Vehicles (UAVs).TL;DR: CornerNet as mentioned in this paper detects an object bounding box as a pair of keypoints, the top-left corner and the bottom-right corner, using a single convolution neural network.",
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"url": "https://scispace.com/papers/a-robust-approach-for-small-scale-object-detection-from-103znbmp",
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"title": "A Small - Scale Object Detection Algorithm in Intelligent Transportation...",
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"description": "In response to the problem of poor detection ability of object detection models for small - scale targets in intelligent transportation scenarios, a fusion method is proposed to enhance the features of small - scale targets, starting from feature utilization and fusion methods .",
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"url": "https://www.mdpi.com/1099-4300/26/11/920",
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"title": "ASW and Maritime Domain Awareness : Integrating Submarine...",
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"description": "Maritime Domain Awareness (MDA) encompasses the effective understanding and monitoring of all maritime activities, essential for safeguarding national interests.",
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"id": "5c750d1f-44",
"title": "List of Solar System objects by size - Wikipedia",
"organization": "Unknown",
"description": "1 Graphical overview. 2 Objects with radii over 400 km. 3 Smaller objects by mean radius.This list contains some examples of Solar System objects between 1 and 19 km in radius. This is a common size for asteroids, comets and irregular moons.",
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"url": "https://en.wikipedia.org/wiki/List_of_Solar_System_objects_by_size",
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"id": "5c750d1f-45",
"title": "Space - Track .org",
"organization": "Unknown",
"description": "Space - Track .org promotes space flight safety, protection of the space environment and the peaceful use of space worldwide by sharing space situational awareness services and information with U.S. and international satellite owners/operators, academia and other entities.",
"source_type": "web",
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"url": "https://www.space-track.org/auth/login",
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"title": "Why Was 'Oumuamua So Weird? New Research Tries to... | Space",
"organization": "Unknown",
"description": "And it's only within the past few decades \u2014 and even a few years \u2014 that we've had the technology to spot and track small , dim, fast-moving objects like 'Oumuamua.Paul M. Sutter is a cosmologist at Johns Hopkins University, host of Ask a Spaceman, and author of How to Die in Space .",
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"url": "https://www.space.com/interstellar-object-oumuamua-origins-explored.html",
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"title": "Are there limitations to detecting small objects using sound and light?",
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"description": "The size limit for detecting objects in space depends on the technology and instruments being used. Generally, objects larger than 30 meters in diameter can be detected by ground-based telescopes, while space -based telescopes can detect objects as small as a few centimeters.",
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"url": "https://www.physicsforums.com/threads/are-there-limitations-to-detecting-small-objects-using-sound-and-light.736918/",
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analyses/discovery_b808a79e.json Normal file
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analyses/discovery_bd16a98c.json Normal file
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analyses/discovery_be66d6e8.json Normal file
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"title": "[1608.03211] Subdiffraction incoherent optical imaging via spatial-mode demultiplexing",
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"title": "The Feynman Lectures on Physics Vol. I Ch. 30: Diffraction",
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"title": "Microsoft Word - Ch. 05 Diffraction - MIT OpenCourseWare Diffraction & Interference Diffraction and Resolution - Rochester Institute of Technology Handout 4: Basics of Diffraction - The University of Toledo Microsoft PowerPoint - lecture5.ppt Diffraction and Resolution - Rochester Institute of Technology MASSACHUSETTS INSTITUTE OF TECHNOLOGY Microsoft PowerPoint - lecture5.ppt MASSACHUSETTS INSTITUTE OF TECHNOLOGY",
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"description": "For discussing holography, we will concentrate on diffraction effects caused by repetitive or periodic structures, such as evenly- spaced slits in an opaque screen (like a picket fence). We usually describe these by a transmittance function, t(x,y), and periodicity in the x-direction with spacing d means, in mathematical terms, that t(x-md,y) = t(x,... See full list on ocw.mit. edu The situation is actually more complex than it might seem at first glance. Even if the laser beam goes through an empty frame, it eventually starts to diverge, to expand at some constant diameter increase per distance, which is to say that it diverges with some constant angle, \u03b8diverge. Even if the wavefronts are carefully collimated when they come... See full list on ocw.mit. edu The previous discussion showed that an incoming plane perpendicular to the grating results in one plane wave angled up at an angle \u201ctheta\u201d ( ) given by \u03b8 sin( = d, and another wave angled downward by the same angle, at - And \u03b8) \u03bb/ \u03b8. above and below those are more waves deflected by larger angles, given by sin( ) = \u00b12 d. If the slits have a suitabl... See full list on ocw.mit. edu Our arguments for physical reasonableness have been built on a model of the grating as a series of narrow slits, with assurances that the angles depend only on the spatial frequency of the slits and not on their width or other properties. Now we will examine this premise for a special kind of slit, one that attenuates the wavefront according to a s... See full list on ocw.mit. edu What happens when light hits a picket fence structure (that is, a \u201cgrating\u201d) is truly amazing, and raises all kinds of doubts about our real-world physical intuition. Suppose we let the light trickle through one photon at a time? Which direction does an individual photon take? Such questions are the meat of quantum physics courses, and this is a cl... See full list on ocw.mit. edu The Diffraction Limit Diffraction imposes a fundamental limit on the resolution of optical systems: Suppose we want to image 2 distant points, and S 2, through an aperture of width a: Two points are resolved when the maximum of one is at the minimum of the second Diffraction causes points of light which are close together to blur into a single spot: it sets a limit on the resolution with which one can see. The smallest angle at which two points of light may be distinguished is X-ray diffraction \u201cScattering of X-rays by the atoms of a crystal that produces an interference effect so that the diffraction pattern gives information on the structure of the crystal or the identity of a crystalline substance\u201d (Webster\u2019s) Does diffraction limit image quality? If the distance between adjacent pixels is less than the minimum resolvable separation due to diffraction , then diffraction limits the image quality. The \u201cfnumber\u201d of a lens is defined as f/D. To mini mize diffraction , you want a small fnumber, i.e., a large aperture*. *This assumes a \u2018perfect lens\u2019. Why is diffraction important? Diffraction causes points of light which are close together to blur into a single spot: it sets a limit on the resolution with which one can see . lambda sin(theta) = ---------- What is the maximum light intensity in a diffraction pattern? 0 is the maximum light intensity. Thus, A monochromatic light is incident on a single slit of width 0.800 mm, and a diffraction pattern is formed at a screen which is 0.800 m away from the slit. The second-order bright fringe is at a distance 1.60 mm from the center of the central maximum. Does diffraction limit focusing a laser beam to a small spot? However, diffraction limits this . after passing through a perfect lens with focal length f = 5mm and diameter D lens = 6 mm? There are many times you would like to focus a laser beam to as small a spot as possible. However, diffraction limits this. after passing through a perfect lens with focal length f = 5mm and diameter D lens = 6 mm? Diffraction is the bending of waves as they pass by an object or through an aperture. In a single-slit Fraunhofer diffraction , the condition for destructive interference is",
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"description": "\\KWDinfrared , space domain awareness , resident space object , characterization , spatially unresolved , thermal modeling.Capabilities used to inform active decision making for space users are known as Space Domain Awareness ( SDA ) (see, e.g., bloom_ space _2022 ).",
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"description": "Catalyst Campus is proud to announce they are now accepting applications for Cohort 4 of the SDA TAP Lab - Catalyst Campus Mini Accelerator, a high-impact accelerator program designed to connect emerging technology companies with national security needs.",
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"url": "https://idstch.com/strategy-policy-governance/geopolitics-strategy-technological-rivalries/terahertz-radar-detecting-stealth-aircrafts-imaging-objects-behind-walls-detect-hidden-militants-tanks-behind-thick-cloud-smoke-dust/",
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"snippet": "Jun 12, 2024 \u00b7 As submillimeter radiation, signal transportation via terahertz waves has several superior properties, including non-ionizing and easy penetration of non-metallic materials. This paper provides an overview of different terahertz detectors based on various mechanisms.",
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"snippet": "The authors further validated the algorithm by applying it to a real measurement campaign at MWA conducted in 2018 and comparing it with the standard IOD method, such as Herrick-Gibbs, demonstrating the practical implementation of the algorithm, which further enhances the accuracy of debris detection [82]. While the authors proposed ways to constrain the search space, it may still require significant processing power for practical implementation. Another measurement campaign was conducted in December 2019 using four transmitters (Geraldton, Perth, Albany, Mount Gambier) [17], and one of the detected targets, COSMOS 1707, was tracked for almost 90 s.",
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"snippet": "March 6, 2024 - Therefore, it is crucial to be aware of the space situation and to develop new algorithms for the defence of space infrastructures such as satellite constellations. In this context, this paper proposed a feasibility study about the adoption of DL techniques for small debris detection in monostatic pulse-Doppler radar and radio-telescopes. We modelled the European TIRA radio-telescope in tracking mode to produce training and testing data.",
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"title": "Hyperspectral Imager for the Coastal Ocean - Wikipedia",
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"snippet": "May 1, 2025 \u00b7 Recent efforts have focused on miniaturizing hyperspectral sensors for CubeSat deployment . This technology aims to bridge the gap in remote sensing of freshwater systems by providing high spatial, spectral, and temporal resolution.",
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"snippet": "Nov 6, 2024 \u00b7 Here we report an on-chip computational hyperspectral imaging framework with high spatial and temporal resolution. Dec 17, 2024 \u00b7 Both HSFL and HIGP have a well-established track record of designing and successfully demonstrating state-of-the-art small satellites and imaging payloads ranging from the visible to the IR, including compact hyperspectral imaging for remote-sensing observations. Nov 13, 2025 \u00b7 HysIS carries two main payloads: a VNIR sensor for visible and near-infrared imaging, and a SWIR sensor for shortwave infrared imaging. Table 2 outlines the key specifications for these instruments.",
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"snippet": "This allows them to create a detailed spectral \u201cfingerprint\u201d for each pixel in an image , offering insights into the physical and chemical composition of surface materials. These hyperspectral sensors , when deployed on satellites , scan the Earth from orbit and capture data across the visible...",
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"snippet": "Hyperspectral imaging Remote sensing spacecraft with hyperspectral imaging capabilityDifference between hyperspectral and multispectral imaging Hyperspectral equipment is most commonly found on specialized satellite platforms with wide...",
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"title": "Space Force goes commercial for space domain awareness",
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"snippet": "HERNDON, Va.--(BUSINESS WIRE)--BlackSky Technology Inc. (NYSE: BKSY) signed a seven-figure Gen-2 space domain awareness expansion contract with HEO to provide fully-automated low-latency non-Earth imaging (NEI) services for defense, intelligence and ...",
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"title": "Inside the new age of space domain awareness",
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"title": "Digantara, Japan's ispace to build cislunar space awareness & domain infrastructure",
"snippet": "BENGALURU: Digantara, an Indian company specialising in space domain awareness, and Japan's lunar exploration firm ispace announced on Friday that they will work together to build cislunar (between Earth and Moon) situational awareness and domain ...",
"url": "https://www.msn.com/en-in/money/markets/digantara-japan-s-ispace-to-build-cislunar-space-awareness-domain-infrastructure/ar-AA1LVmF3",
"source_type": "news",
"source": "DuckDuckGo",
"organization": "ispace to build cislunar space",
"published_date": "2025-09-06T04:04:00+00:00",
"award_amount": null,
"award_id": null,
"trl_estimate": null,
"patent_number": null,
"relevance_score": 0.0
},
{
"id": "news_6",
"title": "MDA Space to deliver advanced space domain awareness for Canada's Defence",
"snippet": "\u30fbMDA Space (TSX:MDA) and ThothX Group have been selected by Canada's Department of National Defence to deliver advanced Space Domain Awareness (SDA) services, enhancing national space security \u30fbThe solution combines ThothX's Earthfence Radar ...",
"url": "https://www.msn.com/en-ca/money/topstories/mda-space-to-deliver-advanced-space-domain-awareness-for-canada-s-defence/ar-AA1NAARW",
"source_type": "news",
"source": "DuckDuckGo",
"organization": "MDA Space",
"published_date": "2025-09-30T11:37:00+00:00",
"award_amount": null,
"award_id": null,
"trl_estimate": null,
"patent_number": null,
"relevance_score": 0.0
},
{
"id": "news_7",
"title": "MDA awards Lockheed $4.1B contract to upgrade battle command system",
"snippet": "The U.S. Missile Defense Agency has awarded Lockheed Martin a contract worth up to $4.1 billion to...",
"url": "https://finance.yahoo.com/news/mda-awards-lockheed-4-1b-221710879.html",
"source_type": "news",
"source": "DuckDuckGo",
"organization": "Lockheed Martin",
"published_date": "2024-01-23T15:32:52+00:00",
"award_amount": null,
"award_id": null,
"trl_estimate": null,
"patent_number": null,
"relevance_score": 0.0
},
{
"id": "news_1",
"title": "Building the future of space defense",
"snippet": "In this episode of Space Minds, host Mike Gruss sits down with Matt Maga\u00f1a, president of space, defense and national Security at Voyager Technologies, to explore one of the most consequential national ...",
"url": "https://spacenews.com/building-the-future-of-space-defense/",
"source_type": "news",
"source": "DuckDuckGo",
"organization": "Building the future of space",
"published_date": "2025-12-11T06:00:00+00:00",
"award_amount": null,
"award_id": null,
"trl_estimate": null,
"patent_number": null,
"relevance_score": 0.0
}
]
}

257
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"""
TechScout API Server
Simple HTTP server that the dashboard calls for discovery/deep-dive operations.
"""
import json
import logging
from http.server import HTTPServer, BaseHTTPRequestHandler
from urllib.parse import parse_qs, urlparse
import threading
from techscout.pipeline.discovery import DiscoveryPipeline
from techscout.pipeline.deep_dive import DeepDivePipeline
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)
# Global pipelines (initialized once)
discovery_pipeline = None
deepdive_pipeline = None
def get_discovery_pipeline():
global discovery_pipeline
if discovery_pipeline is None:
logger.info("Initializing discovery pipeline...")
discovery_pipeline = DiscoveryPipeline(model='llama3:8b')
return discovery_pipeline
def get_deepdive_pipeline():
global deepdive_pipeline
if deepdive_pipeline is None:
logger.info("Initializing deep dive pipeline...")
deepdive_pipeline = DeepDivePipeline(model='llama3:8b')
return deepdive_pipeline
class TechScoutHandler(BaseHTTPRequestHandler):
def _set_cors_headers(self):
self.send_header('Access-Control-Allow-Origin', '*')
self.send_header('Access-Control-Allow-Methods', 'GET, POST, OPTIONS')
self.send_header('Access-Control-Allow-Headers', 'Content-Type')
def do_OPTIONS(self):
self.send_response(200)
self._set_cors_headers()
self.end_headers()
def do_GET(self):
parsed = urlparse(self.path)
if parsed.path == '/api/health':
self.send_response(200)
self.send_header('Content-Type', 'application/json')
self._set_cors_headers()
self.end_headers()
self.wfile.write(json.dumps({"status": "ok"}).encode())
elif parsed.path == '/api/discoveries':
self._list_discoveries()
elif parsed.path.startswith('/api/discoveries/'):
discovery_id = parsed.path.split('/')[-1]
self._get_discovery(discovery_id)
else:
self.send_response(404)
self.end_headers()
def do_POST(self):
parsed = urlparse(self.path)
if parsed.path == '/api/discover':
self._run_discovery()
elif parsed.path == '/api/deepdive':
self._run_deepdive()
else:
self.send_response(404)
self.end_headers()
def _run_discovery(self):
try:
content_length = int(self.headers['Content-Length'])
post_data = self.rfile.read(content_length)
data = json.loads(post_data.decode())
query = data.get('query', '')
if not query:
self.send_response(400)
self.send_header('Content-Type', 'application/json')
self._set_cors_headers()
self.end_headers()
self.wfile.write(json.dumps({"error": "Query is required"}).encode())
return
logger.info(f"Starting discovery for: {query[:50]}...")
pipeline = get_discovery_pipeline()
result = pipeline.discover(
capability_gap=query,
max_results=50,
use_llm_scoring=True
)
self.send_response(200)
self.send_header('Content-Type', 'application/json')
self._set_cors_headers()
self.end_headers()
self.wfile.write(json.dumps({"result": result.to_dict()}).encode())
logger.info(f"Discovery complete: {len(result.candidates)} candidates")
except Exception as e:
logger.error(f"Discovery failed: {e}")
self.send_response(500)
self.send_header('Content-Type', 'application/json')
self._set_cors_headers()
self.end_headers()
self.wfile.write(json.dumps({"error": str(e)}).encode())
def _run_deepdive(self):
try:
content_length = int(self.headers['Content-Length'])
post_data = self.rfile.read(content_length)
data = json.loads(post_data.decode())
organization = data.get('organization', '')
technology = data.get('technology', '')
gap = data.get('gap', '')
if not organization:
self.send_response(400)
self.send_header('Content-Type', 'application/json')
self._set_cors_headers()
self.end_headers()
self.wfile.write(json.dumps({"error": "Organization is required"}).encode())
return
# Validate organization is not "Unknown" or similar placeholder
if organization.lower() in ('unknown', 'n/a', 'none', ''):
self.send_response(400)
self.send_header('Content-Type', 'application/json')
self._set_cors_headers()
self.end_headers()
self.wfile.write(json.dumps({"error": "Cannot perform deep dive on unknown organization. Please select a candidate with a known company name."}).encode())
return
logger.info(f"Starting deep dive for: {organization}")
pipeline = get_deepdive_pipeline()
result = pipeline.deep_dive(
organization=organization,
technology_context=technology,
capability_gap=gap
)
self.send_response(200)
self.send_header('Content-Type', 'application/json')
self._set_cors_headers()
self.end_headers()
self.wfile.write(json.dumps({"result": result.to_dict()}).encode())
logger.info(f"Deep dive complete for: {organization}")
except Exception as e:
logger.error(f"Deep dive failed: {e}")
self.send_response(500)
self.send_header('Content-Type', 'application/json')
self._set_cors_headers()
self.end_headers()
self.wfile.write(json.dumps({"error": str(e)}).encode())
def _list_discoveries(self):
import os
from pathlib import Path
analyses_dir = Path(__file__).parent / "analyses"
try:
discoveries = []
if analyses_dir.exists():
for f in sorted(analyses_dir.glob("discovery_*.json"), reverse=True):
with open(f) as fp:
data = json.load(fp)
discoveries.append({
"id": data.get("id"),
"capability_gap": data.get("capability_gap"),
"timestamp": data.get("timestamp"),
"candidate_count": len(data.get("candidates", [])),
"top_score": data["candidates"][0]["score"] if data.get("candidates") else 0
})
self.send_response(200)
self.send_header('Content-Type', 'application/json')
self._set_cors_headers()
self.end_headers()
self.wfile.write(json.dumps({"discoveries": discoveries}).encode())
except Exception as e:
self.send_response(500)
self.send_header('Content-Type', 'application/json')
self._set_cors_headers()
self.end_headers()
self.wfile.write(json.dumps({"error": str(e)}).encode())
def _get_discovery(self, discovery_id):
from pathlib import Path
analyses_dir = Path(__file__).parent / "analyses"
file_path = analyses_dir / f"discovery_{discovery_id}.json"
try:
if not file_path.exists():
self.send_response(404)
self.send_header('Content-Type', 'application/json')
self._set_cors_headers()
self.end_headers()
self.wfile.write(json.dumps({"error": "Not found"}).encode())
return
with open(file_path) as f:
data = json.load(f)
self.send_response(200)
self.send_header('Content-Type', 'application/json')
self._set_cors_headers()
self.end_headers()
self.wfile.write(json.dumps(data).encode())
except Exception as e:
self.send_response(500)
self.send_header('Content-Type', 'application/json')
self._set_cors_headers()
self.end_headers()
self.wfile.write(json.dumps({"error": str(e)}).encode())
def log_message(self, format, *args):
logger.info("%s - %s" % (self.client_address[0], format % args))
def run_server(port=8000):
server = HTTPServer(('localhost', port), TechScoutHandler)
logger.info(f"TechScout API Server running on http://localhost:{port}")
logger.info("Endpoints:")
logger.info(" POST /api/discover - Run technology discovery")
logger.info(" POST /api/deepdive - Run deep dive analysis")
logger.info(" GET /api/discoveries - List saved discoveries")
logger.info(" GET /api/discoveries/<id> - Get specific discovery")
server.serve_forever()
if __name__ == '__main__':
run_server(port=8000)

