Tech2Transfer — AI-powered Tech Transfer Analysis

Tech2Transfer is an AI-driven platform designed to bridge the gap between cutting-edge scientific research and real-world market applications. By analyzing scientific papers, patents, and technical reports, it evaluates commercial viability, IP strength, market potential, and scalability to accelerate technology transfer and commercialization.

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Overview

Tech2Transfer helps researchers, investors, and industry partners quickly identify and de-risk breakthrough innovations. It combines automated document ingestion, chunk-based analysis, and retrieval-augmented generation (RAG) to produce structured assessments that inform funding, partnerships, and licensing decisions.

How It Works

• Upload PDFs via the web interface or API. Each document is assigned a paper_id for traceability.
• Text is extracted from PDFs using a dedicated parsing library.
• Documents are chunked into ~1,000-token segments and embedded into a vector store using Sentence-BERT.
• When evaluation is needed, a focused context is retrieved by similarity search and fed to the LLM to generate a Tech Transfer Evaluation report.
• Generated insights include commercial viability, IP landscape, market potential, and scalability.

AI Agent Design

Tech2Transfer uses a multi-layered architecture to ingest, parse, and evaluate manuscripts, producing structured reports on Tech Transfer Evaluation Criteria. The key components include input parsing, chunking, embedding, retrieval, and iterative model improvement.

2.1 Input & Parsing

• Manuscripts are uploaded as PDFs via web or API.
• Each document is assigned a paper_id for traceability.
• Text extraction converts manuscripts into raw text suitable for analysis.

2.2 Chunking & Embedding (RAG Approach)

• Chunking: Text is split into segments (often ~1,000 tokens) based on paragraphs, sections, or page boundaries.
• Metadata: Each chunk includes page range, section header, and paper_id.
• Embedding & Indexing: Chunks are embedded with the Sentence-BERT model and stored in a vector database.
• Retrieval: An embedded query retrieves relevant chunks; top N chunks form the context prompt for the LLM.
• The RAG approach improves accuracy (relevant text) and efficiency (smaller prompts).

Roadmap

• Phase 1: Platform development, AI model training for research assessment, and a beta launch with universities and investors.
• Phase 2: Intelligent Matchmaking — connect researchers with relevant stakeholders (Investors, Venture Capitals, Industry Partners).
• Licensing Opportunities: Facilitate connections between R&D teams and potential licensors worldwide.
• Geographic expansion into European and North American research and Tech Transfer institutions.

Conclusion

Tech2Transfer accelerates global science and technology by increasing visibility of research and streamlining the evaluation and funding process. It aims to connect researchers with investors and industry partners to drive commercialization.

Tech2Transfer is powered by DeepSeek's R1 model and is built with Next.js. It supports PDF document ingestion, robust chunking, and retrieval-augmented analysis to produce actionable tech transfer insights.

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How to Use Tech2Transfer

1. Upload your document. Upload a research paper, patent, or technical report (PDF). Each document is assigned a paper_id for tracking.
2. Review the Tech Transfer Evaluation. The platform processes the document, performs RAG-based analysis, and returns a structured report outlining commercial viability, IP landscape, market potential, and scalability.
3. Export & Collaborate. Export reports for investors, licensing teams, or internal stakeholders and initiate matchmaking workflows.

Safety & Compliance

• The platform is designed for research and business evaluation purposes. Users should validate insights with domain experts before making funding or licensing decisions.

Core Features

• AI-powered assessment of research papers, patents, and technical reports
• Retrieval-Augmented Generation (RAG) for efficient, focused analysis
• Automated document ingestion from PDF via web/API
• Chunk-based processing with vector embeddings (Sentence-BERT)
• Structured reporting: commercial viability, IP strength, market potential, scalability
• Stakeholder matchmaking capabilities for researchers, investors, and industry partners
• Beta-ready platform with university and investor partnerships
• Web-based interface built with modern frameworks (Next.js)