tech2transfer
Tech2Transfer is an AI-powered platform that analyzes scientific papers to assess technology readiness, IP strength, and market potential, helping researchers, universities, investors, and industry partners accelerate technology transfer and commercialization.
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Best for: Education & Research
What it does
Research software for decision-makers comparing workflow fit and alternatives.
Best fit
Education & Research
Pricing snapshot
Free
Next step
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tech2transfer
Tech2Transfer is an AI-driven platform that bridges the gap between scientific research and market applications by systematically analysing scientific papers, patents, and technical reports to determine commercial viability, IP strength, market potential, and scalability. The service is aimed at researchers, universities, tech transfer offices, investors, and industry partners seeking to surface, evaluate, and commercialize breakthrough innovations. The platform uses Large Language Models and a Retrieval-Augmented Generation (RAG) pipeline to ingest PDFs, parse and chunk text, embed segments, and produce focused LLM analyses and structured technology transfer evaluation reports.
AI platform analyzing scientific papers for tech readiness and market potential.
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Claim this listing for $29Key Features
AI-Powered Research Analysis
Integrates LLMs and machine learning models to evaluate research papers, patents, and technical reports for commercial viability and technology readiness.
Automated Technology Assessment
Generates structured reports on Tech Transfer Evaluation Criteria, assessing scalability, IP strength, and market potential.
Patent Landscape Analysis
Identifies existing intellectual property challenges to inform commercialization and licensing strategies.
Document Ingestion & Parsing
Accepts PDF uploads via web interface or API, extracts text using pdf-parse, and assigns paper_id for traceability.
Chunking & Embedding (RAG)
Splits documents into chunks (~1,000 tokens), embeds segments using Sentence-BERT, stores embeddings in a vector database, and retrieves relevant chunks for focused LLM prompts.
Retrieval and Contextual LLM Analysis
Performs similarity search on embeddings to form context prompts for the LLM, improving accuracy and efficiency when analyzing long documents.
Pricing
Free trial available (site shows 'Try it for free')
Use Cases
Researcher / University Tech Transfer
Evaluate and surface research projects with commercial potential, prepare structured reports for licensing or spinout formation, and increase visibility to industry partners.
Investor & Venture Scouting
Scan and prioritize scientific papers and patents for investment opportunities by assessing market potential and IP strength.
Industry Partner Sourcing
Identify relevant academic innovations for licensing, partnership, or acquisition by matching research outputs to industry needs.
IP Landscape Assessment
Quickly discover potential IP conflicts and existing patents impacting commercialization strategy.
Integrations
DeepSeek R1
Platform is powered by DeepSeek's R1 model for autonomous AI agent analysis (mentioned on site).
Sentence-BERT embeddings
Embeddings for chunked text are produced using Sentence-BERT and stored in a vector database for retrieval.
pdf-parse
Text extraction from PDFs uses the pdf-parse library as part of document ingestion.
Google sign-in
Site includes a 'Continue with Google' option for account sign-in.
Benefits
Limitations
Frequently Asked Questions
No verified FAQs are available.
Getting Started
- 1 Step 1: Visit the Tech2Transfer site and create an account or continue with Google (option shown on site)
- 2 Step 2: Upload your scientific paper (PDF supported) via the web interface or use the API for document ingestion
- 3 Step 3: Receive an AI-generated Tech Transfer Evaluation report and review assessments for IP, market potential, and commercialization recommendations
Support
Support (site link)
Access support using the 'Support' link provided in the site's navigation/footer.
Legal & Policies
Privacy policy and Terms of services are available via links in the site footer.
API
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