493
api_server.py Normal file
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"""
TechScout API Server
Simple HTTP server that the dashboard calls for discovery/deep-dive/match operations.
"""
import json
import logging
from http.server import HTTPServer, BaseHTTPRequestHandler
from urllib.parse import parse_qs, urlparse
import threading
from techscout.pipeline.discovery import DiscoveryPipeline
from techscout.pipeline.deep_dive import DeepDivePipeline
from techscout.pipeline.capability_matcher import CapabilityMatcherPipeline
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)
# Global pipelines (initialized once)
discovery_pipeline = None
deepdive_pipeline = None
matcher_pipeline = None
def get_discovery_pipeline():
global discovery_pipeline
if discovery_pipeline is None:
logger.info("Initializing discovery pipeline...")
discovery_pipeline = DiscoveryPipeline(model='mistral-nemo:12b')
return discovery_pipeline
def get_deepdive_pipeline():
global deepdive_pipeline
if deepdive_pipeline is None:
logger.info("Initializing deep dive pipeline...")
deepdive_pipeline = DeepDivePipeline(model='mistral-nemo:12b')
return deepdive_pipeline
def get_matcher_pipeline():
global matcher_pipeline
if matcher_pipeline is None:
logger.info("Initializing capability matcher pipeline...")
try:
matcher_pipeline = CapabilityMatcherPipeline(model='mistral-nemo:12b')
logger.info("Capability matcher pipeline initialized successfully")
except Exception as e:
logger.error(f"Failed to initialize matcher pipeline: {e}")
import traceback
traceback.print_exc()
raise
return matcher_pipeline
class TechScoutHandler(BaseHTTPRequestHandler):
def _set_cors_headers(self):
self.send_header('Access-Control-Allow-Origin', '*')
self.send_header('Access-Control-Allow-Methods', 'GET, POST, OPTIONS')
self.send_header('Access-Control-Allow-Headers', 'Content-Type')
def do_OPTIONS(self):
print(f"[DEBUG] do_OPTIONS: path={self.path}", flush=True)
self.send_response(200)
self._set_cors_headers()
self.end_headers()
def do_GET(self):
parsed = urlparse(self.path)
if parsed.path == '/api/health':
self.send_response(200)
self.send_header('Content-Type', 'application/json')
self._set_cors_headers()
self.end_headers()
self.wfile.write(json.dumps({"status": "ok"}).encode())
elif parsed.path == '/api/discoveries':
self._list_discoveries()
elif parsed.path.startswith('/api/discoveries/'):
discovery_id = parsed.path.split('/')[-1]
self._get_discovery(discovery_id)
else:
self.send_response(404)
self.end_headers()
def do_POST(self):
parsed = urlparse(self.path)
print(f"[DEBUG] do_POST: path={parsed.path}", flush=True)
if parsed.path == '/api/discover':
self._run_discovery()
elif parsed.path == '/api/deepdive':
self._run_deepdive()
elif parsed.path == '/api/match':
self._run_match()
elif parsed.path == '/api/match/search':
self._run_match_search()
elif parsed.path == '/api/match/extract':
self._run_match_extract()
elif parsed.path == '/api/match/evaluate':
self._run_match_evaluate()
else:
self.send_response(404)
self.end_headers()
def _run_discovery(self):
try:
content_length = int(self.headers['Content-Length'])
post_data = self.rfile.read(content_length)
data = json.loads(post_data.decode())
query = data.get('query', '')
if not query:
self.send_response(400)
self.send_header('Content-Type', 'application/json')
self._set_cors_headers()
self.end_headers()
self.wfile.write(json.dumps({"error": "Query is required"}).encode())
return
logger.info(f"Starting discovery for: {query[:50]}...")
pipeline = get_discovery_pipeline()
result = pipeline.discover(
capability_gap=query,
max_results=50,
use_llm_scoring=True
)
self.send_response(200)
self.send_header('Content-Type', 'application/json')
self._set_cors_headers()
self.end_headers()
self.wfile.write(json.dumps({"result": result.to_dict()}).encode())
logger.info(f"Discovery complete: {len(result.candidates)} candidates")
except Exception as e:
logger.error(f"Discovery failed: {e}")
self.send_response(500)
self.send_header('Content-Type', 'application/json')
self._set_cors_headers()
self.end_headers()
self.wfile.write(json.dumps({"error": str(e)}).encode())
def _run_deepdive(self):
try:
content_length = int(self.headers['Content-Length'])
post_data = self.rfile.read(content_length)
data = json.loads(post_data.decode())
organization = data.get('organization', '')
technology = data.get('technology', '')
gap = data.get('gap', '')
if not organization:
self.send_response(400)
self.send_header('Content-Type', 'application/json')
self._set_cors_headers()
self.end_headers()
self.wfile.write(json.dumps({"error": "Organization is required"}).encode())
return
# Validate organization is not "Unknown" or similar placeholder
if organization.lower() in ('unknown', 'n/a', 'none', ''):
self.send_response(400)
self.send_header('Content-Type', 'application/json')
self._set_cors_headers()
self.end_headers()
self.wfile.write(json.dumps({"error": "Cannot perform deep dive on unknown organization. Please select a candidate with a known company name."}).encode())
return
logger.info(f"Starting deep dive for: {organization}")
pipeline = get_deepdive_pipeline()
result = pipeline.deep_dive(
organization=organization,
technology_context=technology,
capability_gap=gap
)
self.send_response(200)
self.send_header('Content-Type', 'application/json')
self._set_cors_headers()
self.end_headers()
self.wfile.write(json.dumps({"result": result.to_dict()}).encode())
logger.info(f"Deep dive complete for: {organization}")
except Exception as e:
logger.error(f"Deep dive failed: {e}")
self.send_response(500)
self.send_header('Content-Type', 'application/json')
self._set_cors_headers()
self.end_headers()
self.wfile.write(json.dumps({"error": str(e)}).encode())
def _run_match(self):
"""Run capability-to-technology matching (new pipeline)."""
try:
content_length = int(self.headers['Content-Length'])
post_data = self.rfile.read(content_length)
data = json.loads(post_data.decode())
query = data.get('query', '')
if not query:
self.send_response(400)
self.send_header('Content-Type', 'application/json')
self._set_cors_headers()
self.end_headers()
self.wfile.write(json.dumps({"error": "Query is required"}).encode())
return
# Optional parameters
max_technologies = data.get('max_technologies', 15)
min_fit_score = data.get('min_fit_score', 25)
sources = data.get('sources', None)
logger.info(f"Starting capability match for: {query[:50]}...")
pipeline = get_matcher_pipeline()
result = pipeline.match(
user_input=query,
max_technologies=max_technologies,
min_fit_score=min_fit_score,
sources=sources
)
self.send_response(200)
self.send_header('Content-Type', 'application/json')
self._set_cors_headers()
self.end_headers()
self.wfile.write(json.dumps({"result": result.to_dict()}).encode())
logger.info(f"Match complete: {len(result.technologies)} technologies found")
except Exception as e:
logger.error(f"Match failed: {e}")
import traceback
traceback.print_exc()
self.send_response(500)
self.send_header('Content-Type', 'application/json')
self._set_cors_headers()
self.end_headers()
self.wfile.write(json.dumps({"error": str(e)}).encode())
def _run_match_search(self):
"""Step 1: Parse capability and search sources."""
print("[DEBUG] _run_match_search: Handler started", flush=True)
logger.info("_run_match_search: Handler started")
try:
content_length = int(self.headers['Content-Length'])
print(f"[DEBUG] _run_match_search: Reading {content_length} bytes", flush=True)
logger.info(f"_run_match_search: Reading {content_length} bytes")
post_data = self.rfile.read(content_length)
print("[DEBUG] _run_match_search: Parsing JSON", flush=True)
logger.info(f"_run_match_search: Parsing JSON")
data = json.loads(post_data.decode())
print(f"[DEBUG] _run_match_search: Got data: {list(data.keys())}", flush=True)
logger.info(f"_run_match_search: Got data: {list(data.keys())}")
query = data.get('query', '')
if not query:
self.send_response(400)
self.send_header('Content-Type', 'application/json')
self._set_cors_headers()
self.end_headers()
self.wfile.write(json.dumps({"error": "Query is required"}).encode())
return
sources = data.get('sources', None)
logger.info(f"Step 1 - Search: {query[:50]}...")
logger.info("Getting matcher pipeline...")
pipeline = get_matcher_pipeline()
logger.info("Got pipeline, calling step_search...")
try:
result = pipeline.step_search(
user_input=query,
sources=sources
)
logger.info(f"step_search returned: success={result.success}")
except Exception as e:
logger.error(f"step_search raised exception: {e}")
import traceback
traceback.print_exc()
raise
self.send_response(200)
self.send_header('Content-Type', 'application/json')
self._set_cors_headers()
self.end_headers()
self.wfile.write(json.dumps({"result": result.to_dict()}).encode())
logger.info(f"Step 1 complete: {len(result.search_results)} results found")
except Exception as e:
logger.error(f"Match search failed: {e}")
import traceback
traceback.print_exc()
self.send_response(500)
self.send_header('Content-Type', 'application/json')
self._set_cors_headers()
self.end_headers()
self.wfile.write(json.dumps({"error": str(e)}).encode())
def _run_match_extract(self):
"""Step 2: Extract technologies from search results."""
try:
content_length = int(self.headers['Content-Length'])
post_data = self.rfile.read(content_length)
data = json.loads(post_data.decode())
search_id = data.get('search_id', '')
if not search_id:
self.send_response(400)
self.send_header('Content-Type', 'application/json')
self._set_cors_headers()
self.end_headers()
self.wfile.write(json.dumps({"error": "search_id is required"}).encode())
return
logger.info(f"Step 2 - Extract: search_id={search_id}")
pipeline = get_matcher_pipeline()
result = pipeline.step_extract(search_id=search_id)
self.send_response(200)
self.send_header('Content-Type', 'application/json')
self._set_cors_headers()
self.end_headers()
self.wfile.write(json.dumps({"result": result.to_dict()}).encode())
logger.info(f"Step 2 complete: {len(result.technologies)} technologies extracted")
except Exception as e:
logger.error(f"Match extract failed: {e}")
import traceback
traceback.print_exc()
self.send_response(500)
self.send_header('Content-Type', 'application/json')
self._set_cors_headers()
self.end_headers()
self.wfile.write(json.dumps({"error": str(e)}).encode())
def _run_match_evaluate(self):
"""Step 3: Evaluate selected technologies."""
try:
content_length = int(self.headers['Content-Length'])
post_data = self.rfile.read(content_length)
data = json.loads(post_data.decode())
extraction_id = data.get('extraction_id', '')
technology_ids = data.get('technology_ids', [])
if not extraction_id:
self.send_response(400)
self.send_header('Content-Type', 'application/json')
self._set_cors_headers()
self.end_headers()
self.wfile.write(json.dumps({"error": "extraction_id is required"}).encode())
return
if not technology_ids:
self.send_response(400)
self.send_header('Content-Type', 'application/json')
self._set_cors_headers()
self.end_headers()
self.wfile.write(json.dumps({"error": "technology_ids is required"}).encode())
return
logger.info(f"Step 3 - Evaluate: {len(technology_ids)} technologies")
pipeline = get_matcher_pipeline()
result = pipeline.step_evaluate(
extraction_id=extraction_id,
technology_ids=technology_ids
)
self.send_response(200)
self.send_header('Content-Type', 'application/json')
self._set_cors_headers()
self.end_headers()
self.wfile.write(json.dumps({"result": result.to_dict()}).encode())
logger.info(f"Step 3 complete: {len(result.technologies)} technologies evaluated")
except Exception as e:
logger.error(f"Match evaluate failed: {e}")
import traceback
traceback.print_exc()
self.send_response(500)
self.send_header('Content-Type', 'application/json')
self._set_cors_headers()
self.end_headers()
self.wfile.write(json.dumps({"error": str(e)}).encode())
def _list_discoveries(self):
import os
from pathlib import Path
analyses_dir = Path(__file__).parent / "analyses"
try:
discoveries = []
if analyses_dir.exists():
for f in sorted(analyses_dir.glob("discovery_*.json"), reverse=True):
with open(f) as fp:
data = json.load(fp)
discoveries.append({
"id": data.get("id"),
"capability_gap": data.get("capability_gap"),
"timestamp": data.get("timestamp"),
"candidate_count": len(data.get("candidates", [])),
"top_score": data["candidates"][0]["score"] if data.get("candidates") else 0
})
self.send_response(200)
self.send_header('Content-Type', 'application/json')
self._set_cors_headers()
self.end_headers()
self.wfile.write(json.dumps({"discoveries": discoveries}).encode())
except Exception as e:
self.send_response(500)
self.send_header('Content-Type', 'application/json')
self._set_cors_headers()
self.end_headers()
self.wfile.write(json.dumps({"error": str(e)}).encode())
def _get_discovery(self, discovery_id):
from pathlib import Path
analyses_dir = Path(__file__).parent / "analyses"
file_path = analyses_dir / f"discovery_{discovery_id}.json"
try:
if not file_path.exists():
self.send_response(404)
self.send_header('Content-Type', 'application/json')
self._set_cors_headers()
self.end_headers()
self.wfile.write(json.dumps({"error": "Not found"}).encode())
return
with open(file_path) as f:
data = json.load(f)
self.send_response(200)
self.send_header('Content-Type', 'application/json')
self._set_cors_headers()
self.end_headers()
self.wfile.write(json.dumps(data).encode())
except Exception as e:
self.send_response(500)
self.send_header('Content-Type', 'application/json')
self._set_cors_headers()
self.end_headers()
self.wfile.write(json.dumps({"error": str(e)}).encode())
def log_message(self, format, *args):
logger.info("%s - %s" % (self.client_address[0], format % args))
def run_server(port=8000):
server = HTTPServer(('localhost', port), TechScoutHandler)
logger.info(f"TechScout API Server running on http://localhost:{port}")
logger.info("Endpoints:")
logger.info(" POST /api/discover - Run technology discovery (legacy)")
logger.info(" POST /api/deepdive - Run deep dive analysis")
logger.info("")
logger.info(" Step-based Capability Matching (Guided Workflow):")
logger.info(" POST /api/match/search - Step 1: Parse & Search")
logger.info(" POST /api/match/extract - Step 2: Extract Technologies")
logger.info(" POST /api/match/evaluate - Step 3: Evaluate Selected")
logger.info("")
logger.info(" POST /api/match - Run full matching pipeline (may timeout)")
logger.info(" GET /api/discoveries - List saved discoveries")
logger.info(" GET /api/discoveries/<id> - Get specific discovery")
server.serve_forever()
if __name__ == '__main__':
run_server(port=8000)

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# See https://help.github.com/articles/ignoring-files/ for more about ignoring files.
# dependencies
/node_modules
/.pnp
.pnp.*
.yarn/*
!.yarn/patches
!.yarn/plugins
!.yarn/releases
!.yarn/versions
# testing
/coverage
# next.js
/.next/
/out/
# production
/build
# misc
.DS_Store
*.pem
# debug
npm-debug.log*
yarn-debug.log*
yarn-error.log*
.pnpm-debug.log*
# env files (can opt-in for committing if needed)
.env*
# vercel
.vercel
# typescript
*.tsbuildinfo
next-env.d.ts

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This is a [Next.js](https://nextjs.org) project bootstrapped with [`create-next-app`](https://nextjs.org/docs/app/api-reference/cli/create-next-app).
## Getting Started
First, run the development server:
```bash
npm run dev
# or
yarn dev
# or
pnpm dev
# or
bun dev
```
Open [http://localhost:3000](http://localhost:3000) with your browser to see the result.
You can start editing the page by modifying `app/page.tsx`. The page auto-updates as you edit the file.
This project uses [`next/font`](https://nextjs.org/docs/app/building-your-application/optimizing/fonts) to automatically optimize and load [Geist](https://vercel.com/font), a new font family for Vercel.
## Learn More
To learn more about Next.js, take a look at the following resources:
- [Next.js Documentation](https://nextjs.org/docs) - learn about Next.js features and API.
- [Learn Next.js](https://nextjs.org/learn) - an interactive Next.js tutorial.
You can check out [the Next.js GitHub repository](https://github.com/vercel/next.js) - your feedback and contributions are welcome!
## Deploy on Vercel
The easiest way to deploy your Next.js app is to use the [Vercel Platform](https://vercel.com/new?utm_medium=default-template&filter=next.js&utm_source=create-next-app&utm_campaign=create-next-app-readme) from the creators of Next.js.
Check out our [Next.js deployment documentation](https://nextjs.org/docs/app/building-your-application/deploying) for more details.

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import { NextRequest, NextResponse } from 'next/server';
import { spawn } from 'child_process';
import * as path from 'path';
import * as fs from 'fs';
export async function POST(request: NextRequest) {
const { query } = await request.json();
if (!query) {
return NextResponse.json({ error: 'Query is required' }, { status: 400 });
}
// Create a streaming response
const encoder = new TextEncoder();
const stream = new ReadableStream({
async start(controller) {
// Send initial status
controller.enqueue(encoder.encode(JSON.stringify({ status: 'Starting discovery...' }) + '\n'));
// Path to Python script
const techscoutDir = path.resolve(process.cwd(), '..');
const pythonScript = path.join(techscoutDir, 'run_discovery.py');
// Create a temporary Python script to run the discovery
const scriptContent = `
import sys
import json
sys.path.insert(0, r'${techscoutDir}')
from techscout.pipeline.discovery import DiscoveryPipeline
def main():
pipeline = DiscoveryPipeline(model='llama3:8b')
# Status updates via print
print(json.dumps({"status": "Decomposing capability gap..."}), flush=True)
result = pipeline.discover(
capability_gap="""${query.replace(/"/g, '\\"')}""",
max_results=50,
use_llm_scoring=True
)
# Output result
print(json.dumps({"result": result.to_dict()}), flush=True)
if __name__ == '__main__':
main()
`;
// Write temporary script
fs.writeFileSync(pythonScript, scriptContent);
try {
const python = spawn('python', [pythonScript], {
cwd: techscoutDir,
env: { ...process.env, PYTHONUNBUFFERED: '1' }
});
python.stdout.on('data', (data: Buffer) => {
const lines = data.toString().split('\n').filter(Boolean);
for (const line of lines) {
try {
// Try to parse as JSON
JSON.parse(line);
controller.enqueue(encoder.encode(line + '\n'));
} catch {
// Not JSON, send as status
controller.enqueue(encoder.encode(JSON.stringify({ status: line }) + '\n'));
}
}
});
python.stderr.on('data', (data: Buffer) => {
const message = data.toString();
// Check if it's a progress message (from logging)
if (message.includes('INFO')) {
const match = message.match(/- (.+)$/);
if (match) {
controller.enqueue(encoder.encode(JSON.stringify({ status: match[1] }) + '\n'));
}
}
});
await new Promise<void>((resolve, reject) => {
python.on('close', (code: number) => {
if (code === 0) {
resolve();
} else {
reject(new Error(`Python process exited with code ${code}`));
}
});
python.on('error', reject);
});
} catch (error) {
controller.enqueue(encoder.encode(JSON.stringify({
error: `Discovery failed: ${error}`
}) + '\n'));
} finally {
// Cleanup
try {
fs.unlinkSync(pythonScript);
} catch {
// Ignore cleanup errors
}
controller.close();
}
}
});
return new Response(stream, {
headers: {
'Content-Type': 'text/event-stream',
'Cache-Control': 'no-cache',
'Connection': 'keep-alive',
},
});
}

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import { NextRequest, NextResponse } from 'next/server';
import * as fs from 'fs';
import * as path from 'path';
export async function GET(
request: NextRequest,
{ params }: { params: Promise<{ id: string }> }
) {
const { id } = await params;
const analysesDir = path.resolve(process.cwd(), '..', 'analyses');
const filePath = path.join(analysesDir, `discovery_${id}.json`);
try {
if (!fs.existsSync(filePath)) {
return NextResponse.json({ error: 'Discovery not found' }, { status: 404 });
}
const content = fs.readFileSync(filePath, 'utf-8');
const data = JSON.parse(content);
return NextResponse.json(data);
} catch (error) {
console.error('Failed to load discovery:', error);
return NextResponse.json({ error: String(error) }, { status: 500 });
}
}

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import { NextResponse } from 'next/server';
import * as fs from 'fs';
import * as path from 'path';
export async function GET() {
const analysesDir = path.resolve(process.cwd(), '..', 'analyses');
try {
if (!fs.existsSync(analysesDir)) {
return NextResponse.json({ discoveries: [] });
}
const files = fs.readdirSync(analysesDir)
.filter(f => f.startsWith('discovery_') && f.endsWith('.json'))
.sort((a, b) => b.localeCompare(a)); // Newest first
const discoveries = files.map(file => {
const filePath = path.join(analysesDir, file);
const content = fs.readFileSync(filePath, 'utf-8');
const data = JSON.parse(content);
return {
id: data.id,
capability_gap: data.capability_gap,
timestamp: data.timestamp,
candidate_count: data.candidates?.length || 0,
top_score: data.candidates?.[0]?.score || 0,
};
});
return NextResponse.json({ discoveries });
} catch (error) {
console.error('Failed to list discoveries:', error);
return NextResponse.json({ discoveries: [], error: String(error) });
}
}

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'use client';
import { FiTrendingUp } from 'react-icons/fi';
import Link from 'next/link';
export default function ComparePage() {
return (
<div className="max-w-4xl mx-auto">
<div className="mb-8">
<h1 className="text-3xl font-bold mb-2">
<span className="gradient-text">Compare Candidates</span>
</h1>
<p className="text-gray-400">
Side-by-side comparison of technology candidates
</p>
</div>
<div className="bg-[#12121a] rounded-2xl border border-[#1e1e2e] p-12 text-center">
<FiTrendingUp className="mx-auto text-4xl text-gray-600 mb-4" />
<p className="text-gray-500 mb-4">No candidates selected for comparison</p>
<p className="text-gray-600 text-sm mb-6">
Run a discovery search and select multiple candidates to compare
</p>
<Link
href="/"
className="inline-block px-6 py-3 bg-gradient-to-r from-[#00d4ff] to-[#7c3aed] rounded-lg font-semibold hover:opacity-90 transition-opacity"
>
Start Discovery
</Link>
</div>
</div>
);
}

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'use client';
import Link from 'next/link';
import { usePathname } from 'next/navigation';
import { FiSearch, FiTarget, FiList, FiTrendingUp, FiDatabase, FiSettings } from 'react-icons/fi';
const navItems = [
{ href: '/', label: 'Scout', icon: FiSearch },
{ href: '/discoveries', label: 'Discoveries', icon: FiList },
{ href: '/deep-dives', label: 'Deep Dives', icon: FiTarget },
{ href: '/compare', label: 'Compare', icon: FiTrendingUp },
];
export default function Sidebar() {
const pathname = usePathname();
return (
<aside className="w-64 bg-[#12121a] border-r border-[#1e1e2e] h-screen sticky top-0 flex flex-col">
{/* Logo */}
<div className="p-6 border-b border-[#1e1e2e]">
<Link href="/" className="flex items-center gap-3">
<div className="w-10 h-10 bg-gradient-to-br from-[#00d4ff] to-[#7c3aed] rounded-lg flex items-center justify-center">
<FiDatabase className="text-white text-xl" />
</div>
<div>
<h1 className="font-bold text-lg gradient-text">TECHSCOUT</h1>
<p className="text-xs text-gray-500">Technology Discovery</p>
</div>
</Link>
</div>
{/* Navigation */}
<nav className="flex-1 p-4">
<ul className="space-y-2">
{navItems.map(item => {
const isActive = pathname === item.href;
const Icon = item.icon;
return (
<li key={item.href}>
<Link
href={item.href}
className={`flex items-center gap-3 px-4 py-3 rounded-lg transition-all ${
isActive
? 'bg-gradient-to-r from-[#00d4ff]/20 to-[#7c3aed]/20 text-[#00d4ff] border border-[#00d4ff]/30'
: 'text-gray-400 hover:text-white hover:bg-[#1e1e2e]'
}`}
>
<Icon className={isActive ? 'text-[#00d4ff]' : ''} />
<span className="font-medium">{item.label}</span>
</Link>
</li>
);
})}
</ul>
</nav>
{/* Footer */}
<div className="p-4 border-t border-[#1e1e2e]">
<div className="text-xs text-gray-500 text-center">
<p>TechScout v0.1.0</p>
<p className="mt-1">100% Local Processing</p>
</div>
</div>
</aside>
);
}

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'use client';
import { FiTarget } from 'react-icons/fi';
import Link from 'next/link';
export default function DeepDivesPage() {
return (
<div className="max-w-4xl mx-auto">
<div className="mb-8">
<h1 className="text-3xl font-bold mb-2">
<span className="gradient-text">Deep Dive Analyses</span>
</h1>
<p className="text-gray-400">
Comprehensive company and technology investigations
</p>
</div>
<div className="bg-[#12121a] rounded-2xl border border-[#1e1e2e] p-12 text-center">
<FiTarget className="mx-auto text-4xl text-gray-600 mb-4" />
<p className="text-gray-500 mb-4">No deep dives yet</p>
<p className="text-gray-600 text-sm mb-6">
Run a discovery search first, then select candidates for deep dive analysis
</p>
<Link
href="/"
className="inline-block px-6 py-3 bg-gradient-to-r from-[#00d4ff] to-[#7c3aed] rounded-lg font-semibold hover:opacity-90 transition-opacity"
>
Start Discovery
</Link>
</div>
</div>
);
}

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'use client';
import { useEffect, useState } from 'react';
import { useParams } from 'next/navigation';
import Link from 'next/link';
import { FiArrowLeft, FiExternalLink, FiDownload, FiLoader } from 'react-icons/fi';
import type { DiscoveryResult, TechnologyCandidate, DeepDiveResult } from '../../types';
export default function DiscoveryDetailPage() {
const params = useParams();
const [discovery, setDiscovery] = useState<DiscoveryResult | null>(null);
const [loading, setLoading] = useState(true);
const [selectedCandidate, setSelectedCandidate] = useState<TechnologyCandidate | null>(null);
const [deepDiveLoading, setDeepDiveLoading] = useState(false);
const [deepDiveResult, setDeepDiveResult] = useState<DeepDiveResult | null>(null);
useEffect(() => {
if (params.id) {
fetch(`/api/discoveries/${params.id}`)
.then(res => res.json())
.then(data => {
setDiscovery(data);
setLoading(false);
})
.catch(err => {
console.error('Failed to load discovery:', err);
setLoading(false);
});
}
}, [params.id]);
const handleExport = () => {
if (!discovery) return;
// Create CSV content
const headers = ['Rank', 'Title', 'Organization', 'Score', 'TRL', 'Source', 'Award Amount', 'URL'];
const rows = discovery.candidates.map((c, i) => [
i + 1,
`"${c.title.replace(/"/g, '""')}"`,
`"${c.organization.replace(/"/g, '""')}"`,
(c.score * 100).toFixed(1) + '%',
c.trl_estimate || 'N/A',
c.source_type,
c.award_amount ? `$${c.award_amount.toLocaleString()}` : 'N/A',
c.url
]);
const csv = [headers.join(','), ...rows.map(r => r.join(','))].join('\n');
// Download
const blob = new Blob([csv], { type: 'text/csv' });
const url = URL.createObjectURL(blob);
const a = document.createElement('a');
a.href = url;
a.download = `techscout_${discovery.id}_results.csv`;
a.click();
URL.revokeObjectURL(url);
};
const handleDeepDive = async (candidate: TechnologyCandidate) => {
if (!candidate || !discovery) return;
setDeepDiveLoading(true);
setDeepDiveResult(null);
try {
const response = await fetch('http://localhost:8000/api/deepdive', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({
organization: candidate.organization,
technology: candidate.description,
gap: discovery.capability_gap,
}),
});
if (!response.ok) {
const errorData = await response.json();
throw new Error(errorData.error || 'Deep dive failed');
}
const data = await response.json();
if (data.error) {
throw new Error(data.error);
}
if (data.result) {
setDeepDiveResult(data.result);
}
} catch (error) {
console.error('Deep dive failed:', error);
alert(`Deep dive failed: ${error}`);
} finally {
setDeepDiveLoading(false);
}
};
if (loading) {
return (
<div className="flex items-center justify-center h-64">
<div className="text-gray-500">Loading discovery...</div>
</div>
);
}
if (!discovery) {
return (
<div className="text-center py-12">
<p className="text-gray-500">Discovery not found</p>
<Link href="/discoveries" className="text-[#00d4ff] hover:underline mt-4 inline-block">
Back to discoveries
</Link>
</div>
);
}
return (
<div className="max-w-6xl mx-auto">
{/* Header */}
<div className="mb-6">
<Link href="/discoveries" className="text-gray-500 hover:text-white flex items-center gap-2 mb-4">
<FiArrowLeft /> Back to discoveries
</Link>
<div className="flex items-start justify-between">
<div>
<h1 className="text-2xl font-bold mb-2">{discovery.capability_gap}</h1>
<div className="flex items-center gap-4 text-sm text-gray-500">
<span>{new Date(discovery.timestamp).toLocaleString()}</span>
<span>{discovery.search_duration_seconds.toFixed(1)}s</span>
<span>{discovery.total_results_found} total results</span>
</div>
</div>
<button
onClick={handleExport}
className="flex items-center gap-2 px-4 py-2 bg-[#1e1e2e] rounded-lg hover:bg-[#2e2e3e] transition-colors"
>
<FiDownload /> Export CSV
</button>
</div>
</div>
{/* Query Analysis */}
<div className="bg-[#12121a] rounded-2xl border border-[#1e1e2e] p-6 mb-6">
<h2 className="text-lg font-semibold text-[#00d4ff] mb-3">Query Analysis</h2>
<p className="text-gray-300 mb-4">{discovery.decomposition.understanding}</p>
<div className="flex flex-wrap gap-2 mb-4">
{discovery.decomposition.technical_domains.map((domain, i) => (
<span
key={i}
className="px-3 py-1 bg-[#7c3aed]/20 text-[#7c3aed] rounded-full text-sm border border-[#7c3aed]/30"
>
{domain}
</span>
))}
</div>
<div className="grid grid-cols-2 md:grid-cols-5 gap-4 text-center">
<StatCard label="Candidates" value={discovery.candidates.length} color="#00d4ff" />
<StatCard label="SBIR" value={discovery.source_stats.sbir || 0} color="#22c55e" />
<StatCard label="Patents" value={discovery.source_stats.patents || 0} color="#f59e0b" />
<StatCard label="Contracts" value={discovery.source_stats.contracts || 0} color="#7c3aed" />
<StatCard label="Web" value={discovery.source_stats.web || 0} color="#ef4444" />
</div>
</div>
{/* Results */}
<div className="flex gap-6">
{/* Candidate List */}
<div className="flex-1">
<h2 className="text-lg font-semibold mb-4">Technology Candidates ({discovery.candidates.length})</h2>
<div className="space-y-3">
{discovery.candidates.map((candidate, i) => (
<div
key={candidate.id}
onClick={() => setSelectedCandidate(candidate)}
className={`bg-[#12121a] rounded-xl border p-4 cursor-pointer transition-all ${
selectedCandidate?.id === candidate.id
? 'border-[#00d4ff] glow-accent'
: 'border-[#1e1e2e] hover:border-[#2e2e3e]'
}`}
>
<div className="flex items-start justify-between mb-2">
<div className="flex items-center gap-2">
<span className="text-xs font-mono text-gray-500">#{i + 1}</span>
<span className={`text-xs px-2 py-0.5 rounded ${
candidate.source_type === 'sbir' ? 'bg-[#22c55e]/20 text-[#22c55e]' :
candidate.source_type === 'patent' ? 'bg-[#f59e0b]/20 text-[#f59e0b]' :
candidate.source_type === 'contract' ? 'bg-[#00d4ff]/20 text-[#00d4ff]' :
'bg-[#1e1e2e] text-gray-400'
}`}>
{candidate.source_type}
</span>
</div>
<div className="text-right">
<div className="text-sm font-semibold text-[#00d4ff]">
{(candidate.score * 100).toFixed(0)}%
</div>
{candidate.trl_estimate && (
<div className="text-xs text-gray-500">TRL {candidate.trl_estimate}</div>
)}
</div>
</div>
<h3 className="font-medium text-white mb-1">{candidate.title}</h3>
<p className="text-sm text-gray-500">{candidate.organization}</p>
{candidate.award_amount && (
<p className="text-xs text-[#22c55e] mt-1">${candidate.award_amount.toLocaleString()}</p>
)}
</div>
))}
</div>
</div>
{/* Selected Candidate Detail */}
{selectedCandidate && (
<div className="w-96 bg-[#12121a] rounded-2xl border border-[#00d4ff] p-6 sticky top-8 h-fit glow-accent">
<h3 className="text-lg font-semibold text-white mb-4">{selectedCandidate.title}</h3>
<div className="space-y-4">
<div>
<label className="text-xs text-gray-500">Organization</label>
<p className="text-white">{selectedCandidate.organization}</p>
</div>
<div>
<label className="text-xs text-gray-500">Description</label>
<p className="text-gray-300 text-sm">{selectedCandidate.description}</p>
</div>
<div className="grid grid-cols-2 gap-4">
<div>
<label className="text-xs text-gray-500">Score</label>
<p className="text-[#00d4ff] font-semibold">{(selectedCandidate.score * 100).toFixed(1)}%</p>
</div>
<div>
<label className="text-xs text-gray-500">TRL</label>
<p className="text-white">{selectedCandidate.trl_estimate || 'Unknown'}</p>
</div>
<div>
<label className="text-xs text-gray-500">Source</label>
<p className="text-white capitalize">{selectedCandidate.source_type}</p>
</div>
<div>
<label className="text-xs text-gray-500">Date</label>
<p className="text-white">{selectedCandidate.published_date || 'N/A'}</p>
</div>
</div>
{selectedCandidate.award_amount && (
<div>
<label className="text-xs text-gray-500">Award Amount</label>
<p className="text-[#22c55e] font-semibold">${selectedCandidate.award_amount.toLocaleString()}</p>
</div>
)}
<div className="flex gap-2 pt-4">
<a
href={selectedCandidate.url}
target="_blank"
rel="noopener noreferrer"
className="flex-1 py-2 bg-[#1e1e2e] text-center text-sm rounded-lg hover:bg-[#2e2e3e] transition-colors flex items-center justify-center gap-2"
>
<FiExternalLink /> View Source
</a>
<button
onClick={() => handleDeepDive(selectedCandidate)}
disabled={deepDiveLoading || selectedCandidate.organization === 'Unknown'}
title={selectedCandidate.organization === 'Unknown' ? 'Deep Dive requires a known organization' : 'Run deep dive analysis'}
className="flex-1 py-2 bg-gradient-to-r from-[#00d4ff] to-[#7c3aed] text-center text-sm rounded-lg font-semibold hover:opacity-90 transition-opacity disabled:opacity-50 disabled:cursor-not-allowed flex items-center justify-center gap-2"
>
{deepDiveLoading ? (
<>
<FiLoader className="animate-spin" />
Analyzing...
</>
) : selectedCandidate.organization === 'Unknown' ? (
'No Organization'
) : (
'Deep Dive'
)}
</button>
</div>
</div>
</div>
)}
</div>
{/* Deep Dive Modal */}
{deepDiveResult && (
<DeepDiveModal
result={deepDiveResult}
onClose={() => setDeepDiveResult(null)}
/>
)}
</div>
);
}
function StatCard({ label, value, color }: { label: string; value: number; color: string }) {
return (
<div className="bg-[#0a0a12] rounded-lg p-3">
<div className="text-2xl font-bold" style={{ color }}>{value}</div>
<div className="text-xs text-gray-500">{label}</div>
</div>
);
}
function DeepDiveModal({
result,
onClose
}: {
result: DeepDiveResult;
onClose: () => void;
}) {
return (
<div className="fixed inset-0 bg-black/80 flex items-center justify-center z-50 p-8">
<div className="bg-[#0a0a12] rounded-2xl border border-[#1e1e2e] max-w-4xl w-full max-h-[90vh] overflow-y-auto">
{/* Header */}
<div className="sticky top-0 bg-[#0a0a12] border-b border-[#1e1e2e] p-6 flex items-start justify-between">
<div>
<h2 className="text-2xl font-bold gradient-text">{result.organization}</h2>
<p className="text-gray-500 text-sm mt-1">Deep Dive Analysis</p>
</div>
<button
onClick={onClose}
className="text-gray-500 hover:text-white text-2xl"
>
&times;
</button>
</div>
<div className="p-6 space-y-6">
{/* Recommendation Banner */}
<div className={`p-4 rounded-xl border ${
result.recommendation.includes('STRONGLY RECOMMEND') ? 'bg-[#22c55e]/10 border-[#22c55e]/30 text-[#22c55e]' :
result.recommendation.includes('RECOMMEND') ? 'bg-[#00d4ff]/10 border-[#00d4ff]/30 text-[#00d4ff]' :
result.recommendation.includes('CONSIDER') ? 'bg-[#f59e0b]/10 border-[#f59e0b]/30 text-[#f59e0b]' :
'bg-[#ef4444]/10 border-[#ef4444]/30 text-[#ef4444]'
}`}>
<div className="font-semibold text-lg">{result.recommendation}</div>
</div>
{/* Assessment */}
<Section title="Assessment">
<p className="text-gray-300 whitespace-pre-wrap">{result.assessment}</p>
</Section>
{/* Company Profile */}
<Section title="Company Profile">
<div className="grid grid-cols-2 gap-4">
<InfoItem label="Headquarters" value={result.company_profile.headquarters} />
<InfoItem label="Founded" value={result.company_profile.founded} />
<InfoItem label="Employees" value={result.company_profile.employee_count} />
<InfoItem label="Website" value={result.company_profile.website} isLink />
</div>
<p className="text-gray-400 mt-4 text-sm">{result.company_profile.description}</p>
{result.company_profile.leadership.length > 0 && (
<div className="mt-4">
<label className="text-xs text-gray-500">Leadership</label>
<div className="flex flex-wrap gap-2 mt-1">
{result.company_profile.leadership.map((leader, i) => (
<span key={i} className="px-3 py-1 bg-[#1e1e2e] rounded-full text-sm">
{leader.name} - {leader.title}
</span>
))}
</div>
</div>
)}
</Section>
{/* Technology Profile */}
<Section title="Technology Profile">
<div className="grid grid-cols-2 gap-4 mb-4">
<InfoItem label="TRL Assessment" value={`TRL ${result.technology_profile.trl_assessment}`} />
<InfoItem label="Technology" value={result.technology_profile.name} />
</div>
<div className="mb-4">
<label className="text-xs text-gray-500">Technical Approach</label>
<p className="text-gray-300 text-sm mt-1">{result.technology_profile.technical_approach}</p>
</div>
<div className="mb-4">
<label className="text-xs text-gray-500">Competitive Advantage</label>
<p className="text-gray-300 text-sm mt-1">{result.technology_profile.competitive_advantage}</p>
</div>
{result.technology_profile.key_capabilities.length > 0 && (
<div className="mb-4">
<label className="text-xs text-gray-500">Key Capabilities</label>
<ul className="list-disc list-inside text-gray-300 text-sm mt-1">
{result.technology_profile.key_capabilities.map((cap, i) => (
<li key={i}>{cap}</li>
))}
</ul>
</div>
)}
{result.technology_profile.limitations.length > 0 && (
<div>
<label className="text-xs text-gray-500">Limitations</label>
<ul className="list-disc list-inside text-gray-400 text-sm mt-1">
{result.technology_profile.limitations.map((lim, i) => (
<li key={i}>{lim}</li>
))}
</ul>
</div>
)}
</Section>
{/* Contract History */}
<Section title="Contract History">
<div className="grid grid-cols-3 gap-4 mb-4">
<div className="bg-[#1e1e2e] rounded-lg p-4 text-center">
<div className="text-2xl font-bold text-[#00d4ff]">{result.contract_history.total_contracts}</div>
<div className="text-xs text-gray-500">Total Contracts</div>
</div>
<div className="bg-[#1e1e2e] rounded-lg p-4 text-center">
<div className="text-2xl font-bold text-[#22c55e]">
${(result.contract_history.total_value / 1000000).toFixed(1)}M
</div>
<div className="text-xs text-gray-500">Total Value</div>
</div>
<div className="bg-[#1e1e2e] rounded-lg p-4 text-center">
<div className="text-2xl font-bold text-[#f59e0b]">{result.contract_history.sbir_awards.length}</div>
<div className="text-xs text-gray-500">SBIR Awards</div>
</div>
</div>
{result.contract_history.primary_agencies.length > 0 && (
<div className="mb-4">
<label className="text-xs text-gray-500">Primary Agencies</label>
<div className="flex flex-wrap gap-2 mt-1">
{result.contract_history.primary_agencies.map((agency, i) => (
<span key={i} className="px-3 py-1 bg-[#7c3aed]/20 text-[#7c3aed] rounded-full text-sm">
{agency}
</span>
))}
</div>
</div>
)}
</Section>
{/* Risk Factors */}
{result.risk_factors.length > 0 && (
<Section title="Risk Factors">
<ul className="space-y-2">
{result.risk_factors.map((risk, i) => (
<li key={i} className="flex items-start gap-2 text-[#f59e0b]">
<span className="text-[#f59e0b]">!</span>
<span className="text-gray-300">{risk}</span>
</li>
))}
</ul>
</Section>
)}
{/* News Mentions */}
{result.news_mentions.length > 0 && (
<Section title="Recent News">
<div className="space-y-3">
{result.news_mentions.slice(0, 5).map((news, i) => (
<a
key={i}
href={news.url}
target="_blank"
rel="noopener noreferrer"
className="block p-3 bg-[#1e1e2e] rounded-lg hover:bg-[#2e2e3e] transition-colors"
>
<div className="font-medium text-white text-sm">{news.title}</div>
<div className="text-gray-500 text-xs mt-1">{news.date}</div>
</a>
))}
</div>
</Section>
)}
</div>
</div>
</div>
);
}
function Section({ title, children }: { title: string; children: React.ReactNode }) {
return (
<div className="bg-[#12121a] rounded-xl border border-[#1e1e2e] p-5">
<h3 className="text-lg font-semibold text-[#00d4ff] mb-4">{title}</h3>
{children}
</div>
);
}
function InfoItem({ label, value, isLink }: { label: string; value: unknown; isLink?: boolean }) {
if (!value) return null;
// Convert value to string, handling objects
let displayValue: string;
if (typeof value === 'object' && value !== null) {
const obj = value as Record<string, unknown>;
// Handle common object shapes like {location: "..."}
if (obj.location) {
displayValue = String(obj.location);
} else if (obj.name) {
displayValue = String(obj.name);
} else if (obj.value) {
displayValue = String(obj.value);
} else {
// Fallback to JSON for unknown object shapes
displayValue = JSON.stringify(value);
}
} else {
displayValue = String(value);
}
return (
<div>
<label className="text-xs text-gray-500">{label}</label>
{isLink ? (
<a href={displayValue} target="_blank" rel="noopener noreferrer" className="block text-[#00d4ff] hover:underline text-sm">
{displayValue}
</a>
) : (
<p className="text-white text-sm">{displayValue}</p>
)}
</div>
);
}

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'use client';
import { useEffect, useState } from 'react';
import Link from 'next/link';
import { FiClock, FiTarget, FiTrendingUp } from 'react-icons/fi';
import type { DiscoverySummary } from '../types';
export default function DiscoveriesPage() {
const [discoveries, setDiscoveries] = useState<DiscoverySummary[]>([]);
const [loading, setLoading] = useState(true);
useEffect(() => {
fetch('/api/discoveries')
.then(res => res.json())
.then(data => {
setDiscoveries(data.discoveries || []);
setLoading(false);
})
.catch(err => {
console.error('Failed to load discoveries:', err);
setLoading(false);
});
}, []);
if (loading) {
return (
<div className="flex items-center justify-center h-64">
<div className="text-gray-500">Loading discoveries...</div>
</div>
);
}
return (
<div className="max-w-4xl mx-auto">
<div className="mb-8">
<h1 className="text-3xl font-bold mb-2">
<span className="gradient-text">Discovery History</span>
</h1>
<p className="text-gray-400">
Previous technology scouting searches
</p>
</div>
{discoveries.length === 0 ? (
<div className="bg-[#12121a] rounded-2xl border border-[#1e1e2e] p-12 text-center">
<FiTarget className="mx-auto text-4xl text-gray-600 mb-4" />
<p className="text-gray-500 mb-4">No discoveries yet</p>
<Link
href="/"
className="inline-block px-6 py-3 bg-gradient-to-r from-[#00d4ff] to-[#7c3aed] rounded-lg font-semibold hover:opacity-90 transition-opacity"
>
Start Your First Search
</Link>
</div>
) : (
<div className="space-y-4">
{discoveries.map(discovery => (
<Link
key={discovery.id}
href={`/discoveries/${discovery.id}`}
className="block bg-[#12121a] rounded-xl border border-[#1e1e2e] p-6 hover:border-[#00d4ff]/50 transition-all group"
>
<div className="flex items-start justify-between mb-3">
<div className="flex items-center gap-2 text-sm text-gray-500">
<FiClock />
{new Date(discovery.timestamp).toLocaleString()}
</div>
<div className="flex items-center gap-4">
<div className="text-right">
<div className="text-sm text-gray-500">Candidates</div>
<div className="text-lg font-semibold text-[#00d4ff]">
{discovery.candidate_count}
</div>
</div>
<div className="text-right">
<div className="text-sm text-gray-500">Top Score</div>
<div className="text-lg font-semibold text-[#22c55e]">
{(discovery.top_score * 100).toFixed(0)}%
</div>
</div>
</div>
</div>
<h2 className="text-lg font-medium text-white group-hover:text-[#00d4ff] transition-colors">
{discovery.capability_gap}
</h2>
<div className="mt-4 flex items-center gap-2 text-sm text-gray-500 group-hover:text-[#00d4ff] transition-colors">
<FiTrendingUp />
View Results
</div>
</Link>
))}
</div>
)}
</div>
);
}

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@import "tailwindcss";
:root {
--background: #0a0a12;
--foreground: #ededed;
--accent: #00d4ff;
--accent-secondary: #7c3aed;
--accent-dim: #0099cc;
--card-bg: #12121a;
--card-border: #1e1e2e;
--success: #22c55e;
--warning: #f59e0b;
--danger: #ef4444;
}
@theme inline {
--color-background: var(--background);
--color-foreground: var(--foreground);
--font-sans: var(--font-geist-sans);
--font-mono: var(--font-geist-mono);
}
body {
background: var(--background);
color: var(--foreground);
font-family: Arial, Helvetica, sans-serif;
}
/* Custom scrollbar */
::-webkit-scrollbar {
width: 8px;
height: 8px;
}
::-webkit-scrollbar-track {
background: var(--card-bg);
}
::-webkit-scrollbar-thumb {
background: var(--card-border);
border-radius: 4px;
}
::-webkit-scrollbar-thumb:hover {
background: #2a2a3a;
}
/* Animations */
@keyframes fadeIn {
from {
opacity: 0;
transform: translateY(-10px);
}
to {
opacity: 1;
transform: translateY(0);
}
}
@keyframes pulse-glow {
0%, 100% {
box-shadow: 0 0 5px rgba(0, 212, 255, 0.3);
}
50% {
box-shadow: 0 0 20px rgba(0, 212, 255, 0.6);
}
}
@keyframes spin {
from {
transform: rotate(0deg);
}
to {
transform: rotate(360deg);
}
}
.animate-fadeIn {
animation: fadeIn 0.3s ease-out forwards;
}
.animate-pulse-glow {
animation: pulse-glow 2s ease-in-out infinite;
}
.animate-spin {
animation: spin 1s linear infinite;
}
/* Gradient text */
.gradient-text {
background: linear-gradient(135deg, var(--accent), var(--accent-secondary));
-webkit-background-clip: text;
-webkit-text-fill-color: transparent;
background-clip: text;
}
/* Glow effects */
.glow-accent {
box-shadow: 0 0 20px rgba(0, 212, 255, 0.3);
}
.glow-purple {
box-shadow: 0 0 20px rgba(124, 58, 237, 0.3);
}

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import type { Metadata } from "next";
import { Geist, Geist_Mono } from "next/font/google";
import "./globals.css";
import Sidebar from "./components/Sidebar";
const geistSans = Geist({
variable: "--font-geist-sans",
subsets: ["latin"],
});
const geistMono = Geist_Mono({
variable: "--font-geist-mono",
subsets: ["latin"],
});
export const metadata: Metadata = {
title: "TechScout - Technology Scouting Dashboard",
description: "Requirements-driven technology discovery and capability gap analysis",
};
export default function RootLayout({
children,
}: Readonly<{
children: React.ReactNode;
}>) {
return (
<html lang="en">
<body
className={`${geistSans.variable} ${geistMono.variable} antialiased`}
>
<div className="flex min-h-screen">
<Sidebar />
<main className="flex-1 p-8 overflow-auto">
{children}
</main>
</div>
</body>
</html>
);
}

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// TypeScript types for TechScout dashboard
// ===== Legacy Types (for /api/discover) =====
export interface TechnologyCandidate {
id: string;
title: string;
organization: string;
description: string;
source_type: string;
source: string;
url: string;
score: number;
relevance_score: number;
trl_estimate: number | null;
award_amount: number | null;
published_date: string | null;
award_id: string | null;
patent_number: string | null;
}
export interface Decomposition {
original_query: string;
understanding: string;
technical_domains: string[];
search_queries: string[];
sbir_queries: string[];
patent_queries: string[];
news_queries: string[];
keywords: string[];
exclusions: string[];
target_trl_range: [number, number];
}
export interface DiscoveryResult {
id: string;
capability_gap: string;
timestamp: string;
decomposition: Decomposition;
candidates: TechnologyCandidate[];
source_stats: Record<string, number>;
total_results_found: number;
search_duration_seconds: number;
success: boolean;
error?: string;
}
// ===== New Types (for /api/match - Capability-Technology Matching) =====
export interface CapabilityNeed {
functional_need: string;
domain: string;
implied_constraints: string[];
technology_types_sought: string[];
}
export interface CapabilityCriterion {
criterion: string;
weight: 'must_have' | 'should_have' | 'nice_to_have';
keywords: string[];
}
export interface SourceEvidence {
source_type: string;
source_name: string;
title: string;
url: string;
snippet: string;
contribution: string;
published_date: string | null;
award_amount: number | null;
award_id: string | null;
patent_number: string | null;
}
export interface Developer {
name: string;
type: 'company' | 'university' | 'government' | 'lab' | 'unknown';
location: string | null;
}
export interface CriterionResult {
criterion: string;
weight: string;
assessment: 'SUPPORTS' | 'PARTIAL' | 'DOES_NOT_SUPPORT' | 'UNKNOWN';
evidence: string;
source: string | null;
}
export interface CapabilityMatch {
overall_fit: 'HIGH' | 'MEDIUM' | 'LOW' | 'UNCERTAIN';
fit_score: number;
criteria_results: CriterionResult[];
how_it_addresses_need: string;
key_strengths: string[];
key_limitations: string[];
key_unknowns: string[];
investigation_worthy: boolean;
investigation_rationale: string;
}
export interface Technology {
id: string;
canonical_name: string;
alternate_names: string[];
technology_type: string;
description: string;
capabilities: string[];
mechanism: string | null;
developers: Developer[];
trl_estimate: number | null;
trl_confidence: number;
trl_evidence: string[];
sources: SourceEvidence[];
source_count: number;
grouping_confidence: number;
grouped_from: string[];
// Added during evaluation
capability_match: CapabilityMatch;
}
export interface MatchSummary {
total_technologies: number;
high_fit_count: number;
medium_fit_count: number;
low_fit_count: number;
uncertain_count: number;
top_recommendation: string | null;
}
export interface SearchMetadata {
total_documents_searched: number;
technologies_extracted: number;
technologies_after_grouping: number;
sources_used: string[];
processing_time_seconds: number;
}
export interface CapabilityMatchResult {
id: string;
timestamp: string;
user_input: string;
capability_need: CapabilityNeed | null;
capability_criteria: CapabilityCriterion[];
technologies: Technology[];
summary: MatchSummary;
metadata: SearchMetadata;
success: boolean;
error: string | null;
}
export interface CompanyProfile {
name: string;
description: string;
website: string | null;
headquarters: string | null;
employee_count: string | null;
founded: string | null;
leadership: Array<{ name: string; title: string }>;
}
export interface TechnologyProfile {
name: string;
description: string;
technical_approach: string;
trl_assessment: number;
key_capabilities: string[];
limitations: string[];
competitive_advantage: string;
related_patents: Array<{ title: string; number: string; url: string }>;
}
export interface ContractHistory {
total_contracts: number;
total_value: number;
primary_agencies: string[];
recent_contracts: Array<{
title: string;
amount: number | null;
date: string | null;
agency: string;
url: string;
}>;
sbir_awards: Array<{
title: string;
amount: number | null;
phase: string;
year: string | null;
url: string;
}>;
}
export interface DeepDiveResult {
id: string;
candidate_id: string;
organization: string;
timestamp: string;
company_profile: CompanyProfile;
technology_profile: TechnologyProfile;
contract_history: ContractHistory;
other_offerings: Array<{ title: string; description: string; url: string }>;
news_mentions: Array<{ title: string; snippet: string; date: string | null; url: string }>;
assessment: string;
risk_factors: string[];
recommendation: string;
duration_seconds: number;
success: boolean;
error?: string;
}
export interface DiscoverySummary {
id: string;
capability_gap: string;
timestamp: string;
candidate_count: number;
top_score: number;
}
export interface DeepDiveSummary {
id: string;
organization: string;
timestamp: string;
recommendation: string;
}
// ===== Step-Based Workflow Types (for guided capability matching) =====
export interface SearchResultItem {
id: string;
title: string;
snippet: string;
url: string;
source_type: string;
source_name: string;
organization: string | null;
published_date: string | null;
award_amount: number | null;
trl_estimate: number | null;
}
export interface StepSearchResult {
id: string;
timestamp: string;
user_input: string;
capability_need: CapabilityNeed | null;
capability_criteria: CapabilityCriterion[];
parsed_capability: unknown;
search_results: SearchResultItem[];
source_counts: Record<string, number>;
processing_time_seconds: number;
success: boolean;
error: string | null;
guidance_message: string;
}
export interface TechnologyItem {
id: string;
canonical_name: string;
technology_type: string;
description: string;
capabilities: string[];
developers: Developer[];
trl_estimate: number | null;
source_count: number;
sources: SourceEvidence[];
}
export interface StepExtractionResult {
id: string;
timestamp: string;
search_id: string;
technologies: TechnologyItem[];
raw_extractions: number;
after_grouping: number;
processing_time_seconds: number;
success: boolean;
error: string | null;
guidance_message: string;
}
export interface StepEvaluationResult {
id: string;
timestamp: string;
search_id: string;
technologies: Technology[];
summary: MatchSummary;
processing_time_seconds: number;
success: boolean;
error: string | null;
guidance_message: string;
}
// Workflow step type
export type WorkflowStep = 'input' | 'search' | 'extract' | 'evaluate';

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import { defineConfig, globalIgnores } from "eslint/config";
import nextVitals from "eslint-config-next/core-web-vitals";
import nextTs from "eslint-config-next/typescript";
const eslintConfig = defineConfig([
...nextVitals,
...nextTs,
// Override default ignores of eslint-config-next.
globalIgnores([
// Default ignores of eslint-config-next:
".next/**",
"out/**",
"build/**",
"next-env.d.ts",
]),
]);
export default eslintConfig;

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import type { NextConfig } from "next";
const nextConfig: NextConfig = {
/* config options here */
};
export default nextConfig;

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{
"name": "dashboard",
"version": "0.1.0",
"private": true,
"scripts": {
"dev": "next dev",
"build": "next build",
"start": "next start",
"lint": "eslint"
},
"dependencies": {
"next": "16.1.1",
"react": "19.2.3",
"react-dom": "19.2.3",
"react-icons": "^5.5.0",
"vis-network": "^10.0.2"
},
"devDependencies": {
"@tailwindcss/postcss": "^4",
"@types/node": "^20",
"@types/react": "^19",
"@types/react-dom": "^19",
"eslint": "^9",
"eslint-config-next": "16.1.1",
"tailwindcss": "^4",
"typescript": "^5"
}
}

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const config = {
plugins: {
"@tailwindcss/postcss": {},
},
};
export default config;

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{
"compilerOptions": {
"target": "ES2017",
"lib": ["dom", "dom.iterable", "esnext"],
"allowJs": true,
"skipLibCheck": true,
"strict": true,
"noEmit": true,
"esModuleInterop": true,
"module": "esnext",
"moduleResolution": "bundler",
"resolveJsonModule": true,
"isolatedModules": true,
"jsx": "react-jsx",
"incremental": true,
"plugins": [
{
"name": "next"
}
],
"paths": {
"@/*": ["./*"]
}
},
"include": [
"next-env.d.ts",
"**/*.ts",
"**/*.tsx",
".next/types/**/*.ts",
".next/dev/types/**/*.ts",
"**/*.mts"
],
"exclude": ["node_modules"]
}

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@echo off
echo ============================================================
echo TECHSCOUT - Installation Script
echo Technology Scouting ^& Capability Gap Analysis
echo ============================================================
echo.
:: Check for Python
echo [1/4] Checking Python installation...
python --version >nul 2>&1
if errorlevel 1 (
echo ERROR: Python is not installed or not in PATH
echo Please install Python from https://www.python.org/downloads/
echo Make sure to check "Add Python to PATH" during installation
pause
exit /b 1
)
echo Python found!
:: Check for Node.js
echo [2/4] Checking Node.js installation...
node --version >nul 2>&1
if errorlevel 1 (
echo ERROR: Node.js is not installed or not in PATH
echo Please install Node.js from https://nodejs.org/
pause
exit /b 1
)
echo Node.js found!
:: Check for Ollama
echo [3/4] Checking Ollama installation...
ollama --version >nul 2>&1
if errorlevel 1 (
echo WARNING: Ollama not found in PATH
echo TechScout requires Ollama for AI analysis.
echo Please install from https://ollama.ai
echo After installing, run: ollama pull mistral-nemo:12b
echo.
echo Press any key to continue anyway...
pause >nul
) else (
echo Ollama found!
)
:: Install Python dependencies
echo [4/4] Installing dependencies...
echo.
echo Installing Python packages...
pip install -r "%~dp0requirements.txt" --quiet
if errorlevel 1 (
echo ERROR: Failed to install Python dependencies
pause
exit /b 1
)
echo Installing Dashboard packages...
cd "%~dp0dashboard"
call npm install --silent
if errorlevel 1 (
echo ERROR: Failed to install Dashboard dependencies
pause
exit /b 1
)
cd "%~dp0"
echo.
echo ============================================================
echo Installation Complete!
echo ============================================================
echo.
echo To start TechScout, double-click: launch.bat
echo.
echo Make sure Ollama is running with mistral-nemo:12b model:
echo 1. Open a terminal and run: ollama serve
echo 2. In another terminal run: ollama pull mistral-nemo:12b
echo.
pause

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@echo off
title TechScout Launcher
echo ============================================================
echo TECHSCOUT - Technology Scouting Dashboard
echo ============================================================
echo.
:: Check if Ollama is running
echo [1/3] Checking Ollama status...
curl -s http://localhost:11434/api/tags >nul 2>&1
if errorlevel 1 (
echo.
echo Ollama is not running. Starting it now...
start "Ollama Server" cmd /c "ollama serve"
echo Waiting for Ollama to start...
timeout /t 5 /nobreak >nul
:: Check again
curl -s http://localhost:11434/api/tags >nul 2>&1
if errorlevel 1 (
echo.
echo ERROR: Could not start Ollama automatically.
echo Please install Ollama from https://ollama.ai
echo Then run this launcher again.
pause
exit /b 1
)
)
echo Ollama is running!
:: Start API server
echo.
echo [2/3] Starting API server...
start "TechScout API Server" cmd /k "cd /d %~dp0 && python api_server.py"
:: Wait for API server to start
timeout /t 3 /nobreak >nul
:: Verify API server
curl -s http://localhost:8000/api/health >nul 2>&1
if errorlevel 1 (
echo WARNING: API server may still be starting...
timeout /t 3 /nobreak >nul
)
echo API server started!
:: Start dashboard
echo.
echo [3/3] Starting dashboard...
echo.
echo ============================================================
echo TechScout is ready!
echo.
echo Dashboard: http://localhost:3001
echo API Server: http://localhost:8000
echo.
echo Your browser will open automatically.
echo.
echo TO STOP: Close all TechScout windows, or press Ctrl+C here
echo ============================================================
echo.
cd "%~dp0dashboard"
start "" http://localhost:3001
npm run dev -- -p 3001

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requirements.txt Normal file
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# TechScout Dependencies
# All free/open-source, no API keys required
# Web Search
ddgs>=6.0
# HTTP requests
requests>=2.31
aiohttp>=3.9
# Data handling
pyyaml>=6.0
# Note: Ollama must be installed separately
# Download from: https://ollama.ai
# Then run: ollama pull mistral-nemo:12b

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start_api.bat Normal file
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@echo off
echo ============================================================
echo TECHSCOUT - API Server
echo ============================================================
echo.
echo This server handles discovery and deep-dive requests.
echo Keep this window open while using TechScout.
echo.
echo Press Ctrl+C to stop the server.
echo ============================================================
echo.
cd "%~dp0"
python api_server.py

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@echo off
echo ============================================================
echo Starting Ollama AI Server
echo ============================================================
echo.
echo This window must stay open while using TechScout.
echo.
echo If this is your first time, the model will download (~4GB)
echo.
:: Check if model exists, if not pull it
ollama list | findstr "mistral-nemo:12b" >nul 2>&1
if errorlevel 1 (
echo Downloading mistral-nemo:12b model (this may take a few minutes)...
ollama pull mistral-nemo:12b
)
echo Starting Ollama server...
echo.
ollama serve

15
techscout/__init__.py Normal file
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"""
TechScout - Technology Scouting & Capability Gap Analysis Tool
A requirements-driven technology discovery platform that:
- Decomposes capability gaps into technical search queries
- Searches multiple defense/tech data sources
- Scores results on problem-solution fit
- Provides deep-dive company analysis
Two-phase approach:
- Phase 1: Discovery & Initial Scoring
- Phase 2: Deep Dive Investigation
"""
__version__ = "0.1.0"

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techscout/capability/__init__.py Normal file
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"""Capability parsing module for TechScout."""
from .parser import CapabilityParser
from .types import (
ParsedCapability,
CapabilityNeed,
CapabilityCriterion,
TechnologyIndicators,
)
__all__ = [
"CapabilityParser",
"ParsedCapability",
"CapabilityNeed",
"CapabilityCriterion",
"TechnologyIndicators",
]

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techscout/capability/parser.py Normal file
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"""
Capability Parser for TechScout.
Transforms natural language capability descriptions into structured
capability needs, evaluation criteria, and optimized search queries.
This replaces the simpler query decomposition with capability-focused parsing.
"""
import json
import logging
from typing import Optional, List
from ..extraction.llm_client import OllamaClient
from .types import (
ParsedCapability,
CapabilityNeed,
CapabilityCriterion,
TechnologyIndicators,
)
logger = logging.getLogger(__name__)
class CapabilityParser:
"""
Parses capability gap descriptions into structured capability needs.
Output is used for:
1. Optimized search queries (finding technologies, not topic discussions)
2. Technology extraction criteria (what makes something a technology)
3. Capability fit evaluation (does technology X provide capability Y)
"""
SYSTEM_PROMPT = """You are a defense technology analyst specializing in capability gap analysis.
Your task is to analyze a natural language description of a capability need and:
1. Extract the core functional requirements
2. Define criteria for evaluating whether a technology addresses the need
3. Generate search queries optimized for finding TECHNOLOGIES (not topic discussions)
4. Identify indicators that distinguish actual technologies from general content
Think about what specific, nameable technologies could solve the problem.
Focus on finding solutions, not understanding problems."""
CAPABILITY_PARSE_PROMPT = """Analyze this capability need and generate a structured capability assessment.
CAPABILITY NEED:
{query}
Respond with a JSON object containing:
{{
"understanding": "Your 2-3 sentence technical understanding of what's needed",
"technical_domains": ["relevant", "technical", "domains"],
"capability_need": {{
"functional_need": "The core capability being sought (1-2 sentences)",
"domain": "Primary domain (e.g., Space Domain Awareness, Cyber, Electronic Warfare)",
"implied_constraints": ["constraint1", "constraint2"],
"technology_types_sought": ["sensor", "algorithm", "material", "system", "platform"]
}},
"capability_criteria": [
{{
"criterion": "What the technology must do to address this need",
"weight": "must_have",
"keywords": ["terms", "indicating", "this", "criterion", "is", "met"]
}},
{{
"criterion": "Secondary capability that would be valuable",
"weight": "should_have",
"keywords": ["relevant", "keywords"]
}},
{{
"criterion": "Nice-to-have capability",
"weight": "nice_to_have",
"keywords": ["keywords"]
}}
],
"search_queries": [
"technology-focused query 1 (include terms like: prototype, system, device, demonstrated)",
"technology-focused query 2",
"technology-focused query 3",
"technology-focused query 4",
"technology-focused query 5"
],
"sbir_queries": [
"SBIR-optimized query focusing on R&D and development terms",
"Another SBIR query with different technical angle"
],
"patent_queries": [
"Patent search with technical terminology and apparatus/method/system",
"Alternative patent query"
],
"news_queries": [
"News query for recent technology announcements",
"Industry/defense news query"
],
"keywords": ["key", "technical", "terms"],
"exclusions": ["terms", "to", "exclude", "overview", "introduction", "challenge"],
"technology_indicators": {{
"positive": ["prototype", "demonstrated", "developed", "patent", "Phase II", "system", "device", "sensor", "algorithm", "tested", "deployed"],
"negative": ["overview", "challenge", "problem", "review", "survey", "introduction", "future", "potential", "could", "might"]
}},
"target_trl": {{
"min": 4,
"max": 7,
"rationale": "Why this TRL range"
}}
}}
IMPORTANT:
- Generate at least 3 capability_criteria (at least 1 must_have)
- Search queries should find TECHNOLOGIES, not topic discussions
- Include technology indicator terms (prototype, demonstrated, etc.) in queries
- Exclude vague terms (overview, challenge, potential) where possible"""
def __init__(
self,
ollama_client: Optional[OllamaClient] = None,
model: str = "mistral-nemo:12b"
):
self.client = ollama_client or OllamaClient()
self.model = model
def parse(self, query: str) -> ParsedCapability:
"""
Parse a capability gap description into structured capability needs.
Args:
query: Natural language capability gap description
Returns:
ParsedCapability with structured needs, criteria, and search queries
"""
logger.info(f"Parsing capability: {query[:100]}...")
prompt = self.CAPABILITY_PARSE_PROMPT.format(query=query)
response = self.client.generate(
prompt=prompt,
model=self.model,
system=self.SYSTEM_PROMPT,
temperature=0.2,
format="json"
)
if not response.success:
logger.error(f"LLM call failed: {response.error}")
return self._create_fallback(query, response.error)
# Parse response
try:
data = json.loads(response.content)
except json.JSONDecodeError:
data = self.client.extract_json_from_text(response.content)
if not data:
logger.error("Failed to parse LLM response as JSON")
return self._create_fallback(query, "Failed to parse LLM response")
return self._build_parsed_capability(query, data)
def _build_parsed_capability(self, query: str, data: dict) -> ParsedCapability:
"""Build ParsedCapability from LLM response data."""
# Extract capability need
cap_need_data = data.get("capability_need", {})
capability_need = CapabilityNeed(
functional_need=cap_need_data.get("functional_need", ""),
domain=cap_need_data.get("domain", ""),
implied_constraints=cap_need_data.get("implied_constraints", []),
technology_types_sought=cap_need_data.get("technology_types_sought", []),
)
# Extract capability criteria
criteria_data = data.get("capability_criteria", [])
capability_criteria = []
for c in criteria_data:
weight = c.get("weight", "should_have")
if weight not in ("must_have", "should_have", "nice_to_have"):
weight = "should_have"
capability_criteria.append(CapabilityCriterion(
criterion=c.get("criterion", ""),
weight=weight,
keywords=c.get("keywords", []),
))
# Ensure at least one must_have criterion
if not any(c.weight == "must_have" for c in capability_criteria):
if capability_criteria:
capability_criteria[0].weight = "must_have"
else:
capability_criteria.append(CapabilityCriterion(
criterion=capability_need.functional_need or "Address the capability need",
weight="must_have",
keywords=data.get("keywords", []),
))
# Extract technology indicators
indicators_data = data.get("technology_indicators", {})
technology_indicators = TechnologyIndicators(
positive=indicators_data.get("positive", [
"prototype", "demonstrated", "developed", "patent",
"Phase II", "system", "device", "sensor", "tested"
]),
negative=indicators_data.get("negative", [
"overview", "challenge", "problem", "review",
"survey", "introduction", "future", "potential"
]),
)
# Extract TRL range
trl_data = data.get("target_trl", {})
trl_range = (
trl_data.get("min", 4),
trl_data.get("max", 7)
)
return ParsedCapability(
original_query=query,
understanding=data.get("understanding", ""),
technical_domains=data.get("technical_domains", []),
search_queries=data.get("search_queries", []),
sbir_queries=data.get("sbir_queries", []),
patent_queries=data.get("patent_queries", []),
news_queries=data.get("news_queries", []),
keywords=data.get("keywords", []),
exclusions=data.get("exclusions", []),
target_trl_range=trl_range,
capability_need=capability_need,
capability_criteria=capability_criteria,
technology_indicators=technology_indicators,
success=True,
)
def _create_fallback(self, query: str, error: str) -> ParsedCapability:
"""Create a fallback ParsedCapability when LLM fails."""
# Generate basic queries from the input
words = query.split()
basic_queries = [
query,
" ".join(words[:min(5, len(words))]) + " technology",
" ".join(words[:min(5, len(words))]) + " system prototype",
]
return ParsedCapability(
original_query=query,
understanding="",
technical_domains=[],
search_queries=basic_queries,
sbir_queries=basic_queries[:2],
patent_queries=basic_queries[:2],
news_queries=basic_queries[:2],
keywords=words[:10],
exclusions=[],
capability_need=CapabilityNeed(
functional_need=query,
domain="",
implied_constraints=[],
technology_types_sought=["system", "technology"],
),
capability_criteria=[
CapabilityCriterion(
criterion="Address the stated capability need",
weight="must_have",
keywords=words[:5],
)
],
technology_indicators=TechnologyIndicators(
positive=["prototype", "demonstrated", "system", "device"],
negative=["overview", "challenge", "review"],
),
success=False,
error=error,
)

107
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"""
Type definitions for Capability Parsing.
These types represent the structured output of capability parsing,
which flows downstream to technology extraction and evaluation.
"""
from dataclasses import dataclass, field
from typing import List, Dict, Any, Optional, Literal
@dataclass
class CapabilityNeed:
"""Structured representation of what the user needs."""
functional_need: str # Core capability being sought
domain: str # Domain area (e.g., "Space Domain Awareness")
implied_constraints: List[str] # Constraints inferred from the description
technology_types_sought: List[str] # Types of tech that might help (sensor, algorithm, etc.)
def to_dict(self) -> Dict[str, Any]:
return {
"functional_need": self.functional_need,
"domain": self.domain,
"implied_constraints": self.implied_constraints,
"technology_types_sought": self.technology_types_sought,
}
@dataclass
class CapabilityCriterion:
"""A single criterion for evaluating technology fit."""
criterion: str # What the technology must do
weight: Literal["must_have", "should_have", "nice_to_have"]
keywords: List[str] # Terms that indicate this criterion is met
def to_dict(self) -> Dict[str, Any]:
return {
"criterion": self.criterion,
"weight": self.weight,
"keywords": self.keywords,
}
@dataclass
class TechnologyIndicators:
"""Indicators for identifying actual technologies in search results."""
positive: List[str] # Terms suggesting result describes a technology
negative: List[str] # Terms suggesting result is just topic discussion
def to_dict(self) -> Dict[str, Any]:
return {
"positive": self.positive,
"negative": self.negative,
}
@dataclass
class ParsedCapability:
"""
Complete result of capability parsing.
Extends the original query decomposition with structured capability
information used for technology extraction and evaluation.
"""
# Original input
original_query: str
# Understanding (from original decomposer)
understanding: str
technical_domains: List[str]
# Search queries (from original decomposer)
search_queries: List[str]
sbir_queries: List[str]
patent_queries: List[str]
news_queries: List[str]
keywords: List[str]
exclusions: List[str]
target_trl_range: tuple = (4, 7)
# NEW: Structured capability parsing
capability_need: Optional[CapabilityNeed] = None
capability_criteria: List[CapabilityCriterion] = field(default_factory=list)
technology_indicators: Optional[TechnologyIndicators] = None
# Status
success: bool = True
error: Optional[str] = None
def to_dict(self) -> Dict[str, Any]:
return {
"original_query": self.original_query,
"understanding": self.understanding,
"technical_domains": self.technical_domains,
"search_queries": self.search_queries,
"sbir_queries": self.sbir_queries,
"patent_queries": self.patent_queries,
"news_queries": self.news_queries,
"keywords": self.keywords,
"exclusions": self.exclusions,
"target_trl_range": list(self.target_trl_range),
"capability_need": self.capability_need.to_dict() if self.capability_need else None,
"capability_criteria": [c.to_dict() for c in self.capability_criteria],
"technology_indicators": self.technology_indicators.to_dict() if self.technology_indicators else None,
"success": self.success,
"error": self.error,
}

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"""
TechScout CLI
Command-line interface for technology scouting.
"""
import argparse
import logging
import sys
import json
from pathlib import Path
from .config import config
from .pipeline.discovery import DiscoveryPipeline
from .pipeline.deep_dive import DeepDivePipeline
from .extraction.llm_client import OllamaClient
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)
def cmd_discover(args):
"""Run Phase 1 discovery."""
print(f"\n{'='*60}")
print("TECHSCOUT - Phase 1: Discovery")
print(f"{'='*60}\n")
pipeline = DiscoveryPipeline(model=args.model)
print(f"Capability Gap: {args.query}\n")
print("Starting discovery process...\n")
result = pipeline.discover(
capability_gap=args.query,
max_results=args.max_results,
use_llm_scoring=not args.fast
)
if not result.success:
print(f"\nError: {result.error}")
return
# Display results
print(f"\n{'='*60}")
print("DISCOVERY RESULTS")
print(f"{'='*60}\n")
print(f"Query Understanding:")
print(f" {result.decomposition.get('understanding', 'N/A')}\n")
print(f"Technical Domains: {', '.join(result.decomposition.get('technical_domains', []))}\n")
print(f"Source Statistics:")
for source, count in result.source_stats.items():
print(f" - {source}: {count} results")
print(f"\nTotal Results: {result.total_results_found}")
print(f"Search Time: {result.search_duration_seconds:.1f}s\n")
print(f"TOP {len(result.candidates)} CANDIDATES:")
print("-" * 60)
for i, candidate in enumerate(result.candidates[:20], 1):
print(f"\n{i}. {candidate.title[:70]}")
print(f" Organization: {candidate.organization}")
print(f" Source: {candidate.source_type} | Score: {candidate.score:.2f}")
if candidate.trl_estimate:
print(f" TRL: {candidate.trl_estimate}")
if candidate.award_amount:
print(f" Award: ${candidate.award_amount:,.0f}")
print(f" URL: {candidate.url[:60]}...")
print(f"\n\nResults saved to: {config.analyses_dir / f'discovery_{result.id}.json'}")
def cmd_deepdive(args):
"""Run Phase 2 deep dive."""
print(f"\n{'='*60}")
print("TECHSCOUT - Phase 2: Deep Dive")
print(f"{'='*60}\n")
pipeline = DeepDivePipeline(model=args.model)
print(f"Organization: {args.organization}")
print(f"Technology: {args.technology}\n")
print("Starting deep dive analysis...\n")
result = pipeline.deep_dive(
organization=args.organization,
technology_context=args.technology,
capability_gap=args.gap
)
if not result.success:
print(f"\nError: {result.error}")
return
# Display results
print(f"\n{'='*60}")
print("DEEP DIVE RESULTS")
print(f"{'='*60}\n")
print("COMPANY PROFILE")
print("-" * 40)
print(f"Name: {result.company_profile.name}")
print(f"Description: {result.company_profile.description}")
print(f"Headquarters: {result.company_profile.headquarters or 'Unknown'}")
print(f"Founded: {result.company_profile.founded or 'Unknown'}")
if result.company_profile.leadership:
print("Leadership:")
for leader in result.company_profile.leadership[:3]:
print(f" - {leader.get('name', 'Unknown')}: {leader.get('title', 'Unknown')}")
print(f"\nTECHNOLOGY PROFILE")
print("-" * 40)
print(f"Technology: {result.technology_profile.name}")
print(f"TRL Assessment: {result.technology_profile.trl_assessment}")
print(f"Approach: {result.technology_profile.technical_approach[:200]}...")
print(f"Key Capabilities:")
for cap in result.technology_profile.key_capabilities[:5]:
print(f" - {cap}")
print(f"Competitive Advantage: {result.technology_profile.competitive_advantage}")
print(f"\nCONTRACT HISTORY")
print("-" * 40)
print(f"Total Contracts: {result.contract_history.total_contracts}")
print(f"Total Value: ${result.contract_history.total_value:,.0f}")
print(f"Primary Agencies: {', '.join(result.contract_history.primary_agencies[:3])}")
print(f"SBIR Awards: {len(result.contract_history.sbir_awards)}")
print(f"\nASSESSMENT")
print("-" * 40)
print(result.assessment)
print(f"\nRISK FACTORS")
for risk in result.risk_factors:
print(f" - {risk}")
print(f"\nRECOMMENDATION: {result.recommendation}")
print(f"\n\nResults saved to: {config.analyses_dir / f'deepdive_{result.id}.json'}")
def cmd_list(args):
"""List saved analyses."""
print("\nSaved Analyses:")
print("-" * 60)
discoveries = list(config.analyses_dir.glob("discovery_*.json"))
deep_dives = list(config.analyses_dir.glob("deepdive_*.json"))
if discoveries:
print("\nPhase 1 - Discovery:")
for f in sorted(discoveries, reverse=True)[:10]:
with open(f) as fp:
data = json.load(fp)
print(f" {f.stem}: {data.get('capability_gap', 'Unknown')[:50]}...")
if deep_dives:
print("\nPhase 2 - Deep Dive:")
for f in sorted(deep_dives, reverse=True)[:10]:
with open(f) as fp:
data = json.load(fp)
print(f" {f.stem}: {data.get('organization', 'Unknown')}")
if not discoveries and not deep_dives:
print(" No analyses found.")
def cmd_check(args):
"""Check system status."""
print("\nTechScout System Check")
print("-" * 40)
# Check Ollama
client = OllamaClient()
if client.is_available():
print("Ollama: RUNNING")
models = client.list_models()
print(f" Available models: {', '.join(models[:5])}")
else:
print("Ollama: NOT RUNNING")
print(" Please start Ollama: ollama serve")
# Check directories
print(f"\nDirectories:")
print(f" Analyses: {config.analyses_dir} ({'exists' if config.analyses_dir.exists() else 'MISSING'})")
print(f" Exports: {config.exports_dir} ({'exists' if config.exports_dir.exists() else 'MISSING'})")
def main():
parser = argparse.ArgumentParser(
description="TechScout - Technology Scouting & Capability Gap Analysis"
)
subparsers = parser.add_subparsers(dest="command", help="Commands")
# Discover command
discover_parser = subparsers.add_parser(
"discover",
help="Phase 1: Discover technologies for a capability gap"
)
discover_parser.add_argument(
"query",
help="Natural language capability gap description"
)
discover_parser.add_argument(
"--model", "-m",
default="mistral-nemo:12b",
help="Ollama model to use"
)
discover_parser.add_argument(
"--max-results", "-n",
type=int,
default=50,
help="Maximum candidates to return"
)
discover_parser.add_argument(
"--fast",
action="store_true",
help="Skip LLM scoring for faster results"
)
# Deep dive command
deepdive_parser = subparsers.add_parser(
"deepdive",
help="Phase 2: Deep dive into a company/technology"
)
deepdive_parser.add_argument(
"organization",
help="Company name to investigate"
)
deepdive_parser.add_argument(
"--technology", "-t",
required=True,
help="Technology context/description"
)
deepdive_parser.add_argument(
"--gap", "-g",
required=True,
help="Original capability gap"
)
deepdive_parser.add_argument(
"--model", "-m",
default="mistral-nemo:12b",
help="Ollama model to use"
)
# List command
list_parser = subparsers.add_parser(
"list",
help="List saved analyses"
)
# Check command
check_parser = subparsers.add_parser(
"check",
help="Check system status"
)
args = parser.parse_args()
if args.command == "discover":
cmd_discover(args)
elif args.command == "deepdive":
cmd_deepdive(args)
elif args.command == "list":
cmd_list(args)
elif args.command == "check":
cmd_check(args)
else:
parser.print_help()
if __name__ == "__main__":
main()

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"""
TechScout Configuration
Central configuration for all components.
"""
from dataclasses import dataclass, field
from typing import List, Optional
from pathlib import Path
@dataclass
class OllamaConfig:
"""Ollama LLM configuration."""
base_url: str = "http://localhost:11434"
fast_model: str = "llama3.2:latest" # Quick decomposition
default_model: str = "mistral-nemo:12b" # Standard analysis
quality_model: str = "qwen2.5:14b" # Deep analysis
temperature: float = 0.1
max_tokens: int = 4096
@dataclass
class SearchConfig:
"""Search configuration."""
max_results_per_source: int = 20
request_delay: float = 1.0 # Rate limiting
timeout: int = 30
# Source weights for scoring
source_weights: dict = field(default_factory=lambda: {
"sbir_sttr": 1.2, # High relevance for defense tech
"uspto": 1.1, # Patents indicate innovation
"usaspending": 1.0, # Contract history
"defense_news": 0.9, # News/press
"web_search": 0.8, # General web
"arxiv": 1.0, # Academic research
})
@dataclass
class ScoringConfig:
"""Scoring and ranking configuration."""
# Weights for different scoring factors
technical_relevance_weight: float = 0.35
trl_match_weight: float = 0.20
recency_weight: float = 0.15
source_authority_weight: float = 0.15
funding_indicator_weight: float = 0.15
# TRL preferences (target TRL range)
target_trl_min: int = 4
target_trl_max: int = 7
# Recency decay (years)
recency_half_life: float = 2.0
@dataclass
class TechScoutConfig:
"""Main TechScout configuration."""
# Paths
base_dir: Path = field(default_factory=lambda: Path(__file__).parent.parent)
personas_dir: Path = field(default_factory=lambda: Path(__file__).parent.parent / "personas")
analyses_dir: Path = field(default_factory=lambda: Path(__file__).parent.parent / "analyses")
exports_dir: Path = field(default_factory=lambda: Path(__file__).parent.parent / "exports")
# Sub-configs
ollama: OllamaConfig = field(default_factory=OllamaConfig)
search: SearchConfig = field(default_factory=SearchConfig)
scoring: ScoringConfig = field(default_factory=ScoringConfig)
# Analysis settings
max_phase1_results: int = 50
max_phase2_candidates: int = 10
def __post_init__(self):
"""Ensure directories exist."""
self.analyses_dir.mkdir(parents=True, exist_ok=True)
self.exports_dir.mkdir(parents=True, exist_ok=True)
# Global config instance
config = TechScoutConfig()

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"""Extraction module for TechScout."""
from .llm_client import OllamaClient
from .decomposer import QueryDecomposer
from .scorer import ResultScorer
from .org_extractor import OrganizationExtractor
__all__ = ["OllamaClient", "QueryDecomposer", "ResultScorer", "OrganizationExtractor"]

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"""
Query Decomposition for TechScout.
Takes a natural language capability gap description and decomposes it
into specific technical search queries optimized for different data sources.
"""
import json
import logging
from dataclasses import dataclass, field
from typing import List, Dict, Any, Optional
from .llm_client import OllamaClient
logger = logging.getLogger(__name__)
@dataclass
class DecomposedQuery:
"""Result of query decomposition."""
original_query: str
understanding: str # LLM's understanding of the problem
technical_domains: List[str] # Identified technical areas
search_queries: List[str] # General search queries
sbir_queries: List[str] # Optimized for SBIR search
patent_queries: List[str] # Optimized for patent search
news_queries: List[str] # Optimized for news search
keywords: List[str] # Key technical terms
exclusions: List[str] # Terms to exclude (reduce noise)
target_trl_range: tuple = (4, 7) # Target TRL range
success: bool = True
error: Optional[str] = None
def to_dict(self) -> Dict[str, Any]:
return {
"original_query": self.original_query,
"understanding": self.understanding,
"technical_domains": self.technical_domains,
"search_queries": self.search_queries,
"sbir_queries": self.sbir_queries,
"patent_queries": self.patent_queries,
"news_queries": self.news_queries,
"keywords": self.keywords,
"exclusions": self.exclusions,
"target_trl_range": list(self.target_trl_range),
"success": self.success,
"error": self.error
}
class QueryDecomposer:
"""
Decomposes capability gap descriptions into technical search queries.
"""
SYSTEM_PROMPT = """You are a defense technology analyst specializing in translating operational capability gaps into technical search strategies.
Your task is to take a natural language description of a capability need and:
1. Understand the underlying technical problem
2. Identify relevant technical domains and approaches
3. Generate optimized search queries for different data sources
4. Identify key technical terms and exclusions
Be specific and technical. Think about what technologies, methods, and approaches could solve the problem.
Consider both mature solutions and emerging technologies."""
DECOMPOSITION_PROMPT = """Analyze this capability gap and generate search strategies:
CAPABILITY GAP:
{query}
Respond with a JSON object containing:
{{
"understanding": "Your technical understanding of what's needed (2-3 sentences)",
"technical_domains": ["list", "of", "relevant", "technical", "domains"],
"search_queries": [
"general technical search query 1",
"general technical search query 2",
"general technical search query 3",
"general technical search query 4",
"general technical search query 5"
],
"sbir_queries": [
"SBIR-optimized query focusing on R&D terms",
"Another SBIR query with different technical angle"
],
"patent_queries": [
"Patent search query with technical terminology",
"Alternative patent query with different approach"
],
"news_queries": [
"News query for recent developments",
"Industry news angle query"
],
"keywords": ["key", "technical", "terms", "for", "filtering"],
"exclusions": ["terms", "to", "exclude", "reduce", "noise"],
"target_trl": {{
"min": 4,
"max": 7,
"rationale": "Why this TRL range is appropriate"
}}
}}
Generate diverse queries that approach the problem from different angles.
SBIR queries should use R&D terminology.
Patent queries should use precise technical language.
News queries should be more accessible.
Provide at least 5 general search queries covering different technical approaches."""
def __init__(
self,
ollama_client: Optional[OllamaClient] = None,
model: str = "mistral-nemo:12b"
):
self.client = ollama_client or OllamaClient()
self.model = model
def decompose(self, query: str) -> DecomposedQuery:
"""
Decompose a capability gap into search queries.
Args:
query: Natural language capability gap description
Returns:
DecomposedQuery with optimized search strategies
"""
prompt = self.DECOMPOSITION_PROMPT.format(query=query)
response = self.client.generate(
prompt=prompt,
model=self.model,
system=self.SYSTEM_PROMPT,
temperature=0.2,
format="json"
)
if not response.success:
return DecomposedQuery(
original_query=query,
understanding="",
technical_domains=[],
search_queries=[],
sbir_queries=[],
patent_queries=[],
news_queries=[],
keywords=[],
exclusions=[],
success=False,
error=response.error
)
# Parse response
try:
data = json.loads(response.content)
except json.JSONDecodeError:
data = self.client.extract_json_from_text(response.content)
if not data:
return DecomposedQuery(
original_query=query,
understanding="",
technical_domains=[],
search_queries=[],
sbir_queries=[],
patent_queries=[],
news_queries=[],
keywords=[],
exclusions=[],
success=False,
error="Failed to parse LLM response"
)
# Extract TRL range
trl_data = data.get("target_trl", {})
trl_range = (
trl_data.get("min", 4),
trl_data.get("max", 7)
)
return DecomposedQuery(
original_query=query,
understanding=data.get("understanding", ""),
technical_domains=data.get("technical_domains", []),
search_queries=data.get("search_queries", []),
sbir_queries=data.get("sbir_queries", []),
patent_queries=data.get("patent_queries", []),
news_queries=data.get("news_queries", []),
keywords=data.get("keywords", []),
exclusions=data.get("exclusions", []),
target_trl_range=trl_range,
success=True
)
def enhance_query(self, base_query: str, context: str) -> List[str]:
"""
Generate additional queries based on initial results context.
Args:
base_query: Original capability gap
context: Summary of initial findings
Returns:
List of refined search queries
"""
prompt = f"""Based on initial search results, generate refined queries.
ORIGINAL CAPABILITY GAP:
{base_query}
INITIAL FINDINGS:
{context}
Generate 3-5 refined search queries that:
1. Drill deeper into promising approaches found
2. Explore adjacent technical areas
3. Target specific companies or technologies identified
Respond with a JSON object:
{{
"refined_queries": ["query1", "query2", "query3"]
}}"""
response = self.client.generate(
prompt=prompt,
model=self.model,
temperature=0.3,
format="json"
)
if not response.success:
return []
try:
data = json.loads(response.content)
return data.get("refined_queries", [])
except json.JSONDecodeError:
return []

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"""
Ollama LLM Client for TechScout.
Handles all LLM interactions for query decomposition, analysis, and scoring.
"""
import json
import logging
import re
import requests
from dataclasses import dataclass
from typing import Optional, Dict, Any, List
logger = logging.getLogger(__name__)
@dataclass
class LLMResponse:
"""Response from LLM."""
content: str
success: bool
error: Optional[str] = None
model: str = ""
tokens_used: int = 0
class OllamaClient:
"""
Client for Ollama local LLM.
"""
def __init__(
self,
base_url: str = "http://localhost:11434",
default_model: str = "mistral-nemo:12b"
):
self.base_url = base_url.rstrip("/")
self.default_model = default_model
def generate(
self,
prompt: str,
model: Optional[str] = None,
system: Optional[str] = None,
temperature: float = 0.1,
max_tokens: int = 4096,
format: Optional[str] = None # "json" for JSON mode
) -> LLMResponse:
"""
Generate a response from Ollama.
Args:
prompt: The user prompt
model: Model to use (defaults to self.default_model)
system: System prompt
temperature: Sampling temperature
max_tokens: Max tokens to generate
format: Response format ("json" for JSON mode)
Returns:
LLMResponse object
"""
model = model or self.default_model
payload = {
"model": model,
"prompt": prompt,
"stream": False,
"options": {
"temperature": temperature,
"num_predict": max_tokens,
}
}
if system:
payload["system"] = system
if format == "json":
payload["format"] = "json"
try:
response = requests.post(
f"{self.base_url}/api/generate",
json=payload,
timeout=120
)
response.raise_for_status()
data = response.json()
return LLMResponse(
content=data.get("response", ""),
success=True,
model=model,
tokens_used=data.get("eval_count", 0)
)
except requests.exceptions.Timeout:
return LLMResponse(
content="",
success=False,
error="Request timed out",
model=model
)
except requests.exceptions.RequestException as e:
return LLMResponse(
content="",
success=False,
error=str(e),
model=model
)
def extract_json_from_text(self, text: str) -> Optional[Dict[str, Any]]:
"""Extract JSON from text that might contain other content."""
# Try to find JSON block
json_patterns = [
r'```json\s*([\s\S]*?)\s*```',
r'```\s*([\s\S]*?)\s*```',
r'\{[\s\S]*\}',
]
for pattern in json_patterns:
matches = re.findall(pattern, text)
for match in matches:
try:
return json.loads(match)
except json.JSONDecodeError:
continue
# Try parsing the whole text
try:
return json.loads(text)
except json.JSONDecodeError:
return None
def is_available(self) -> bool:
"""Check if Ollama is running and accessible."""
try:
response = requests.get(f"{self.base_url}/api/tags", timeout=5)
return response.status_code == 200
except Exception:
return False
def list_models(self) -> List[str]:
"""List available models."""
try:
response = requests.get(f"{self.base_url}/api/tags", timeout=10)
response.raise_for_status()
data = response.json()
return [m["name"] for m in data.get("models", [])]
except Exception as e:
logger.error(f"Failed to list models: {e}")
return []

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"""
Organization Extractor for TechScout.
Hybrid approach: Fast regex patterns first, LLM fallback for ambiguous cases.
Extracts company/organization names from unstructured text like news articles and web pages.
"""
import re
import logging
from typing import Optional, List, Tuple
from dataclasses import dataclass
logger = logging.getLogger(__name__)
@dataclass
class ExtractionResult:
"""Result of organization extraction."""
organization: Optional[str]
confidence: float # 0.0 to 1.0
method: str # "regex", "llm", or "none"
class OrganizationExtractor:
"""
Extracts organization names from text using hybrid regex + LLM approach.
Strategy:
1. Try fast regex patterns for common company suffixes
2. Try stock ticker extraction (NYSE:XXX, NASDAQ:XXX)
3. Try news source patterns (BUSINESS WIRE, PR Newswire headers)
4. Fall back to LLM for ambiguous cases
"""
def __init__(self, llm_client=None, model: str = "mistral-nemo:12b"):
self.llm_client = llm_client
self.model = model
# Common company suffixes - ordered by specificity
self.company_suffixes = [
r"Inc\.",
r"Inc",
r"LLC",
r"L\.L\.C\.",
r"Corp\.",
r"Corp",
r"Corporation",
r"Ltd\.",
r"Ltd",
r"Limited",
r"Co\.",
r"Company",
r"Technologies",
r"Technology",
r"Systems",
r"Solutions",
r"Industries",
r"Aerospace",
r"Defense",
r"Dynamics",
r"Sciences",
r"Labs",
r"Laboratories",
r"Group",
r"Holdings",
r"Partners",
r"Ventures",
r"Analytics",
r"Robotics",
r"Space",
r"Aviation",
]
# Build regex pattern for company names
suffix_pattern = "|".join(self.company_suffixes)
# Match: 1-4 capitalized words followed by a suffix
self.company_pattern = re.compile(
rf'\b([A-Z][a-zA-Z]*(?:\s+[A-Z][a-zA-Z]*)?(?:\s+[A-Z][a-zA-Z]*)?(?:\s+[A-Z][a-zA-Z]*)?)\s+({suffix_pattern})\b',
re.IGNORECASE
)
# Stock ticker patterns
self.ticker_patterns = [
re.compile(r'\((?:NYSE|NASDAQ|AMEX|OTC):\s*([A-Z]{1,5})\)', re.IGNORECASE),
re.compile(r'(?:NYSE|NASDAQ|AMEX|OTC):\s*([A-Z]{1,5})\b', re.IGNORECASE),
re.compile(r'\(([A-Z]{2,5})\)', re.IGNORECASE), # Just ticker in parens like (BKSY)
]
# News wire header patterns - company often appears right after location
self.news_wire_pattern = re.compile(
r'(?:HERNDON|SAN FRANCISCO|NEW YORK|LOS ANGELES|WASHINGTON|DENVER|SEATTLE|BOSTON|AUSTIN|ARLINGTON|'
r'BOULDER|HUNTSVILLE|COLORADO SPRINGS|MOUNTAIN VIEW|PALO ALTO|REDMOND|SAN DIEGO|PHOENIX|DALLAS|'
r'HOUSTON|CHICAGO|ATLANTA|MIAMI|TAMPA|ORLANDO|RESTON|MCLEAN|CHANTILLY|STERLING),?\s+'
r'(?:[A-Za-z\.]+,?\s*)?--?\s*' # State abbreviation
r'(?:\([^)]+\)--?\s*)?' # Optional source like (BUSINESS WIRE)
r'([A-Z][A-Za-z]+(?:\s+[A-Z][a-zA-Z]+)*(?:\s+(?:Inc|LLC|Corp|Ltd|Technologies|Technology|Systems|Solutions|'
r'Aerospace|Defense|Dynamics|Sciences|Space|Aviation)\.?)?)',
re.IGNORECASE
)
# University/research institution patterns
self.institution_patterns = [
re.compile(r'\b((?:University of [A-Z][a-z]+(?:\s+[A-Z][a-z]+)?|'
r'[A-Z][a-z]+(?:\s+[A-Z][a-z]+)?\s+University|'
r'MIT|Caltech|Stanford|Harvard|Carnegie Mellon|Georgia Tech|'
r'[A-Z][a-z]+\s+Institute of Technology))\b'),
re.compile(r'\b((?:[A-Z][a-z]+\s+)?(?:National Laboratory|Research Center|Research Institute))\b'),
]
# Known defense/space companies for quick matching
self.known_companies = {
"blacksky": "BlackSky Technology Inc.",
"spacex": "SpaceX",
"northrop": "Northrop Grumman",
"lockheed": "Lockheed Martin",
"raytheon": "Raytheon",
"boeing": "Boeing",
"general dynamics": "General Dynamics",
"bae systems": "BAE Systems",
"l3harris": "L3Harris Technologies",
"leidos": "Leidos",
"saic": "SAIC",
"maxar": "Maxar Technologies",
"planet labs": "Planet Labs",
"capella space": "Capella Space",
"rocket lab": "Rocket Lab",
"aerojet": "Aerojet Rocketdyne",
"ball aerospace": "Ball Aerospace",
"sierra nevada": "Sierra Nevada Corporation",
"relativity space": "Relativity Space",
"anduril": "Anduril Industries",
"palantir": "Palantir Technologies",
"shield ai": "Shield AI",
"heo robotics": "HEO Robotics",
"mda space": "MDA Space",
"mda": "MDA Space",
"thales": "Thales Group",
"airbus": "Airbus Defence and Space",
"blue origin": "Blue Origin",
"virgin orbit": "Virgin Orbit",
"astra": "Astra Space",
"spire global": "Spire Global",
"hawkeye 360": "HawkEye 360",
"iceye": "ICEYE",
"umbra": "Umbra Space",
"terran orbital": "Terran Orbital",
"momentus": "Momentus Space",
"ast spacemobile": "AST SpaceMobile",
"viasat": "Viasat",
"iridium": "Iridium Communications",
"ses": "SES S.A.",
"intelsat": "Intelsat",
"eutelsat": "Eutelsat",
"telesat": "Telesat",
"oneweb": "OneWeb",
"starlink": "SpaceX Starlink",
"amazon kuiper": "Amazon Kuiper",
}
def extract(self, title: str, snippet: str) -> ExtractionResult:
"""
Extract organization from title and snippet.
Args:
title: Article/result title
snippet: Description/body text
Returns:
ExtractionResult with organization name, confidence, and method
"""
combined_text = f"{title} {snippet}"
# Try methods in order of speed and confidence
# 1. Check known companies first (fastest, highest confidence)
result = self._extract_known_company(combined_text)
if result:
return ExtractionResult(organization=result, confidence=0.95, method="known")
# 2. Try stock ticker extraction (very reliable when present)
result = self._extract_from_ticker(combined_text)
if result:
return ExtractionResult(organization=result, confidence=0.9, method="ticker")
# 3. Try news wire header pattern
result = self._extract_from_news_wire(snippet)
if result:
return ExtractionResult(organization=result, confidence=0.85, method="news_wire")
# 4. Try company suffix pattern
result = self._extract_from_suffix(combined_text)
if result:
return ExtractionResult(organization=result, confidence=0.8, method="suffix")
# 5. Try institution patterns
result = self._extract_institution(combined_text)
if result:
return ExtractionResult(organization=result, confidence=0.75, method="institution")
# 6. Fall back to LLM if available
if self.llm_client:
result = self._extract_with_llm(title, snippet)
if result:
return ExtractionResult(organization=result, confidence=0.7, method="llm")
# No extraction possible
return ExtractionResult(organization=None, confidence=0.0, method="none")
def _extract_known_company(self, text: str) -> Optional[str]:
"""Check for known defense/space companies."""
text_lower = text.lower()
for keyword, full_name in self.known_companies.items():
if keyword in text_lower:
return full_name
return None
def _extract_from_ticker(self, text: str) -> Optional[str]:
"""Extract company name associated with stock ticker."""
for pattern in self.ticker_patterns:
match = pattern.search(text)
if match:
ticker = match.group(1).upper()
# Try to find the company name near the ticker
# Look for pattern like "Company Name (NYSE:XXX)" or "Company Name (XXX)"
before_ticker = text[:match.start()]
# Find company name pattern before ticker
company_match = self.company_pattern.search(before_ticker[-100:])
if company_match:
name = f"{company_match.group(1)} {company_match.group(2)}"
return self._clean_company_name(name)
# Try to get the last few capitalized words before ticker
words_before = before_ticker.strip().split()[-5:]
if words_before:
# Look for capitalized sequence
company_words = []
for word in reversed(words_before):
clean_word = word.strip('(),-:')
if clean_word and clean_word[0].isupper():
company_words.insert(0, clean_word)
elif company_words:
break
if company_words:
return " ".join(company_words)
return None
def _extract_from_news_wire(self, text: str) -> Optional[str]:
"""Extract from news wire format (CITY -- Company Name announced...)."""
match = self.news_wire_pattern.search(text)
if match:
return self._clean_company_name(match.group(1))
return None
def _extract_from_suffix(self, text: str) -> Optional[str]:
"""Extract using company suffix patterns."""
matches = list(self.company_pattern.finditer(text))
if matches:
# Prefer matches from title (earlier in combined text)
# Take the first substantial match
for match in matches:
name = f"{match.group(1)} {match.group(2)}"
cleaned = self._clean_company_name(name)
if cleaned and len(cleaned) > 3:
return cleaned
return None
def _extract_institution(self, text: str) -> Optional[str]:
"""Extract university or research institution names."""
for pattern in self.institution_patterns:
match = pattern.search(text)
if match:
return match.group(1)
return None
def _extract_with_llm(self, title: str, snippet: str) -> Optional[str]:
"""Use LLM to extract organization name as fallback."""
if not self.llm_client:
return None
prompt = f"""Extract the primary company or organization name from this text.
Title: {title}
Description: {snippet}
Rules:
- Return ONLY the organization name, nothing else
- If it's a company, include the suffix (Inc, LLC, Corp, etc.) if known
- If no clear organization can be identified, return "NONE"
- Do not make up or guess organization names
- Prefer companies over universities/agencies if both are mentioned
Organization name:"""
try:
response = self.llm_client.generate(
prompt=prompt,
model=self.model,
temperature=0.0, # Deterministic
max_tokens=50
)
if response.success:
result = response.content.strip()
# Validate result
if result and result.upper() != "NONE" and len(result) > 2:
# Basic validation - should look like a company name
if not any(x in result.lower() for x in ["i don't", "cannot", "no ", "none", "unknown"]):
return self._clean_company_name(result)
except Exception as e:
logger.warning(f"LLM extraction failed: {e}")
return None
def _clean_company_name(self, name: str) -> str:
"""Clean and normalize company name."""
if not name:
return ""
# Remove common prefixes
prefixes_to_remove = [
"the ", "a ", "an ",
"by ", "from ", "at ",
"-- ", "- ",
]
name_lower = name.lower()
for prefix in prefixes_to_remove:
if name_lower.startswith(prefix):
name = name[len(prefix):]
name_lower = name.lower()
# Remove trailing punctuation
name = name.rstrip('.,;:')
# Normalize whitespace
name = " ".join(name.split())
return name.strip()
def extract_batch(
self,
items: List[Tuple[str, str]],
use_llm_fallback: bool = True
) -> List[ExtractionResult]:
"""
Extract organizations from multiple items.
Args:
items: List of (title, snippet) tuples
use_llm_fallback: Whether to use LLM for items that regex can't handle
Returns:
List of ExtractionResult objects
"""
results = []
for title, snippet in items:
# extract() already includes LLM fallback
result = self.extract(title, snippet)
# Log extraction for debugging
if result.organization:
logger.info(f"Extracted '{result.organization}' from '{title[:50]}...' (method: {result.method})")
else:
logger.debug(f"No organization found in '{title[:50]}...'")
results.append(result)
return results

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"""
Result Scoring for TechScout.
Scores search results based on problem-solution fit using LLM analysis
and heuristic factors.
"""
import json
import logging
import math
from dataclasses import dataclass
from datetime import datetime
from typing import List, Dict, Any, Optional
from .llm_client import OllamaClient
from ..search.base import SearchResult
logger = logging.getLogger(__name__)
@dataclass
class ScoringWeights:
"""Weights for different scoring factors."""
technical_relevance: float = 0.35
trl_match: float = 0.20
recency: float = 0.15
source_authority: float = 0.15
funding_indicator: float = 0.15
class ResultScorer:
"""
Scores and ranks search results based on problem-solution fit.
"""
SOURCE_AUTHORITY = {
"sbir": 0.9,
"patent": 0.85,
"contract": 0.8,
"government": 0.85,
"academic": 0.75,
"news": 0.6,
"web": 0.5,
}
def __init__(
self,
ollama_client: Optional[OllamaClient] = None,
model: str = "mistral-nemo:12b",
weights: Optional[ScoringWeights] = None
):
self.client = ollama_client or OllamaClient()
self.model = model
self.weights = weights or ScoringWeights()
def score_results(
self,
results: List[SearchResult],
capability_gap: str,
target_trl: tuple = (4, 7),
use_llm: bool = True
) -> List[SearchResult]:
"""
Score all results and return sorted by final score.
Args:
results: List of search results to score
capability_gap: Original capability gap description
target_trl: Target TRL range (min, max)
use_llm: Whether to use LLM for relevance scoring
Returns:
Sorted list of results with scores populated
"""
if not results:
return []
# Score each result
for result in results:
# LLM-based relevance score
if use_llm:
result.relevance_score = self._score_relevance_llm(
result, capability_gap
)
else:
result.relevance_score = self._score_relevance_heuristic(
result, capability_gap
)
# Calculate component scores
trl_score = self._score_trl(result.trl_estimate, target_trl)
recency_score = self._score_recency(result.published_date)
authority_score = self.SOURCE_AUTHORITY.get(result.source_type, 0.5)
funding_score = self._score_funding(result.award_amount)
# Calculate final weighted score
result.final_score = (
self.weights.technical_relevance * result.relevance_score +
self.weights.trl_match * trl_score +
self.weights.recency * recency_score +
self.weights.source_authority * authority_score +
self.weights.funding_indicator * funding_score
)
# Sort by final score descending
results.sort(key=lambda x: x.final_score, reverse=True)
return results
def _score_relevance_llm(
self,
result: SearchResult,
capability_gap: str
) -> float:
"""Score relevance using LLM."""
prompt = f"""Rate how well this technology/solution addresses the capability gap.
CAPABILITY GAP:
{capability_gap}
POTENTIAL SOLUTION:
Title: {result.title}
Description: {result.snippet}
Organization: {result.organization or 'Unknown'}
Source: {result.source_type}
Rate the relevance from 0.0 to 1.0 where:
- 0.0-0.2: Not relevant
- 0.2-0.4: Tangentially related
- 0.4-0.6: Somewhat relevant
- 0.6-0.8: Highly relevant
- 0.8-1.0: Direct solution
Respond with JSON:
{{
"score": 0.0-1.0,
"rationale": "Brief explanation"
}}"""
response = self.client.generate(
prompt=prompt,
model=self.model,
temperature=0.1,
format="json"
)
if not response.success:
return self._score_relevance_heuristic(result, capability_gap)
try:
data = json.loads(response.content)
return min(1.0, max(0.0, float(data.get("score", 0.5))))
except (json.JSONDecodeError, ValueError, TypeError):
return self._score_relevance_heuristic(result, capability_gap)
def _score_relevance_heuristic(
self,
result: SearchResult,
capability_gap: str
) -> float:
"""Score relevance using keyword matching heuristics."""
# Simple keyword overlap scoring
gap_words = set(capability_gap.lower().split())
result_text = f"{result.title} {result.snippet}".lower()
result_words = set(result_text.split())
# Remove common words
stopwords = {'the', 'a', 'an', 'is', 'are', 'was', 'were', 'be', 'been',
'being', 'have', 'has', 'had', 'do', 'does', 'did', 'will',
'would', 'could', 'should', 'may', 'might', 'must', 'shall',
'can', 'need', 'for', 'of', 'to', 'in', 'on', 'at', 'by',
'from', 'with', 'and', 'or', 'not', 'that', 'this', 'it'}
gap_words -= stopwords
result_words -= stopwords
if not gap_words:
return 0.5
overlap = len(gap_words & result_words)
score = overlap / len(gap_words)
return min(1.0, score)
def _score_trl(self, trl: Optional[int], target_range: tuple) -> float:
"""Score based on TRL match."""
if trl is None:
return 0.5 # Unknown TRL
min_trl, max_trl = target_range
if min_trl <= trl <= max_trl:
return 1.0
elif trl < min_trl:
# Too early stage
distance = min_trl - trl
return max(0.2, 1.0 - (distance * 0.2))
else:
# Too mature (might be less innovative)
distance = trl - max_trl
return max(0.4, 1.0 - (distance * 0.15))
def _score_recency(self, date_str: Optional[str]) -> float:
"""Score based on how recent the result is."""
if not date_str:
return 0.5
try:
# Try to parse various date formats
for fmt in ["%Y-%m-%d", "%Y-%m", "%Y", "%m/%d/%Y"]:
try:
date = datetime.strptime(date_str[:len(fmt.replace('%', '0'))], fmt)
break
except ValueError:
continue
else:
# Try to extract year
import re
year_match = re.search(r'20\d{2}', date_str)
if year_match:
date = datetime(int(year_match.group()), 6, 1)
else:
return 0.5
# Calculate age in years
age_days = (datetime.now() - date).days
age_years = age_days / 365.25
# Exponential decay with 2-year half-life
score = math.exp(-0.347 * age_years) # ln(2)/2 ≈ 0.347
return max(0.1, min(1.0, score))
except Exception:
return 0.5
def _score_funding(self, amount: Optional[float]) -> float:
"""Score based on funding amount (higher = more validated)."""
if amount is None:
return 0.5
# Log scale scoring
if amount <= 0:
return 0.3
elif amount < 100000: # < $100K
return 0.5
elif amount < 500000: # $100K - $500K
return 0.65
elif amount < 1000000: # $500K - $1M
return 0.75
elif amount < 5000000: # $1M - $5M
return 0.85
else: # > $5M
return 0.95
def batch_score_relevance(
self,
results: List[SearchResult],
capability_gap: str,
batch_size: int = 5
) -> List[float]:
"""
Score multiple results in batches for efficiency.
Args:
results: Results to score
capability_gap: Capability gap description
batch_size: How many to score per LLM call
Returns:
List of relevance scores
"""
scores = []
for i in range(0, len(results), batch_size):
batch = results[i:i + batch_size]
# Build batch prompt
items = []
for j, result in enumerate(batch):
items.append(f"""
Item {j + 1}:
Title: {result.title}
Description: {result.snippet[:200]}
Organization: {result.organization or 'Unknown'}""")
prompt = f"""Rate how well each item addresses this capability gap.
CAPABILITY GAP:
{capability_gap}
ITEMS TO SCORE:
{''.join(items)}
Respond with JSON:
{{
"scores": [
{{"item": 1, "score": 0.0-1.0}},
{{"item": 2, "score": 0.0-1.0}},
...
]
}}"""
response = self.client.generate(
prompt=prompt,
model=self.model,
temperature=0.1,
format="json"
)
if response.success:
try:
data = json.loads(response.content)
batch_scores = {s["item"]: s["score"] for s in data.get("scores", [])}
for j in range(len(batch)):
scores.append(batch_scores.get(j + 1, 0.5))
except (json.JSONDecodeError, KeyError):
scores.extend([0.5] * len(batch))
else:
scores.extend([0.5] * len(batch))
return scores

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"""Pipeline module for TechScout."""
from .discovery import DiscoveryPipeline
from .deep_dive import DeepDivePipeline
from .capability_matcher import CapabilityMatcherPipeline, CapabilityMatchResult
__all__ = [
"DiscoveryPipeline",
"DeepDivePipeline",
"CapabilityMatcherPipeline",
"CapabilityMatchResult",
]

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"""
Phase 2: Deep Dive Pipeline
Takes selected technology candidates and performs comprehensive
company and technology analysis.
"""
import json
import logging
from dataclasses import dataclass, field
from datetime import datetime
from pathlib import Path
from typing import List, Dict, Any, Optional
import uuid
from ..config import TechScoutConfig, config as default_config
from ..extraction.llm_client import OllamaClient
from ..search.web import WebSearcher
from ..search.base import SearchResult
from ..sources.sbir import SBIRSearcher
from ..sources.patents import PatentSearcher
from ..sources.contracts import ContractSearcher
logger = logging.getLogger(__name__)
@dataclass
class CompanyProfile:
"""Comprehensive company profile."""
name: str
description: str
website: Optional[str] = None
headquarters: Optional[str] = None
employee_count: Optional[str] = None
founded: Optional[str] = None
leadership: List[Dict[str, str]] = field(default_factory=list)
def to_dict(self) -> Dict[str, Any]:
return {
"name": self.name,
"description": self.description,
"website": self.website,
"headquarters": self.headquarters,
"employee_count": self.employee_count,
"founded": self.founded,
"leadership": self.leadership,
}
@dataclass
class TechnologyProfile:
"""Detailed technology profile."""
name: str
description: str
technical_approach: str
trl_assessment: int
key_capabilities: List[str]
limitations: List[str]
competitive_advantage: str
related_patents: List[Dict[str, Any]] = field(default_factory=list)
def to_dict(self) -> Dict[str, Any]:
return {
"name": self.name,
"description": self.description,
"technical_approach": self.technical_approach,
"trl_assessment": self.trl_assessment,
"key_capabilities": self.key_capabilities,
"limitations": self.limitations,
"competitive_advantage": self.competitive_advantage,
"related_patents": self.related_patents,
}
@dataclass
class ContractHistory:
"""Company's federal contract history."""
total_contracts: int
total_value: float
primary_agencies: List[str]
recent_contracts: List[Dict[str, Any]]
sbir_awards: List[Dict[str, Any]]
def to_dict(self) -> Dict[str, Any]:
return {
"total_contracts": self.total_contracts,
"total_value": self.total_value,
"primary_agencies": self.primary_agencies,
"recent_contracts": self.recent_contracts,
"sbir_awards": self.sbir_awards,
}
@dataclass
class DeepDiveResult:
"""Result of Phase 2 deep dive."""
id: str
candidate_id: str
organization: str
timestamp: str
company_profile: CompanyProfile
technology_profile: TechnologyProfile
contract_history: ContractHistory
other_offerings: List[Dict[str, Any]]
news_mentions: List[Dict[str, Any]]
assessment: str # LLM-generated overall assessment
risk_factors: List[str]
recommendation: str
duration_seconds: float
success: bool = True
error: Optional[str] = None
def to_dict(self) -> Dict[str, Any]:
return {
"id": self.id,
"candidate_id": self.candidate_id,
"organization": self.organization,
"timestamp": self.timestamp,
"company_profile": self.company_profile.to_dict(),
"technology_profile": self.technology_profile.to_dict(),
"contract_history": self.contract_history.to_dict(),
"other_offerings": self.other_offerings,
"news_mentions": self.news_mentions,
"assessment": self.assessment,
"risk_factors": self.risk_factors,
"recommendation": self.recommendation,
"duration_seconds": self.duration_seconds,
"success": self.success,
"error": self.error,
}
def save(self, path: Path):
"""Save result to JSON file."""
with open(path, "w") as f:
json.dump(self.to_dict(), f, indent=2)
class DeepDivePipeline:
"""
Phase 2 Deep Dive Pipeline.
1. Research company background
2. Analyze technology capabilities
3. Pull contract history
4. Find related patents
5. Search for news/press
6. Generate comprehensive assessment
"""
def __init__(
self,
config: Optional[TechScoutConfig] = None,
model: str = "mistral-nemo:12b"
):
self.config = config or default_config
self.model = model
# Initialize components
self.llm_client = OllamaClient(
base_url=self.config.ollama.base_url,
default_model=model
)
# Initialize searchers
self.web_searcher = WebSearcher()
self.sbir_searcher = SBIRSearcher()
self.patent_searcher = PatentSearcher()
self.contract_searcher = ContractSearcher()
def deep_dive(
self,
organization: str,
technology_context: str,
capability_gap: str,
candidate_id: str = ""
) -> DeepDiveResult:
"""
Run Phase 2 deep dive on a company/technology.
Args:
organization: Company name
technology_context: Description of the technology
capability_gap: Original capability gap
candidate_id: ID from Phase 1 candidate
Returns:
DeepDiveResult with comprehensive analysis
"""
start_time = datetime.now()
result_id = str(uuid.uuid4())[:8]
logger.info(f"Starting deep dive on: {organization}")
# 1. Research company profile
logger.info("Researching company profile...")
company_profile = self._research_company(organization)
# 2. Analyze technology
logger.info("Analyzing technology...")
tech_profile = self._analyze_technology(
organization, technology_context, capability_gap
)
# 3. Get contract history
logger.info("Fetching contract history...")
contract_history = self._get_contract_history(organization)
# 4. Find other offerings
logger.info("Finding other company offerings...")
other_offerings = self._find_other_offerings(organization)
# 5. Search news
logger.info("Searching news mentions...")
news_mentions = self._search_news(organization)
# 6. Generate assessment
logger.info("Generating assessment...")
assessment, risk_factors, recommendation = self._generate_assessment(
organization,
technology_context,
capability_gap,
company_profile,
tech_profile,
contract_history
)
duration = (datetime.now() - start_time).total_seconds()
result = DeepDiveResult(
id=result_id,
candidate_id=candidate_id,
organization=organization,
timestamp=datetime.now().isoformat(),
company_profile=company_profile,
technology_profile=tech_profile,
contract_history=contract_history,
other_offerings=other_offerings,
news_mentions=news_mentions,
assessment=assessment,
risk_factors=risk_factors,
recommendation=recommendation,
duration_seconds=duration,
success=True
)
# Save result
save_path = self.config.analyses_dir / f"deepdive_{result_id}.json"
result.save(save_path)
logger.info(f"Saved deep dive result to {save_path}")
return result
def _research_company(self, organization: str) -> CompanyProfile:
"""Research company background."""
# Search web for company info
results = self.web_searcher.search(
f'"{organization}" company about founded headquarters',
max_results=5
)
# Build context from results
context = "\n".join([f"- {r.title}: {r.snippet}" for r in results])
# Use LLM to extract structured info
prompt = f"""Extract company information from these search results.
Company: {organization}
Search Results:
{context}
Respond with JSON:
{{
"description": "Brief company description",
"website": "company website if found",
"headquarters": "location if found",
"employee_count": "approximate if found",
"founded": "year if found",
"leadership": [
{{"name": "CEO Name", "title": "CEO"}},
{{"name": "CTO Name", "title": "CTO"}}
]
}}
Only include information explicitly found. Use null for unknown fields."""
response = self.llm_client.generate(
prompt=prompt,
temperature=0.1,
format="json"
)
if response.success:
try:
data = json.loads(response.content)
return CompanyProfile(
name=organization,
description=data.get("description", ""),
website=data.get("website"),
headquarters=data.get("headquarters"),
employee_count=data.get("employee_count"),
founded=data.get("founded"),
leadership=data.get("leadership", [])
)
except json.JSONDecodeError:
pass
return CompanyProfile(name=organization, description="Information not available")
def _analyze_technology(
self,
organization: str,
technology_context: str,
capability_gap: str
) -> TechnologyProfile:
"""Analyze the technology in depth."""
# Search for patents by this organization
patents = self.patent_searcher.search(
capability_gap.split()[0], # First keyword
max_results=5,
assignee=organization
)
patent_context = "\n".join([
f"- {p.title}" for p in patents
]) if patents else "No patents found"
prompt = f"""Analyze this technology's capabilities.
Company: {organization}
Technology Context: {technology_context}
Capability Gap: {capability_gap}
Related Patents: {patent_context}
Provide a technical assessment as JSON:
{{
"name": "Technology name",
"description": "Detailed technical description",
"technical_approach": "How the technology works",
"trl_assessment": 1-9,
"key_capabilities": ["capability 1", "capability 2"],
"limitations": ["limitation 1", "limitation 2"],
"competitive_advantage": "What makes this approach unique"
}}"""
response = self.llm_client.generate(
prompt=prompt,
temperature=0.2,
format="json"
)
related_patents = [
{"title": p.title, "number": p.patent_number, "url": p.url}
for p in patents
]
if response.success:
try:
data = json.loads(response.content)
return TechnologyProfile(
name=data.get("name", "Unknown"),
description=data.get("description", ""),
technical_approach=data.get("technical_approach", ""),
trl_assessment=data.get("trl_assessment", 5),
key_capabilities=data.get("key_capabilities", []),
limitations=data.get("limitations", []),
competitive_advantage=data.get("competitive_advantage", ""),
related_patents=related_patents
)
except json.JSONDecodeError:
pass
return TechnologyProfile(
name="Unknown",
description=technology_context,
technical_approach="",
trl_assessment=5,
key_capabilities=[],
limitations=[],
competitive_advantage="",
related_patents=related_patents
)
def _get_contract_history(self, organization: str) -> ContractHistory:
"""Get federal contract history."""
# Get contracts
contracts = self.contract_searcher.get_company_contracts(organization, max_results=20)
# Get SBIR awards
sbir_results = self.sbir_searcher.search(f'"{organization}"', max_results=10)
# Calculate totals
total_value = sum(c.award_amount or 0 for c in contracts)
# Get agency breakdown
agencies = {}
for c in contracts:
agency = c.raw_data.get("Awarding Agency", "Unknown")
agencies[agency] = agencies.get(agency, 0) + 1
primary_agencies = sorted(agencies.keys(), key=lambda x: agencies[x], reverse=True)[:5]
return ContractHistory(
total_contracts=len(contracts),
total_value=total_value,
primary_agencies=primary_agencies,
recent_contracts=[
{
"title": c.title,
"amount": c.award_amount,
"date": c.published_date,
"agency": c.raw_data.get("Awarding Agency"),
"url": c.url
}
for c in contracts[:10]
],
sbir_awards=[
{
"title": s.title,
"amount": s.award_amount,
"phase": s.raw_data.get("phase"),
"year": s.published_date,
"url": s.url
}
for s in sbir_results
]
)
def _find_other_offerings(self, organization: str) -> List[Dict[str, Any]]:
"""Find other products/services from this company."""
results = self.web_searcher.search(
f'"{organization}" products services solutions offerings',
max_results=10
)
offerings = []
for r in results:
offerings.append({
"title": r.title,
"description": r.snippet,
"url": r.url
})
return offerings
def _search_news(self, organization: str) -> List[Dict[str, Any]]:
"""Search recent news about the company."""
results = self.web_searcher.search(
f'"{organization}"',
max_results=10,
news_only=True,
time_filter="y" # Last year
)
news = []
for r in results:
news.append({
"title": r.title,
"snippet": r.snippet,
"date": r.published_date,
"url": r.url
})
return news
def _generate_assessment(
self,
organization: str,
technology_context: str,
capability_gap: str,
company: CompanyProfile,
tech: TechnologyProfile,
contracts: ContractHistory
) -> tuple:
"""Generate overall assessment using LLM."""
prompt = f"""Generate an investment/partnership assessment.
CAPABILITY GAP:
{capability_gap}
COMPANY: {organization}
{company.description}
Headquarters: {company.headquarters or 'Unknown'}
Founded: {company.founded or 'Unknown'}
TECHNOLOGY:
{tech.description}
TRL: {tech.trl_assessment}
Approach: {tech.technical_approach}
CONTRACT HISTORY:
Total Contracts: {contracts.total_contracts}
Total Value: ${contracts.total_value:,.0f}
Primary Agencies: {', '.join(contracts.primary_agencies[:3])}
SBIR Awards: {len(contracts.sbir_awards)}
Provide assessment as JSON:
{{
"assessment": "2-3 paragraph overall assessment of fit for the capability gap",
"risk_factors": ["risk 1", "risk 2", "risk 3"],
"recommendation": "STRONGLY RECOMMEND | RECOMMEND | CONSIDER | DO NOT RECOMMEND",
"recommendation_rationale": "Brief explanation"
}}"""
response = self.llm_client.generate(
prompt=prompt,
model=self.model,
temperature=0.3,
format="json"
)
if response.success:
try:
data = json.loads(response.content)
return (
data.get("assessment", "Assessment not available"),
data.get("risk_factors", []),
f"{data.get('recommendation', 'CONSIDER')}: {data.get('recommendation_rationale', '')}"
)
except json.JSONDecodeError:
pass
return ("Assessment generation failed", [], "CONSIDER: Insufficient data")

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"""
Phase 1: Discovery Pipeline
Takes a capability gap, decomposes it into searches, queries multiple sources,
scores results, and returns ranked candidates.
"""
import json
import logging
from dataclasses import dataclass, field
from datetime import datetime
from pathlib import Path
from typing import List, Dict, Any, Optional
import uuid
from ..config import TechScoutConfig, config as default_config
from ..extraction.llm_client import OllamaClient
from ..extraction.decomposer import QueryDecomposer, DecomposedQuery
from ..extraction.scorer import ResultScorer
from ..extraction.org_extractor import OrganizationExtractor
from ..search.web import WebSearcher
from ..search.base import SearchResult
from ..sources.sbir import SBIRSearcher
from ..sources.patents import PatentSearcher
from ..sources.contracts import ContractSearcher
logger = logging.getLogger(__name__)
@dataclass
class TechnologyCandidate:
"""A scored technology candidate."""
id: str
title: str
organization: str
description: str
source_type: str
source: str
url: str
score: float
relevance_score: float
trl_estimate: Optional[int]
award_amount: Optional[float]
published_date: Optional[str]
award_id: Optional[str]
patent_number: Optional[str]
def to_dict(self) -> Dict[str, Any]:
return {
"id": self.id,
"title": self.title,
"organization": self.organization,
"description": self.description,
"source_type": self.source_type,
"source": self.source,
"url": self.url,
"score": self.score,
"relevance_score": self.relevance_score,
"trl_estimate": self.trl_estimate,
"award_amount": self.award_amount,
"published_date": self.published_date,
"award_id": self.award_id,
"patent_number": self.patent_number,
}
@dataclass
class DiscoveryResult:
"""Result of Phase 1 discovery."""
id: str
capability_gap: str
timestamp: str
decomposition: Dict[str, Any]
candidates: List[TechnologyCandidate]
source_stats: Dict[str, int]
total_results_found: int
search_duration_seconds: float
success: bool = True
error: Optional[str] = None
def to_dict(self) -> Dict[str, Any]:
return {
"id": self.id,
"capability_gap": self.capability_gap,
"timestamp": self.timestamp,
"decomposition": self.decomposition,
"candidates": [c.to_dict() for c in self.candidates],
"source_stats": self.source_stats,
"total_results_found": self.total_results_found,
"search_duration_seconds": self.search_duration_seconds,
"success": self.success,
"error": self.error,
}
def save(self, path: Path):
"""Save result to JSON file."""
with open(path, "w") as f:
json.dump(self.to_dict(), f, indent=2)
class DiscoveryPipeline:
"""
Phase 1 Discovery Pipeline.
1. Decompose capability gap into search queries
2. Search multiple sources (SBIR, patents, contracts, web)
3. Score and rank results
4. Return top candidates
"""
def __init__(
self,
config: Optional[TechScoutConfig] = None,
model: str = "mistral-nemo:12b"
):
self.config = config or default_config
self.model = model
# Initialize components
self.llm_client = OllamaClient(
base_url=self.config.ollama.base_url,
default_model=model
)
self.decomposer = QueryDecomposer(self.llm_client, model)
self.scorer = ResultScorer(self.llm_client, model)
self.org_extractor = OrganizationExtractor(self.llm_client, model)
# Initialize searchers
self.web_searcher = WebSearcher()
self.sbir_searcher = SBIRSearcher()
self.patent_searcher = PatentSearcher()
self.contract_searcher = ContractSearcher()
def discover(
self,
capability_gap: str,
max_results: int = 50,
use_llm_scoring: bool = True,
sources: Optional[List[str]] = None
) -> DiscoveryResult:
"""
Run Phase 1 discovery.
Args:
capability_gap: Natural language description of capability need
max_results: Maximum candidates to return
use_llm_scoring: Use LLM for relevance scoring (slower but better)
sources: Which sources to search (default: all)
Returns:
DiscoveryResult with ranked candidates
"""
start_time = datetime.now()
result_id = str(uuid.uuid4())[:8]
sources = sources or ["sbir", "patents", "contracts", "web", "news"]
logger.info(f"Starting discovery for: {capability_gap[:100]}...")
# Step 1: Decompose the query
logger.info("Decomposing capability gap into search queries...")
decomposition = self.decomposer.decompose(capability_gap)
if not decomposition.success:
return DiscoveryResult(
id=result_id,
capability_gap=capability_gap,
timestamp=datetime.now().isoformat(),
decomposition={},
candidates=[],
source_stats={},
total_results_found=0,
search_duration_seconds=0,
success=False,
error=f"Query decomposition failed: {decomposition.error}"
)
logger.info(f"Generated {len(decomposition.search_queries)} search queries")
# Step 2: Search all sources
all_results: List[SearchResult] = []
source_stats: Dict[str, int] = {}
# SBIR/STTR
if "sbir" in sources:
logger.info("Searching SBIR/STTR awards...")
for query in decomposition.sbir_queries[:3]:
try:
results = self.sbir_searcher.search(query, max_results=15)
all_results.extend(results)
except Exception as e:
logger.warning(f"SBIR search failed: {e}")
source_stats["sbir"] = len([r for r in all_results if r.source_type == "sbir"])
# Patents
if "patents" in sources:
logger.info("Searching patents...")
for query in decomposition.patent_queries[:3]:
try:
results = self.patent_searcher.search(query, max_results=15)
all_results.extend(results)
except Exception as e:
logger.warning(f"Patent search failed: {e}")
source_stats["patents"] = len([r for r in all_results if r.source_type == "patent"])
# Federal contracts
if "contracts" in sources:
logger.info("Searching federal contracts...")
for query in decomposition.search_queries[:2]:
try:
results = self.contract_searcher.search_dod(query, max_results=10)
all_results.extend(results)
except Exception as e:
logger.warning(f"Contract search failed: {e}")
source_stats["contracts"] = len([r for r in all_results if r.source_type == "contract"])
# Web search
if "web" in sources:
logger.info("Searching web...")
for query in decomposition.search_queries[:4]:
try:
results = self.web_searcher.search(query, max_results=10)
all_results.extend(results)
except Exception as e:
logger.warning(f"Web search failed: {e}")
source_stats["web"] = len([r for r in all_results if r.source_type == "web"])
# News search
if "news" in sources:
logger.info("Searching defense news...")
for query in decomposition.news_queries[:2]:
try:
results = self.web_searcher.search(query, max_results=10, news_only=True)
all_results.extend(results)
except Exception as e:
logger.warning(f"News search failed: {e}")
source_stats["news"] = len([r for r in all_results if r.source_type == "news"])
total_found = len(all_results)
logger.info(f"Found {total_found} total results")
# Step 3: Deduplicate by URL
seen_urls = set()
unique_results = []
for result in all_results:
if result.url and result.url not in seen_urls:
seen_urls.add(result.url)
unique_results.append(result)
logger.info(f"After deduplication: {len(unique_results)} unique results")
# Step 3.5: Extract organizations for web/news results that don't have them
logger.info("Extracting organizations from web/news results...")
results_needing_org = [
(i, r) for i, r in enumerate(unique_results)
if r.source_type in ("web", "news", "government", "academic") and not r.organization
]
if results_needing_org:
# Extract organizations using hybrid regex + LLM approach
items_to_extract = [(r.title, r.snippet) for _, r in results_needing_org]
extractions = self.org_extractor.extract_batch(items_to_extract, use_llm_fallback=True)
# Update results with extracted organizations
for (idx, result), extraction in zip(results_needing_org, extractions):
if extraction.organization:
unique_results[idx].organization = extraction.organization
logger.debug(f"Extracted org '{extraction.organization}' from '{result.title[:50]}' (method: {extraction.method})")
extracted_count = sum(1 for e in extractions if e.organization)
logger.info(f"Extracted organizations for {extracted_count}/{len(results_needing_org)} web/news results")
# Step 4: Score and rank
logger.info("Scoring results...")
scored_results = self.scorer.score_results(
unique_results,
capability_gap,
target_trl=decomposition.target_trl_range,
use_llm=use_llm_scoring
)
# Step 5: Convert to candidates
candidates = []
for i, result in enumerate(scored_results[:max_results]):
candidates.append(TechnologyCandidate(
id=f"{result_id}-{i}",
title=result.title,
organization=result.organization or "Unknown",
description=result.snippet,
source_type=result.source_type,
source=result.source,
url=result.url,
score=result.final_score,
relevance_score=result.relevance_score,
trl_estimate=result.trl_estimate,
award_amount=result.award_amount,
published_date=result.published_date,
award_id=result.award_id,
patent_number=result.patent_number,
))
duration = (datetime.now() - start_time).total_seconds()
discovery_result = DiscoveryResult(
id=result_id,
capability_gap=capability_gap,
timestamp=datetime.now().isoformat(),
decomposition=decomposition.to_dict(),
candidates=candidates,
source_stats=source_stats,
total_results_found=total_found,
search_duration_seconds=duration,
success=True
)
# Save result
save_path = self.config.analyses_dir / f"discovery_{result_id}.json"
discovery_result.save(save_path)
logger.info(f"Saved discovery result to {save_path}")
return discovery_result

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"""Search module for TechScout."""
from .base import BaseSearcher, SearchResult
from .web import WebSearcher
__all__ = ["BaseSearcher", "SearchResult", "WebSearcher"]

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"""
Base search classes for TechScout.
"""
from abc import ABC, abstractmethod
from dataclasses import dataclass, field
from typing import List, Optional, Dict, Any
from datetime import datetime
@dataclass
class SearchResult:
"""A single search result from any source."""
title: str
url: str
snippet: str
source: str # Which searcher found this
source_type: str # sbir, patent, contract, news, web
rank: int = 0 # Position in results
# Metadata
published_date: Optional[str] = None
organization: Optional[str] = None # Company/institution name
award_amount: Optional[float] = None # For contracts/grants
trl_estimate: Optional[int] = None # Technology readiness level
# Identifiers
award_id: Optional[str] = None # SBIR award ID, contract number
patent_number: Optional[str] = None
# Raw data for later processing
raw_data: Dict[str, Any] = field(default_factory=dict)
# Scoring (filled in later)
relevance_score: float = 0.0
final_score: float = 0.0
def to_dict(self) -> Dict[str, Any]:
return {
"title": self.title,
"url": self.url,
"snippet": self.snippet,
"source": self.source,
"source_type": self.source_type,
"rank": self.rank,
"published_date": self.published_date,
"organization": self.organization,
"award_amount": self.award_amount,
"trl_estimate": self.trl_estimate,
"award_id": self.award_id,
"patent_number": self.patent_number,
"relevance_score": self.relevance_score,
"final_score": self.final_score,
}
class BaseSearcher(ABC):
"""Abstract base class for all searchers."""
@property
@abstractmethod
def name(self) -> str:
"""Return the name of this searcher."""
pass
@property
@abstractmethod
def source_type(self) -> str:
"""Return the type of source (sbir, patent, contract, news, web)."""
pass
@abstractmethod
def search(self, query: str, max_results: int = 20, **kwargs) -> List[SearchResult]:
"""
Execute a search.
Args:
query: Search query
max_results: Maximum results to return
Returns:
List of SearchResult objects
"""
pass
def search_multiple(
self,
queries: List[str],
max_results_per_query: int = 10
) -> List[SearchResult]:
"""
Execute multiple searches and deduplicate.
Args:
queries: List of search queries
max_results_per_query: Max results per query
Returns:
Deduplicated list of results
"""
all_results = []
seen_urls = set()
for query in queries:
results = self.search(query, max_results=max_results_per_query)
for result in results:
if result.url not in seen_urls:
seen_urls.add(result.url)
all_results.append(result)
return all_results

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"""
Web Search using DuckDuckGo.
General web search for technology scouting.
"""
import logging
from typing import List, Optional
from urllib.parse import urlparse
from datetime import datetime
from .base import BaseSearcher, SearchResult
logger = logging.getLogger(__name__)
class WebSearcher(BaseSearcher):
"""
General web search using DuckDuckGo.
Used for defense news, tech publications, and general discovery.
"""
def __init__(self, delay_between_searches: float = 1.0):
self.delay = delay_between_searches
self._last_search_time: Optional[datetime] = None
@property
def name(self) -> str:
return "DuckDuckGo"
@property
def source_type(self) -> str:
return "web"
def _extract_domain(self, url: str) -> str:
try:
parsed = urlparse(url)
return parsed.netloc
except Exception:
return ""
def _rate_limit(self):
if self._last_search_time is not None:
import time
elapsed = (datetime.now() - self._last_search_time).total_seconds()
if elapsed < self.delay:
time.sleep(self.delay - elapsed)
self._last_search_time = datetime.now()
def search(
self,
query: str,
max_results: int = 20,
news_only: bool = False,
time_filter: Optional[str] = None
) -> List[SearchResult]:
"""
Execute DuckDuckGo search.
Args:
query: Search query
max_results: Maximum results
news_only: Search news instead of web
time_filter: d=day, w=week, m=month, y=year
"""
try:
from ddgs import DDGS
except ImportError:
raise ImportError("ddgs not installed. Run: pip install ddgs")
self._rate_limit()
results = []
try:
with DDGS() as ddgs:
if news_only:
search_results = list(ddgs.news(
query,
timelimit=time_filter,
max_results=max_results
))
for rank, result in enumerate(search_results, 1):
results.append(SearchResult(
title=result.get("title", ""),
url=result.get("url", ""),
snippet=result.get("body", ""),
source=self.name,
source_type="news",
rank=rank,
published_date=result.get("date"),
raw_data=result
))
else:
search_results = list(ddgs.text(
query,
timelimit=time_filter,
max_results=max_results
))
for rank, result in enumerate(search_results, 1):
# Detect source type based on domain
url = result.get("href", "")
domain = self._extract_domain(url)
source_type = self._classify_domain(domain)
results.append(SearchResult(
title=result.get("title", ""),
url=url,
snippet=result.get("body", ""),
source=self.name,
source_type=source_type,
rank=rank,
raw_data=result
))
logger.info(f"Web search for '{query}' returned {len(results)} results")
except Exception as e:
logger.error(f"Web search failed: {e}")
raise
return results
def _classify_domain(self, domain: str) -> str:
"""Classify domain into source type."""
domain = domain.lower()
# Defense/government sources
if any(x in domain for x in ['.gov', '.mil']):
return "government"
# News sources
if any(x in domain for x in [
'news', 'reuters', 'bloomberg', 'defensenews',
'spacenews', 'aviationweek', 'janes', 'breakingdefense'
]):
return "news"
# Academic
if any(x in domain for x in ['.edu', 'arxiv', 'ieee', 'springer', 'nature']):
return "academic"
# Patent databases
if any(x in domain for x in ['patent', 'uspto', 'espacenet']):
return "patent"
return "web"
def search_defense_news(self, query: str, max_results: int = 15) -> List[SearchResult]:
"""Search specifically for defense news."""
defense_query = f"{query} site:defensenews.com OR site:breakingdefense.com OR site:janes.com OR site:aviationweek.com"
return self.search(defense_query, max_results=max_results)
def search_space_tech(self, query: str, max_results: int = 15) -> List[SearchResult]:
"""Search specifically for space technology news."""
space_query = f"{query} site:spacenews.com OR site:spaceflightnow.com OR site:nasaspaceflight.com"
return self.search(space_query, max_results=max_results)

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"""
Specialized data source integrations for TechScout.
Free APIs:
- SBIR.gov - SBIR/STTR awards
- USPTO - Patent data
- USASpending.gov - Federal contracts
- SAM.gov - Company registrations
"""
from .sbir import SBIRSearcher
from .patents import PatentSearcher
from .contracts import ContractSearcher
__all__ = ["SBIRSearcher", "PatentSearcher", "ContractSearcher"]

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"""
USASpending.gov Contract Search
Search federal contract awards to understand company capabilities,
contract history, and funding relationships.
API Documentation: https://api.usaspending.gov/
Free, no API key required.
"""
import logging
import requests
from typing import List, Optional, Dict, Any
from datetime import datetime, timedelta
from ..search.base import BaseSearcher, SearchResult
logger = logging.getLogger(__name__)
class ContractSearcher(BaseSearcher):
"""
Search USASpending.gov for federal contracts.
Useful for:
- Finding companies with relevant contract experience
- Understanding funding levels for technologies
- Identifying prime contractors and their focus areas
"""
BASE_URL = "https://api.usaspending.gov/api/v2"
def __init__(self, timeout: int = 30):
self.timeout = timeout
@property
def name(self) -> str:
return "USASpending"
@property
def source_type(self) -> str:
return "contract"
def search(
self,
query: str,
max_results: int = 20,
agency: Optional[str] = None,
min_amount: Optional[float] = None,
years_back: int = 5
) -> List[SearchResult]:
"""
Search federal contracts.
Args:
query: Keyword search
max_results: Maximum results
agency: Filter by agency (e.g., "Department of Defense")
min_amount: Minimum contract value
years_back: How many years back to search
Returns:
List of SearchResult objects
"""
# Calculate date range
end_date = datetime.now()
start_date = end_date - timedelta(days=years_back * 365)
payload = {
"filters": {
"keywords": [query],
"time_period": [{
"start_date": start_date.strftime("%Y-%m-%d"),
"end_date": end_date.strftime("%Y-%m-%d")
}]
},
"fields": [
"Award ID",
"Recipient Name",
"Description",
"Award Amount",
"Start Date",
"Awarding Agency",
"Awarding Sub Agency",
"Contract Award Type"
],
"page": 1,
"limit": min(max_results, 100),
"sort": "Award Amount",
"order": "desc"
}
if agency:
payload["filters"]["agencies"] = [{
"type": "awarding",
"tier": "toptier",
"name": agency
}]
if min_amount:
payload["filters"]["award_amounts"] = [{
"lower_bound": min_amount
}]
results = []
try:
response = requests.post(
f"{self.BASE_URL}/search/spending_by_award/",
json=payload,
timeout=self.timeout,
headers={"Content-Type": "application/json"}
)
response.raise_for_status()
data = response.json()
for rank, award in enumerate(data.get("results", []), 1):
if rank > max_results:
break
award_id = award.get("Award ID", "")
# USASpending award detail URL
url = f"https://www.usaspending.gov/award/{award_id}" if award_id else ""
results.append(SearchResult(
title=award.get("Description", "")[:200] if award.get("Description") else "Contract Award",
url=url,
snippet=award.get("Description", "")[:500] if award.get("Description") else "",
source=self.name,
source_type=self.source_type,
rank=rank,
published_date=award.get("Start Date"),
organization=award.get("Recipient Name", ""),
award_amount=self._parse_amount(award.get("Award Amount")),
award_id=award_id,
trl_estimate=6, # Contracts typically for more mature tech
raw_data=award
))
logger.info(f"Contract search for '{query}' returned {len(results)} results")
except requests.exceptions.RequestException as e:
logger.error(f"USASpending API request failed: {e}")
except Exception as e:
logger.error(f"Contract search error: {e}")
return results
def _parse_amount(self, amount) -> Optional[float]:
"""Parse award amount to float."""
if amount is None:
return None
try:
return float(amount)
except (ValueError, TypeError):
return None
def search_dod(self, query: str, max_results: int = 20) -> List[SearchResult]:
"""Search specifically DOD contracts."""
return self.search(query, max_results=max_results, agency="Department of Defense")
def search_space_force(self, query: str, max_results: int = 20) -> List[SearchResult]:
"""Search Space Force / space-related DOD contracts."""
space_query = f"{query} (space OR satellite OR orbital OR launch)"
return self.search(space_query, max_results=max_results, agency="Department of Defense")
def get_recipient_profile(self, recipient_name: str) -> Dict[str, Any]:
"""
Get profile information about a contract recipient.
Returns summary of their contract history.
"""
payload = {
"filters": {
"recipient_search_text": [recipient_name],
"time_period": [{
"start_date": "2019-01-01",
"end_date": datetime.now().strftime("%Y-%m-%d")
}]
},
"category": "awarding_agency",
"limit": 10
}
try:
response = requests.post(
f"{self.BASE_URL}/search/spending_by_category/",
json=payload,
timeout=self.timeout,
headers={"Content-Type": "application/json"}
)
response.raise_for_status()
data = response.json()
return {
"recipient_name": recipient_name,
"total_contracts": data.get("total", 0),
"agencies": data.get("results", [])
}
except Exception as e:
logger.error(f"Failed to get recipient profile: {e}")
return {"recipient_name": recipient_name, "error": str(e)}
def get_company_contracts(
self,
company_name: str,
max_results: int = 50
) -> List[SearchResult]:
"""Get all recent contracts for a specific company."""
payload = {
"filters": {
"recipient_search_text": [company_name],
"time_period": [{
"start_date": "2020-01-01",
"end_date": datetime.now().strftime("%Y-%m-%d")
}]
},
"fields": [
"Award ID",
"Recipient Name",
"Description",
"Award Amount",
"Start Date",
"Awarding Agency",
"Awarding Sub Agency"
],
"page": 1,
"limit": min(max_results, 100),
"sort": "Start Date",
"order": "desc"
}
results = []
try:
response = requests.post(
f"{self.BASE_URL}/search/spending_by_award/",
json=payload,
timeout=self.timeout,
headers={"Content-Type": "application/json"}
)
response.raise_for_status()
data = response.json()
for rank, award in enumerate(data.get("results", []), 1):
award_id = award.get("Award ID", "")
url = f"https://www.usaspending.gov/award/{award_id}" if award_id else ""
results.append(SearchResult(
title=award.get("Description", "")[:200] if award.get("Description") else "Contract",
url=url,
snippet=f"Agency: {award.get('Awarding Agency', 'Unknown')}",
source=self.name,
source_type=self.source_type,
rank=rank,
published_date=award.get("Start Date"),
organization=award.get("Recipient Name", ""),
award_amount=self._parse_amount(award.get("Award Amount")),
award_id=award_id,
raw_data=award
))
logger.info(f"Found {len(results)} contracts for {company_name}")
except Exception as e:
logger.error(f"Company contract search failed: {e}")
return results

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"""
USPTO Patent Search
Uses the USPTO PatentsView API for patent searches.
Free, no API key required.
API Documentation: https://patentsview.org/apis/api-endpoints
"""
import logging
import requests
from typing import List, Optional, Dict, Any
from datetime import datetime
from ..search.base import BaseSearcher, SearchResult
logger = logging.getLogger(__name__)
class PatentSearcher(BaseSearcher):
"""
Search USPTO patent database via PatentsView API.
Patents indicate active R&D and can reveal technical approaches
to solving capability gaps.
"""
BASE_URL = "https://api.patentsview.org/patents/query"
def __init__(self, timeout: int = 30):
self.timeout = timeout
@property
def name(self) -> str:
return "USPTO Patents"
@property
def source_type(self) -> str:
return "patent"
def search(
self,
query: str,
max_results: int = 20,
year_start: Optional[int] = None,
assignee: Optional[str] = None
) -> List[SearchResult]:
"""
Search USPTO patents.
Args:
query: Text search (searches title, abstract, claims)
max_results: Maximum results
year_start: Only patents from this year forward
assignee: Filter by assignee/company name
Returns:
List of SearchResult objects
"""
# Build query filter
query_parts = [{"_text_any": {"patent_abstract": query}}]
if year_start:
query_parts.append({
"_gte": {"patent_date": f"{year_start}-01-01"}
})
if assignee:
query_parts.append({
"_text_any": {"assignee_organization": assignee}
})
# Combine with AND
if len(query_parts) > 1:
query_filter = {"_and": query_parts}
else:
query_filter = query_parts[0]
payload = {
"q": query_filter,
"f": [
"patent_number",
"patent_title",
"patent_abstract",
"patent_date",
"assignee_organization",
"inventor_first_name",
"inventor_last_name"
],
"o": {"per_page": min(max_results, 100)},
"s": [{"patent_date": "desc"}] # Most recent first
}
results = []
try:
response = requests.post(
self.BASE_URL,
json=payload,
timeout=self.timeout,
headers={"Content-Type": "application/json"}
)
response.raise_for_status()
data = response.json()
patents = data.get("patents", [])
for rank, patent in enumerate(patents, 1):
if rank > max_results:
break
patent_number = patent.get("patent_number", "")
url = f"https://patents.google.com/patent/US{patent_number}" if patent_number else ""
# Get primary assignee
assignees = patent.get("assignees", [])
org = assignees[0].get("assignee_organization", "") if assignees else ""
results.append(SearchResult(
title=patent.get("patent_title", ""),
url=url,
snippet=patent.get("patent_abstract", "")[:500] if patent.get("patent_abstract") else "",
source=self.name,
source_type=self.source_type,
rank=rank,
published_date=patent.get("patent_date"),
organization=org,
patent_number=patent_number,
trl_estimate=5, # Patents typically represent mid-TRL tech
raw_data=patent
))
logger.info(f"Patent search for '{query}' returned {len(results)} results")
except requests.exceptions.RequestException as e:
logger.error(f"USPTO API request failed: {e}")
except Exception as e:
logger.error(f"Patent search error: {e}")
return results
def search_defense_contractors(self, query: str, max_results: int = 20) -> List[SearchResult]:
"""Search patents from major defense contractors."""
contractors = [
"Lockheed Martin",
"Raytheon",
"Northrop Grumman",
"Boeing",
"General Dynamics",
"L3Harris",
"BAE Systems"
]
all_results = []
per_contractor = max(2, max_results // len(contractors))
for contractor in contractors:
results = self.search(
query,
max_results=per_contractor,
assignee=contractor
)
all_results.extend(results)
# Sort by date and limit
all_results.sort(key=lambda x: x.published_date or "", reverse=True)
return all_results[:max_results]
def get_patent_details(self, patent_number: str) -> Optional[Dict[str, Any]]:
"""Get detailed information about a specific patent."""
payload = {
"q": {"patent_number": patent_number},
"f": [
"patent_number",
"patent_title",
"patent_abstract",
"patent_date",
"patent_type",
"assignee_organization",
"assignee_type",
"inventor_first_name",
"inventor_last_name",
"inventor_city",
"inventor_state",
"cpc_section_id",
"cpc_subsection_title"
]
}
try:
response = requests.post(
self.BASE_URL,
json=payload,
timeout=self.timeout,
headers={"Content-Type": "application/json"}
)
response.raise_for_status()
data = response.json()
patents = data.get("patents", [])
return patents[0] if patents else None
except Exception as e:
logger.error(f"Failed to get patent details: {e}")
return None

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"""
SBIR/STTR Award Search
Uses the SBIR.gov API to search for Small Business Innovation Research
and Small Business Technology Transfer awards.
API Documentation: https://www.sbir.gov/api
Free, no API key required.
"""
import logging
import requests
from typing import List, Optional, Dict, Any
from datetime import datetime
from ..search.base import BaseSearcher, SearchResult
logger = logging.getLogger(__name__)
class SBIRSearcher(BaseSearcher):
"""
Search SBIR/STTR awards database.
The SBIR program funds R&D at small businesses, making it
excellent for finding emerging defense technologies.
"""
BASE_URL = "https://www.sbir.gov/api/awards.json"
def __init__(self, timeout: int = 30):
self.timeout = timeout
@property
def name(self) -> str:
return "SBIR.gov"
@property
def source_type(self) -> str:
return "sbir"
def search(
self,
query: str,
max_results: int = 20,
agency: Optional[str] = None,
year_start: Optional[int] = None,
year_end: Optional[int] = None,
phase: Optional[str] = None # "Phase I", "Phase II", "Phase III"
) -> List[SearchResult]:
"""
Search SBIR/STTR awards.
Args:
query: Keyword search
max_results: Maximum results to return
agency: Filter by agency (e.g., "DOD", "NASA", "DOE")
year_start: Start year filter
year_end: End year filter
phase: Filter by phase
Returns:
List of SearchResult objects
"""
params = {
"keyword": query,
"rows": min(max_results, 100), # API limit
}
if agency:
params["agency"] = agency
if year_start:
params["year_start"] = year_start
if year_end:
params["year_end"] = year_end
if phase:
params["phase"] = phase
results = []
try:
response = requests.get(
self.BASE_URL,
params=params,
timeout=self.timeout
)
response.raise_for_status()
data = response.json()
for rank, award in enumerate(data, 1):
if rank > max_results:
break
# Estimate TRL based on phase
trl_estimate = self._estimate_trl(award.get("phase", ""))
# Build URL to award page
award_id = award.get("award_id", "")
url = f"https://www.sbir.gov/sbirsearch/detail/{award_id}" if award_id else ""
results.append(SearchResult(
title=award.get("award_title", ""),
url=url,
snippet=award.get("abstract", "")[:500] if award.get("abstract") else "",
source=self.name,
source_type=self.source_type,
rank=rank,
published_date=award.get("award_year"),
organization=award.get("firm", ""),
award_amount=self._parse_amount(award.get("award_amount")),
trl_estimate=trl_estimate,
award_id=award_id,
raw_data=award
))
logger.info(f"SBIR search for '{query}' returned {len(results)} results")
except requests.exceptions.RequestException as e:
logger.error(f"SBIR API request failed: {e}")
except Exception as e:
logger.error(f"SBIR search error: {e}")
return results
def _estimate_trl(self, phase: str) -> int:
"""Estimate TRL based on SBIR phase."""
phase = phase.lower()
if "phase i" in phase and "ii" not in phase:
return 3 # Proof of concept
elif "phase ii" in phase and "iii" not in phase:
return 5 # Prototype development
elif "phase iii" in phase:
return 7 # Production ready
return 4 # Default mid-range
def _parse_amount(self, amount) -> Optional[float]:
"""Parse award amount to float."""
if amount is None:
return None
try:
if isinstance(amount, str):
amount = amount.replace("$", "").replace(",", "")
return float(amount)
except (ValueError, TypeError):
return None
def search_defense(self, query: str, max_results: int = 20) -> List[SearchResult]:
"""Search specifically DOD SBIR awards."""
return self.search(query, max_results=max_results, agency="DOD")
def search_space(self, query: str, max_results: int = 20) -> List[SearchResult]:
"""Search NASA and Space Force SBIR awards."""
# Search NASA
nasa_results = self.search(query, max_results=max_results // 2, agency="NASA")
# Search DOD (includes Space Force)
dod_results = self.search(
f"{query} space OR satellite OR orbital",
max_results=max_results // 2,
agency="DOD"
)
return nasa_results + dod_results
def get_award_details(self, award_id: str) -> Optional[Dict[str, Any]]:
"""Get detailed information about a specific award."""
try:
response = requests.get(
self.BASE_URL,
params={"award_id": award_id},
timeout=self.timeout
)
response.raise_for_status()
data = response.json()
return data[0] if data else None
except Exception as e:
logger.error(f"Failed to get award details: {e}")
return None

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techscout/technology/__init__.py Normal file
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"""Technology extraction, grouping, and evaluation module for TechScout."""
from .types import (
ExtractedTechnology,
GroupedTechnology,
SourceEvidence,
Developer,
CapabilityMatch,
CriterionResult,
EvaluatedTechnology,
)
from .extractor import TechnologyExtractor
from .grouper import TechnologyGrouper
from .evaluator import CapabilityEvaluator
__all__ = [
# Types
"ExtractedTechnology",
"GroupedTechnology",
"SourceEvidence",
"Developer",
"CapabilityMatch",
"CriterionResult",
"EvaluatedTechnology",
# Services
"TechnologyExtractor",
"TechnologyGrouper",
"CapabilityEvaluator",
]

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techscout/technology/evaluator.py Normal file
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"""
Capability Evaluator for TechScout.
Evaluates how well each technology matches the user's capability need.
Produces structured capability-fit assessments.
This is Stage 5 of the Capability-Technology Matching pipeline.
"""
import json
import logging
from typing import List, Optional
from ..extraction.llm_client import OllamaClient
from ..capability.types import CapabilityNeed, CapabilityCriterion
from .types import (
GroupedTechnology,
CapabilityMatch,
CriterionResult,
EvaluatedTechnology,
)
logger = logging.getLogger(__name__)
class CapabilityEvaluator:
"""
Evaluates technologies against capability needs.
For each technology, produces:
- Per-criterion assessments (SUPPORTS/PARTIAL/DOES_NOT_SUPPORT/UNKNOWN)
- Overall fit score (0-100)
- Narrative explanation
- Strengths, limitations, unknowns
- Investigation recommendation
"""
EVALUATION_PROMPT = """Evaluate whether this technology addresses the user's capability need.
USER'S CAPABILITY NEED:
{functional_need}
TECHNOLOGY:
Name: {tech_name}
Type: {tech_type}
Description: {description}
Capabilities: {capabilities}
Developer(s): {developers}
Maturity: TRL {trl_estimate} (Evidence: {trl_evidence})
EVALUATION CRITERIA:
{criteria_text}
INSTRUCTIONS:
For each criterion, assess:
- SUPPORTS: Technology clearly provides this capability
- PARTIAL: Technology partially addresses this, with limitations
- DOES_NOT_SUPPORT: Technology does not address this
- UNKNOWN: Insufficient information to determine
Respond with JSON:
{{
"criteria_results": [
{{
"criterion": "criterion text",
"weight": "must_have|should_have|nice_to_have",
"assessment": "SUPPORTS|PARTIAL|DOES_NOT_SUPPORT|UNKNOWN",
"evidence": "Explanation with evidence from technology description"
}}
],
"how_it_addresses_need": "2-3 sentences explaining how this technology could address the capability need",
"key_strengths": ["strength 1", "strength 2"],
"key_limitations": ["limitation 1", "limitation 2"],
"key_unknowns": ["unknown 1", "unknown 2"],
"investigation_worthy": true/false,
"investigation_rationale": "Why this technology is/isn't worth investigating further"
}}"""
def __init__(
self,
ollama_client: Optional[OllamaClient] = None,
model: str = "mistral-nemo:12b"
):
self.client = ollama_client or OllamaClient()
self.model = model
def evaluate_all(
self,
technologies: List[GroupedTechnology],
capability_need: CapabilityNeed,
capability_criteria: List[CapabilityCriterion]
) -> List[EvaluatedTechnology]:
"""
Evaluate all technologies against capability criteria.
Args:
technologies: List of grouped technologies to evaluate
capability_need: Structured capability need
capability_criteria: List of evaluation criteria
Returns:
List of evaluated technologies with fit assessments
"""
logger.info(f"Evaluating {len(technologies)} technologies against capability criteria...")
evaluated = []
for tech in technologies:
match = self._evaluate_technology(tech, capability_need, capability_criteria)
evaluated.append(EvaluatedTechnology(
technology=tech,
capability_match=match
))
# Sort by fit score descending
evaluated.sort(key=lambda e: e.capability_match.fit_score, reverse=True)
logger.info(f"Evaluation complete. Top score: {evaluated[0].capability_match.fit_score if evaluated else 0}")
return evaluated
def _evaluate_technology(
self,
tech: GroupedTechnology,
capability_need: CapabilityNeed,
capability_criteria: List[CapabilityCriterion]
) -> CapabilityMatch:
"""Evaluate a single technology against capability criteria."""
# Build criteria text for prompt
criteria_text = ""
for i, criterion in enumerate(capability_criteria, 1):
criteria_text += f"{i}. [{criterion.weight.upper()}] {criterion.criterion}\n"
criteria_text += f" Keywords: {', '.join(criterion.keywords[:5])}\n"
# Build developers text
developers_text = ", ".join(d.name for d in tech.developers) if tech.developers else "Unknown"
prompt = self.EVALUATION_PROMPT.format(
functional_need=capability_need.functional_need,
tech_name=tech.canonical_name,
tech_type=tech.technology_type,
description=tech.description[:500],
capabilities="; ".join(tech.capabilities[:5]),
developers=developers_text,
trl_estimate=tech.trl_estimate or "Unknown",
trl_evidence="; ".join(tech.trl_evidence[:3]) if tech.trl_evidence else "None",
criteria_text=criteria_text
)
response = self.client.generate(
prompt=prompt,
model=self.model,
temperature=0.1,
format="json"
)
if not response.success:
logger.warning(f"LLM evaluation failed for: {tech.canonical_name}")
return self._create_fallback_match(tech, capability_criteria)
try:
data = json.loads(response.content)
except json.JSONDecodeError:
data = self.client.extract_json_from_text(response.content)
if not data:
return self._create_fallback_match(tech, capability_criteria)
return self._build_capability_match(data, capability_criteria)
def _build_capability_match(
self,
data: dict,
capability_criteria: List[CapabilityCriterion]
) -> CapabilityMatch:
"""Build CapabilityMatch from LLM response."""
# Parse criteria results
criteria_results = []
results_data = data.get("criteria_results", [])
for result in results_data:
assessment = result.get("assessment", "UNKNOWN")
if assessment not in ("SUPPORTS", "PARTIAL", "DOES_NOT_SUPPORT", "UNKNOWN"):
assessment = "UNKNOWN"
weight = result.get("weight", "should_have")
if weight not in ("must_have", "should_have", "nice_to_have"):
weight = "should_have"
criteria_results.append(CriterionResult(
criterion=result.get("criterion", ""),
weight=weight,
assessment=assessment,
evidence=result.get("evidence", ""),
source=result.get("source")
))
# Calculate fit score
fit_score = self._calculate_fit_score(criteria_results)
# Determine overall fit
overall_fit = self._determine_overall_fit(fit_score, criteria_results)
return CapabilityMatch(
overall_fit=overall_fit,
fit_score=fit_score,
criteria_results=criteria_results,
how_it_addresses_need=data.get("how_it_addresses_need", ""),
key_strengths=data.get("key_strengths", []),
key_limitations=data.get("key_limitations", []),
key_unknowns=data.get("key_unknowns", []),
investigation_worthy=data.get("investigation_worthy", fit_score >= 50),
investigation_rationale=data.get("investigation_rationale", "")
)
def _calculate_fit_score(self, criteria_results: List[CriterionResult]) -> int:
"""
Calculate fit score based on criteria results.
Scoring:
- must_have SUPPORTS: +30 points
- must_have PARTIAL: +15 points
- must_have DOES_NOT_SUPPORT: 0 points (caps overall)
- should_have SUPPORTS: +15 points
- should_have PARTIAL: +8 points
- nice_to_have SUPPORTS: +5 points
- nice_to_have PARTIAL: +3 points
- UNKNOWN: +5 points (benefit of doubt)
Normalized to 0-100.
"""
if not criteria_results:
return 50 # Neutral score if no criteria
total_points = 0
max_points = 0
for result in criteria_results:
weight = result.weight
assessment = result.assessment
# Calculate max possible points for this criterion
if weight == "must_have":
max_points += 30
elif weight == "should_have":
max_points += 15
else: # nice_to_have
max_points += 5
# Calculate actual points
if assessment == "SUPPORTS":
if weight == "must_have":
total_points += 30
elif weight == "should_have":
total_points += 15
else:
total_points += 5
elif assessment == "PARTIAL":
if weight == "must_have":
total_points += 15
elif weight == "should_have":
total_points += 8
else:
total_points += 3
elif assessment == "UNKNOWN":
# Benefit of doubt
total_points += 5
# DOES_NOT_SUPPORT adds 0 points
# Normalize to 0-100
if max_points > 0:
score = int((total_points / max_points) * 100)
else:
score = 50
return min(100, max(0, score))
def _determine_overall_fit(
self,
score: int,
criteria_results: List[CriterionResult]
) -> str:
"""Determine overall fit category based on score and criteria results."""
# Check for must_have failures
must_have_failures = sum(
1 for r in criteria_results
if r.weight == "must_have" and r.assessment == "DOES_NOT_SUPPORT"
)
# Check for too many unknowns
unknown_count = sum(1 for r in criteria_results if r.assessment == "UNKNOWN")
unknown_ratio = unknown_count / len(criteria_results) if criteria_results else 0
# If most criteria are unknown, mark as UNCERTAIN
if unknown_ratio > 0.5:
return "UNCERTAIN"
# Apply score-based categorization with must_have penalty
if must_have_failures >= 2:
return "LOW"
elif must_have_failures == 1:
# Cap at MEDIUM if any must_have fails
if score >= 50:
return "MEDIUM"
else:
return "LOW"
else:
# No must_have failures
if score >= 75:
return "HIGH"
elif score >= 50:
return "MEDIUM"
elif score >= 25:
return "LOW"
else:
return "LOW"
def _create_fallback_match(
self,
tech: GroupedTechnology,
capability_criteria: List[CapabilityCriterion]
) -> CapabilityMatch:
"""Create a fallback capability match using heuristics."""
# Simple keyword-based assessment
tech_text = f"{tech.canonical_name} {tech.description} {' '.join(tech.capabilities)}".lower()
criteria_results = []
for criterion in capability_criteria:
# Check if any keywords appear in tech text
keyword_matches = sum(1 for kw in criterion.keywords if kw.lower() in tech_text)
if keyword_matches >= 2:
assessment = "SUPPORTS"
elif keyword_matches == 1:
assessment = "PARTIAL"
else:
assessment = "UNKNOWN"
criteria_results.append(CriterionResult(
criterion=criterion.criterion,
weight=criterion.weight,
assessment=assessment,
evidence=f"Heuristic: {keyword_matches} keyword matches",
source=None
))
fit_score = self._calculate_fit_score(criteria_results)
overall_fit = self._determine_overall_fit(fit_score, criteria_results)
return CapabilityMatch(
overall_fit=overall_fit,
fit_score=fit_score,
criteria_results=criteria_results,
how_it_addresses_need="Assessment based on keyword matching (LLM unavailable)",
key_strengths=[],
key_limitations=["Full LLM assessment unavailable"],
key_unknowns=["Detailed capability analysis not performed"],
investigation_worthy=fit_score >= 50,
investigation_rationale="Based on keyword matching only"
)

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"""
Technology Extractor for TechScout.
Extracts specific, nameable technologies from search results.
Filters out results that are just topic discussions or generic content.
This is Stage 3 of the Capability-Technology Matching pipeline.
"""
import json
import logging
import uuid
from typing import List, Optional, Tuple
from dataclasses import dataclass
from ..extraction.llm_client import OllamaClient
from ..search.base import SearchResult
from ..capability.types import CapabilityNeed, TechnologyIndicators
from .types import ExtractedTechnology
logger = logging.getLogger(__name__)
@dataclass
class ExtractionResult:
"""Result of technology extraction from search results."""
technologies: List[ExtractedTechnology]
discarded_ids: List[str] # IDs of results that weren't technologies
extraction_stats: dict
class TechnologyExtractor:
"""
Extracts specific technologies from search results.
Uses a combination of:
1. Heuristic pre-filtering (source type, keyword indicators)
2. LLM-based extraction (for actual technology identification)
"""
EXTRACTION_PROMPT = """Analyze this search result and determine if it describes a SPECIFIC TECHNOLOGY.
CONTEXT - User is looking for:
{functional_need}
SEARCH RESULT:
Source Type: {source_type}
Title: {title}
Content: {snippet}
Organization: {organization}
INSTRUCTIONS:
1. Does this describe a SPECIFIC, NAMEABLE technology?
- YES examples: "SPAD Array Detector", "Quantum dot infrared sensor", "ML-based tracking algorithm"
- NO examples: Topic discussions, overviews, problem descriptions, generic categories like "sensor technology"
2. If YES, extract technology details. If NO, explain why.
Respond with JSON:
{{
"is_technology": true/false,
"reason": "Why this is/isn't a specific technology",
"technology": {{
"name": "Specific technology name",
"type": "sensor|algorithm|material|system|platform|device|method|software|other",
"description": "What it is (1-2 sentences)",
"capabilities": ["capability 1", "capability 2"],
"mechanism": "How it works (if described)",
"developer": "Organization name if mentioned",
"developer_type": "company|university|government|lab|unknown",
"trl_indicators": ["Phase II SBIR", "prototype demonstrated", etc.]
}}
}}
If is_technology is false, omit the technology object."""
BATCH_EXTRACTION_PROMPT = """Analyze these search results and identify which describe SPECIFIC TECHNOLOGIES.
CONTEXT - User is looking for:
{functional_need}
SEARCH RESULTS:
{results_text}
For each result, determine if it describes a specific, nameable technology (not generic category or topic discussion).
Respond with JSON:
{{
"extractions": [
{{
"item_number": 1,
"is_technology": true/false,
"reason": "brief explanation",
"technology": {{...}} or null
}},
...
]
}}
Technology object schema (when is_technology is true):
{{
"name": "Specific technology name",
"type": "sensor|algorithm|material|system|platform|device|method|software|other",
"description": "What it is",
"capabilities": ["cap1", "cap2"],
"developer": "Organization if mentioned",
"trl_indicators": ["evidence of maturity"]
}}"""
# Source types more likely to describe actual technologies
HIGH_TECH_LIKELIHOOD_SOURCES = {"sbir", "patent"}
MEDIUM_TECH_LIKELIHOOD_SOURCES = {"contract"}
LOW_TECH_LIKELIHOOD_SOURCES = {"news", "web", "academic"}
def __init__(
self,
ollama_client: Optional[OllamaClient] = None,
model: str = "mistral-nemo:12b"
):
self.client = ollama_client or OllamaClient()
self.model = model
def extract_all(
self,
results: List[SearchResult],
capability_need: CapabilityNeed,
technology_indicators: Optional[TechnologyIndicators] = None,
batch_size: int = 5
) -> ExtractionResult:
"""
Extract technologies from all search results.
Args:
results: List of search results to process
capability_need: Structured capability need for context
technology_indicators: Positive/negative indicators
batch_size: How many results to process per LLM call
Returns:
ExtractionResult with extracted technologies and stats
"""
logger.info(f"Extracting technologies from {len(results)} results...")
technologies = []
discarded_ids = []
# Separate by likelihood of being a technology
high_priority = []
medium_priority = []
low_priority = []
for result in results:
if result.source_type in self.HIGH_TECH_LIKELIHOOD_SOURCES:
high_priority.append(result)
elif result.source_type in self.MEDIUM_TECH_LIKELIHOOD_SOURCES:
medium_priority.append(result)
else:
low_priority.append(result)
# Process high priority (all)
if high_priority:
logger.info(f"Processing {len(high_priority)} high-priority results (SBIR/patents)...")
techs, discarded = self._process_batch_results(
high_priority, capability_need, batch_size
)
technologies.extend(techs)
discarded_ids.extend(discarded)
# Process medium priority (all)
if medium_priority:
logger.info(f"Processing {len(medium_priority)} medium-priority results (contracts)...")
techs, discarded = self._process_batch_results(
medium_priority, capability_need, batch_size
)
technologies.extend(techs)
discarded_ids.extend(discarded)
# Process low priority with pre-filtering
if low_priority:
# Apply heuristic filter first
filtered_low = self._heuristic_filter(low_priority, technology_indicators)
logger.info(f"Processing {len(filtered_low)}/{len(low_priority)} low-priority results (passed heuristic filter)...")
if filtered_low:
techs, discarded = self._process_batch_results(
filtered_low, capability_need, batch_size
)
technologies.extend(techs)
discarded_ids.extend(discarded)
# Add filtered-out results to discarded
filtered_out_ids = [
r.url for r in low_priority if r not in filtered_low
]
discarded_ids.extend(filtered_out_ids)
logger.info(f"Extracted {len(technologies)} technologies, discarded {len(discarded_ids)} results")
return ExtractionResult(
technologies=technologies,
discarded_ids=discarded_ids,
extraction_stats={
"total_results": len(results),
"high_priority_processed": len(high_priority),
"medium_priority_processed": len(medium_priority),
"low_priority_processed": len(low_priority),
"technologies_extracted": len(technologies),
"results_discarded": len(discarded_ids),
}
)
def _heuristic_filter(
self,
results: List[SearchResult],
indicators: Optional[TechnologyIndicators]
) -> List[SearchResult]:
"""Apply heuristic filtering to identify likely technology results."""
if not indicators:
# Default indicators
positive = {"prototype", "demonstrated", "system", "device", "sensor",
"developed", "patent", "phase ii", "tested", "deployed"}
negative = {"overview", "challenge", "review", "introduction", "survey",
"future", "potential", "could", "might", "problems"}
else:
positive = set(w.lower() for w in indicators.positive)
negative = set(w.lower() for w in indicators.negative)
filtered = []
for result in results:
text = f"{result.title} {result.snippet}".lower()
# Count positive and negative indicators
pos_count = sum(1 for term in positive if term in text)
neg_count = sum(1 for term in negative if term in text)
# Include if more positive than negative, or any positive found
if pos_count > 0 and pos_count >= neg_count:
filtered.append(result)
elif pos_count > 0:
filtered.append(result)
return filtered
def _process_batch_results(
self,
results: List[SearchResult],
capability_need: CapabilityNeed,
batch_size: int
) -> Tuple[List[ExtractedTechnology], List[str]]:
"""Process results in batches using LLM."""
technologies = []
discarded = []
for i in range(0, len(results), batch_size):
batch = results[i:i + batch_size]
if len(batch) == 1:
# Single result - use single extraction
result = batch[0]
tech = self._extract_single(result, capability_need)
if tech:
technologies.append(tech)
else:
discarded.append(result.url)
else:
# Multiple results - use batch extraction
batch_techs, batch_discarded = self._extract_batch(batch, capability_need)
technologies.extend(batch_techs)
discarded.extend(batch_discarded)
return technologies, discarded
def _extract_single(
self,
result: SearchResult,
capability_need: CapabilityNeed
) -> Optional[ExtractedTechnology]:
"""Extract technology from a single result."""
prompt = self.EXTRACTION_PROMPT.format(
functional_need=capability_need.functional_need,
source_type=result.source_type,
title=result.title,
snippet=result.snippet[:500],
organization=result.organization or "Unknown"
)
response = self.client.generate(
prompt=prompt,
model=self.model,
temperature=0.1,
format="json"
)
if not response.success:
logger.warning(f"LLM extraction failed for: {result.title[:50]}")
return None
try:
data = json.loads(response.content)
except json.JSONDecodeError:
data = self.client.extract_json_from_text(response.content)
if not data:
return None
if not data.get("is_technology", False):
return None
tech_data = data.get("technology", {})
if not tech_data or not tech_data.get("name"):
return None
return self._build_extracted_technology(result, tech_data)
def _extract_batch(
self,
results: List[SearchResult],
capability_need: CapabilityNeed
) -> Tuple[List[ExtractedTechnology], List[str]]:
"""Extract technologies from a batch of results."""
# Build results text
results_text = ""
for i, result in enumerate(results, 1):
results_text += f"""
Item {i}:
Source: {result.source_type}
Title: {result.title}
Organization: {result.organization or 'Unknown'}
Content: {result.snippet[:300]}
---"""
prompt = self.BATCH_EXTRACTION_PROMPT.format(
functional_need=capability_need.functional_need,
results_text=results_text
)
response = self.client.generate(
prompt=prompt,
model=self.model,
temperature=0.1,
format="json"
)
technologies = []
discarded = []
if not response.success:
logger.warning("Batch extraction failed, falling back to individual extraction")
for result in results:
tech = self._extract_single(result, capability_need)
if tech:
technologies.append(tech)
else:
discarded.append(result.url)
return technologies, discarded
try:
data = json.loads(response.content)
except json.JSONDecodeError:
data = self.client.extract_json_from_text(response.content)
if not data:
# Fallback to individual extraction
for result in results:
tech = self._extract_single(result, capability_need)
if tech:
technologies.append(tech)
else:
discarded.append(result.url)
return technologies, discarded
extractions = data.get("extractions", [])
for extraction in extractions:
item_num = extraction.get("item_number", 0)
if 1 <= item_num <= len(results):
result = results[item_num - 1]
if extraction.get("is_technology", False):
tech_data = extraction.get("technology", {})
if tech_data and tech_data.get("name"):
tech = self._build_extracted_technology(result, tech_data)
if tech:
technologies.append(tech)
continue
discarded.append(result.url)
return technologies, discarded
def _build_extracted_technology(
self,
result: SearchResult,
tech_data: dict
) -> ExtractedTechnology:
"""Build ExtractedTechnology from extraction data."""
tech_id = str(uuid.uuid4())[:8]
# Estimate TRL from indicators
trl_indicators = tech_data.get("trl_indicators", [])
trl_estimate = self._estimate_trl(trl_indicators, result)
return ExtractedTechnology(
id=tech_id,
source_result_id=result.url,
extraction_confidence=0.8 if result.source_type in self.HIGH_TECH_LIKELIHOOD_SOURCES else 0.6,
name=tech_data.get("name", ""),
technology_type=tech_data.get("type", "system"),
description=tech_data.get("description", ""),
capabilities=tech_data.get("capabilities", []),
mechanism=tech_data.get("mechanism"),
developer=tech_data.get("developer") or result.organization,
developer_type=tech_data.get("developer_type", "unknown"),
trl_estimate=trl_estimate,
trl_evidence=trl_indicators,
source_type=result.source_type,
source_url=result.url,
source_title=result.title,
source_snippet=result.snippet,
)
def _estimate_trl(
self,
trl_indicators: List[str],
result: SearchResult
) -> Optional[int]:
"""Estimate TRL from indicators and source type."""
# Use existing TRL if available
if result.trl_estimate:
return result.trl_estimate
# Estimate from indicators
indicators_lower = " ".join(trl_indicators).lower()
if any(term in indicators_lower for term in ["deployed", "operational", "fielded"]):
return 9
elif any(term in indicators_lower for term in ["production", "qualified"]):
return 8
elif any(term in indicators_lower for term in ["phase iii", "demonstration"]):
return 7
elif any(term in indicators_lower for term in ["prototype", "phase ii"]):
return 5
elif any(term in indicators_lower for term in ["phase i", "laboratory"]):
return 4
elif any(term in indicators_lower for term in ["concept", "proof"]):
return 3
elif any(term in indicators_lower for term in ["research", "basic"]):
return 2
# Estimate from source type
source_trl_defaults = {
"sbir": 4,
"patent": 5,
"contract": 6,
"news": 5,
"web": 5,
}
return source_trl_defaults.get(result.source_type, 5)

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"""
Technology Grouper for TechScout.
Groups duplicate technologies across sources into single entities.
Uses conservative heuristic matching to avoid false merges.
This is Stage 4 of the Capability-Technology Matching pipeline.
"""
import logging
import uuid
from typing import List, Dict, Set, Tuple, Optional
from dataclasses import dataclass
from collections import defaultdict
from .types import ExtractedTechnology, GroupedTechnology, SourceEvidence, Developer
logger = logging.getLogger(__name__)
@dataclass
class SimilarityScore:
"""Similarity scores between two technologies."""
name_similarity: float # 0-1, string similarity
developer_match: bool # Same developer?
capability_overlap: float # 0-1, Jaccard similarity
description_similarity: float # 0-1, keyword overlap
overall: float # Combined score
class TechnologyGrouper:
"""
Groups extracted technologies that describe the same underlying technology.
Uses conservative matching:
- Same developer + similar name → definite merge
- Very similar name + similar capabilities → likely merge
- Otherwise → keep separate (avoid false merges)
"""
# Thresholds for grouping decisions
HIGH_CONFIDENCE_NAME_THRESHOLD = 0.85
MEDIUM_CONFIDENCE_NAME_THRESHOLD = 0.70
CAPABILITY_OVERLAP_THRESHOLD = 0.40
SAME_DEVELOPER_NAME_THRESHOLD = 0.65
def __init__(self):
pass
def group(self, technologies: List[ExtractedTechnology]) -> List[GroupedTechnology]:
"""
Group extracted technologies into unique technologies.
Args:
technologies: List of extracted technologies (may have duplicates)
Returns:
List of grouped technologies (deduplicated)
"""
if not technologies:
return []
logger.info(f"Grouping {len(technologies)} extracted technologies...")
# Build groups using union-find-like approach
groups: Dict[str, List[ExtractedTechnology]] = {}
tech_to_group: Dict[str, str] = {}
for tech in technologies:
# Find if this tech should merge with an existing group
merge_group_id = self._find_merge_candidate(tech, groups, tech_to_group)
if merge_group_id:
# Merge into existing group
groups[merge_group_id].append(tech)
tech_to_group[tech.id] = merge_group_id
else:
# Create new group
group_id = tech.id
groups[group_id] = [tech]
tech_to_group[tech.id] = group_id
# Convert groups to GroupedTechnology objects
grouped_technologies = []
for group_id, tech_list in groups.items():
grouped = self._merge_group(tech_list)
grouped_technologies.append(grouped)
logger.info(f"Grouped into {len(grouped_technologies)} unique technologies")
return grouped_technologies
def _find_merge_candidate(
self,
tech: ExtractedTechnology,
groups: Dict[str, List[ExtractedTechnology]],
tech_to_group: Dict[str, str]
) -> Optional[str]:
"""Find an existing group to merge this technology into."""
best_match_group = None
best_match_score = 0.0
for group_id, group_techs in groups.items():
# Compare against the first (canonical) tech in the group
representative = group_techs[0]
similarity = self._calculate_similarity(tech, representative)
should_merge, confidence = self._should_merge(similarity)
if should_merge and confidence > best_match_score:
best_match_group = group_id
best_match_score = confidence
return best_match_group
def _calculate_similarity(
self,
tech1: ExtractedTechnology,
tech2: ExtractedTechnology
) -> SimilarityScore:
"""Calculate similarity between two technologies."""
# Name similarity (fuzzy string match)
name_sim = self._string_similarity(tech1.name, tech2.name)
# Developer match
dev1 = (tech1.developer or "").lower().strip()
dev2 = (tech2.developer or "").lower().strip()
developer_match = False
if dev1 and dev2:
developer_match = (
dev1 == dev2 or
self._string_similarity(dev1, dev2) > 0.8 or
dev1 in dev2 or
dev2 in dev1
)
# Capability overlap (Jaccard similarity)
caps1 = set(self._extract_keywords(tech1.capabilities))
caps2 = set(self._extract_keywords(tech2.capabilities))
cap_overlap = self._jaccard_similarity(caps1, caps2)
# Description similarity (keyword overlap)
desc1_words = set(self._extract_keywords([tech1.description]))
desc2_words = set(self._extract_keywords([tech2.description]))
desc_sim = self._jaccard_similarity(desc1_words, desc2_words)
# Overall score
if developer_match:
overall = 0.5 * name_sim + 0.3 * cap_overlap + 0.2 * desc_sim
else:
overall = 0.6 * name_sim + 0.25 * cap_overlap + 0.15 * desc_sim
return SimilarityScore(
name_similarity=name_sim,
developer_match=developer_match,
capability_overlap=cap_overlap,
description_similarity=desc_sim,
overall=overall
)
def _should_merge(self, similarity: SimilarityScore) -> Tuple[bool, float]:
"""
Decide whether to merge based on similarity scores.
Returns (should_merge, confidence)
"""
# High confidence: Same developer + reasonably similar name
if similarity.developer_match and similarity.name_similarity > self.SAME_DEVELOPER_NAME_THRESHOLD:
return True, 0.9
# High confidence: Very similar names
if similarity.name_similarity > self.HIGH_CONFIDENCE_NAME_THRESHOLD:
return True, 0.85
# Medium confidence: Similar name + good capability overlap
if (similarity.name_similarity > self.MEDIUM_CONFIDENCE_NAME_THRESHOLD and
similarity.capability_overlap > self.CAPABILITY_OVERLAP_THRESHOLD):
return True, 0.7
# Same developer but different names - might be related but keep separate
if similarity.developer_match and similarity.name_similarity > 0.5:
# Only merge if capabilities strongly overlap
if similarity.capability_overlap > 0.6:
return True, 0.6
return False, 0.0
def _merge_group(self, technologies: List[ExtractedTechnology]) -> GroupedTechnology:
"""Merge multiple extracted technologies into one grouped technology."""
if len(technologies) == 1:
tech = technologies[0]
return self._single_to_grouped(tech)
# Choose canonical name (most specific/complete)
canonical_name = self._choose_canonical_name([t.name for t in technologies])
# Collect alternate names
alternate_names = list(set(
t.name for t in technologies if t.name != canonical_name
))
# Choose best technology type
type_counts = defaultdict(int)
for t in technologies:
type_counts[t.technology_type] += 1
technology_type = max(type_counts, key=type_counts.get) if type_counts else "system"
# Merge descriptions (choose longest/most detailed)
descriptions = [t.description for t in technologies if t.description]
description = max(descriptions, key=len) if descriptions else ""
# Merge capabilities (deduplicate)
all_capabilities = []
seen_caps = set()
for t in technologies:
for cap in t.capabilities:
cap_lower = cap.lower()
if cap_lower not in seen_caps:
seen_caps.add(cap_lower)
all_capabilities.append(cap)
# Merge mechanisms
mechanisms = [t.mechanism for t in technologies if t.mechanism]
mechanism = max(mechanisms, key=len) if mechanisms else None
# Collect developers
developers = []
seen_devs = set()
for t in technologies:
if t.developer and t.developer.lower() not in seen_devs:
seen_devs.add(t.developer.lower())
developers.append(Developer(
name=t.developer,
type=t.developer_type or "unknown"
))
# Best TRL estimate (highest confidence based on source type)
trl_estimate = None
trl_confidence = 0.0
trl_evidence = []
for t in technologies:
if t.trl_estimate:
# SBIR/patent sources have higher confidence
conf = 0.9 if t.source_type in ("sbir", "patent") else 0.7
if conf > trl_confidence:
trl_estimate = t.trl_estimate
trl_confidence = conf
trl_evidence.extend(t.trl_evidence)
trl_evidence = list(set(trl_evidence))
# Build source evidence
sources = []
for t in technologies:
sources.append(SourceEvidence(
source_type=t.source_type,
source_name=t.source_type.upper(),
title=t.source_title,
url=t.source_url,
snippet=t.source_snippet,
contribution=self._determine_contribution(t),
))
return GroupedTechnology(
id=str(uuid.uuid4())[:8],
canonical_name=canonical_name,
alternate_names=alternate_names,
technology_type=technology_type,
description=description,
capabilities=all_capabilities,
mechanism=mechanism,
developers=developers,
trl_estimate=trl_estimate,
trl_confidence=trl_confidence,
trl_evidence=trl_evidence,
sources=sources,
source_count=len(sources),
grouping_confidence=0.8 if len(technologies) > 1 else 1.0,
grouped_from=[t.id for t in technologies],
)
def _single_to_grouped(self, tech: ExtractedTechnology) -> GroupedTechnology:
"""Convert a single extracted technology to grouped format."""
developers = []
if tech.developer:
developers.append(Developer(
name=tech.developer,
type=tech.developer_type or "unknown"
))
source = SourceEvidence(
source_type=tech.source_type,
source_name=tech.source_type.upper(),
title=tech.source_title,
url=tech.source_url,
snippet=tech.source_snippet,
contribution=self._determine_contribution(tech),
)
return GroupedTechnology(
id=tech.id,
canonical_name=tech.name,
alternate_names=[],
technology_type=tech.technology_type,
description=tech.description,
capabilities=tech.capabilities,
mechanism=tech.mechanism,
developers=developers,
trl_estimate=tech.trl_estimate,
trl_confidence=0.7,
trl_evidence=tech.trl_evidence,
sources=[source],
source_count=1,
grouping_confidence=1.0,
grouped_from=[tech.id],
)
def _choose_canonical_name(self, names: List[str]) -> str:
"""Choose the most specific/complete name as canonical."""
if not names:
return "Unknown Technology"
# Prefer longer, more specific names
# But penalize overly long names (likely full titles, not tech names)
def name_score(name: str) -> float:
length = len(name)
if length < 5:
return 0.3
elif length > 100:
return 0.4
elif length > 50:
return 0.6
else:
return 0.8 + (length / 100)
return max(names, key=name_score)
def _determine_contribution(self, tech: ExtractedTechnology) -> str:
"""Determine what this source contributes to the technology profile."""
contributions = []
if tech.source_type == "sbir":
contributions.append("R&D funding")
contributions.append("development status")
elif tech.source_type == "patent":
contributions.append("technical claims")
contributions.append("innovation details")
elif tech.source_type == "contract":
contributions.append("government interest")
contributions.append("deployment status")
elif tech.source_type == "news":
contributions.append("recent developments")
else:
contributions.append("general information")
if tech.developer:
contributions.append("developer info")
return ", ".join(contributions)
def _string_similarity(self, s1: str, s2: str) -> float:
"""Calculate string similarity using token-based comparison."""
if not s1 or not s2:
return 0.0
# Normalize
s1 = s1.lower().strip()
s2 = s2.lower().strip()
if s1 == s2:
return 1.0
# Token-based comparison
tokens1 = set(s1.split())
tokens2 = set(s2.split())
# Remove common words
stopwords = {'the', 'a', 'an', 'for', 'of', 'and', 'in', 'to', 'with'}
tokens1 -= stopwords
tokens2 -= stopwords
if not tokens1 or not tokens2:
return 0.0
# Jaccard similarity of tokens
intersection = len(tokens1 & tokens2)
union = len(tokens1 | tokens2)
return intersection / union if union > 0 else 0.0
def _jaccard_similarity(self, set1: Set[str], set2: Set[str]) -> float:
"""Calculate Jaccard similarity between two sets."""
if not set1 or not set2:
return 0.0
intersection = len(set1 & set2)
union = len(set1 | set2)
return intersection / union if union > 0 else 0.0
def _extract_keywords(self, texts: List[str]) -> List[str]:
"""Extract keywords from a list of texts."""
stopwords = {
'the', 'a', 'an', 'is', 'are', 'was', 'were', 'be', 'been',
'being', 'have', 'has', 'had', 'do', 'does', 'did', 'will',
'would', 'could', 'should', 'may', 'might', 'must', 'shall',
'can', 'need', 'for', 'of', 'to', 'in', 'on', 'at', 'by',
'from', 'with', 'and', 'or', 'not', 'that', 'this', 'it',
'as', 'its', 'also', 'than', 'such', 'into', 'which'
}
keywords = []
for text in texts:
if not text:
continue
words = text.lower().split()
for word in words:
word = word.strip('.,;:!?()[]{}"\'-')
if word and len(word) > 2 and word not in stopwords:
keywords.append(word)
return keywords

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"""
Type definitions for Technology Extraction, Grouping, and Evaluation.
These types represent the technology-centric data model that replaces
the document-centric search result model.
"""
from dataclasses import dataclass, field
from typing import List, Dict, Any, Optional, Literal
@dataclass
class SourceEvidence:
"""Evidence from a single source contributing to a technology."""
source_type: str # sbir, patent, contract, news, web
source_name: str # Display name of the source
title: str # Title of the source document
url: str # URL to the source
snippet: str # Relevant excerpt
contribution: str # What this source contributes (e.g., "funding info", "technical details")
published_date: Optional[str] = None
award_amount: Optional[float] = None
award_id: Optional[str] = None
patent_number: Optional[str] = None
def to_dict(self) -> Dict[str, Any]:
return {
"source_type": self.source_type,
"source_name": self.source_name,
"title": self.title,
"url": self.url,
"snippet": self.snippet,
"contribution": self.contribution,
"published_date": self.published_date,
"award_amount": self.award_amount,
"award_id": self.award_id,
"patent_number": self.patent_number,
}
@dataclass
class Developer:
"""Organization developing a technology."""
name: str
type: Literal["company", "university", "government", "lab", "unknown"] = "unknown"
location: Optional[str] = None
def to_dict(self) -> Dict[str, Any]:
return {
"name": self.name,
"type": self.type,
"location": self.location,
}
@dataclass
class ExtractedTechnology:
"""
A technology extracted from a single search result.
This is the output of Stage 3 (Technology Extraction).
Multiple ExtractedTechnology instances may be grouped into
a single GroupedTechnology if they describe the same tech.
"""
# Extraction metadata
id: str # Unique ID for this extraction
source_result_id: str # Links back to original SearchResult
extraction_confidence: float # 0-1, how confident is extraction
# Technology identity
name: str # Specific technology name
technology_type: str # sensor, algorithm, material, system, etc.
# Technology details
description: str # What it is (1-2 sentences)
capabilities: List[str] # What it does
mechanism: Optional[str] = None # How it works (if described)
# Developer
developer: Optional[str] = None # Organization name
developer_type: Optional[str] = None
# Maturity
trl_estimate: Optional[int] = None
trl_evidence: List[str] = field(default_factory=list)
# Source info
source_type: str = ""
source_url: str = ""
source_title: str = ""
source_snippet: str = ""
def to_dict(self) -> Dict[str, Any]:
return {
"id": self.id,
"source_result_id": self.source_result_id,
"extraction_confidence": self.extraction_confidence,
"name": self.name,
"technology_type": self.technology_type,
"description": self.description,
"capabilities": self.capabilities,
"mechanism": self.mechanism,
"developer": self.developer,
"developer_type": self.developer_type,
"trl_estimate": self.trl_estimate,
"trl_evidence": self.trl_evidence,
"source_type": self.source_type,
"source_url": self.source_url,
"source_title": self.source_title,
"source_snippet": self.source_snippet,
}
@dataclass
class GroupedTechnology:
"""
A technology consolidated from multiple sources.
This is the output of Stage 4 (Technology Grouping).
Merges information from multiple ExtractedTechnology instances
that describe the same underlying technology.
"""
# Identity
id: str
canonical_name: str # Best name for this technology
alternate_names: List[str] = field(default_factory=list)
technology_type: str = ""
# Merged information
description: str = "" # Best/combined description
capabilities: List[str] = field(default_factory=list)
mechanism: Optional[str] = None
# Developers
developers: List[Developer] = field(default_factory=list)
# Maturity (highest confidence estimate)
trl_estimate: Optional[int] = None
trl_confidence: float = 0.0
trl_evidence: List[str] = field(default_factory=list)
# Source evidence
sources: List[SourceEvidence] = field(default_factory=list)
source_count: int = 0
# Grouping metadata
grouping_confidence: float = 1.0
grouped_from: List[str] = field(default_factory=list) # IDs of ExtractedTechnologies
def to_dict(self) -> Dict[str, Any]:
return {
"id": self.id,
"canonical_name": self.canonical_name,
"alternate_names": self.alternate_names,
"technology_type": self.technology_type,
"description": self.description,
"capabilities": self.capabilities,
"mechanism": self.mechanism,
"developers": [d.to_dict() for d in self.developers],
"trl_estimate": self.trl_estimate,
"trl_confidence": self.trl_confidence,
"trl_evidence": self.trl_evidence,
"sources": [s.to_dict() for s in self.sources],
"source_count": self.source_count,
"grouping_confidence": self.grouping_confidence,
"grouped_from": self.grouped_from,
}
@dataclass
class CriterionResult:
"""Result of evaluating a technology against a single criterion."""
criterion: str
weight: str # must_have, should_have, nice_to_have
assessment: Literal["SUPPORTS", "PARTIAL", "DOES_NOT_SUPPORT", "UNKNOWN"]
evidence: str # Why this assessment
source: Optional[str] = None # Which source provided evidence
def to_dict(self) -> Dict[str, Any]:
return {
"criterion": self.criterion,
"weight": self.weight,
"assessment": self.assessment,
"evidence": self.evidence,
"source": self.source,
}
@dataclass
class CapabilityMatch:
"""
Assessment of how well a technology matches the capability need.
This is the output of Stage 5 (Capability Fit Evaluation).
"""
# Overall assessment
overall_fit: Literal["HIGH", "MEDIUM", "LOW", "UNCERTAIN"]
fit_score: int # 0-100
# Per-criterion evaluation
criteria_results: List[CriterionResult] = field(default_factory=list)
# Narrative
how_it_addresses_need: str = "" # 2-3 sentences
key_strengths: List[str] = field(default_factory=list)
key_limitations: List[str] = field(default_factory=list)
key_unknowns: List[str] = field(default_factory=list)
# Recommendation
investigation_worthy: bool = False
investigation_rationale: str = ""
def to_dict(self) -> Dict[str, Any]:
return {
"overall_fit": self.overall_fit,
"fit_score": self.fit_score,
"criteria_results": [c.to_dict() for c in self.criteria_results],
"how_it_addresses_need": self.how_it_addresses_need,
"key_strengths": self.key_strengths,
"key_limitations": self.key_limitations,
"key_unknowns": self.key_unknowns,
"investigation_worthy": self.investigation_worthy,
"investigation_rationale": self.investigation_rationale,
}
@dataclass
class EvaluatedTechnology:
"""
A technology with capability fit assessment.
This is the final output entity that combines:
- Technology information (from GroupedTechnology)
- Capability match assessment (from evaluation)
"""
technology: GroupedTechnology
capability_match: CapabilityMatch
def to_dict(self) -> Dict[str, Any]:
tech_dict = self.technology.to_dict()
tech_dict["capability_match"] = self.capability_match.to_dict()
return tech_dict