Tech2Transfer

Tech2Transfer targets a well-known bottleneck in innovation: the huge volume of published research versus the small fraction that ever becomes a commercial product. The platform ingests manuscripts and technical documents, extracts their text, and runs an LLM-driven evaluation against technology-transfer criteria such as commercial viability, intellectual-property strength, scalability, and market potential.

Technically, the service uses a retrieval-augmented generation (RAG) pipeline. Uploaded PDFs are parsed, split into small chunks (roughly 1,000 tokens each) tagged with page and section metadata, embedded with Sentence-BERT, and stored in a vector database. When the agent needs to check something specific, it embeds a query, retrieves the most relevant chunks by similarity, and feeds that focused context to the language model — which keeps analysis accurate while avoiding the cost of processing a whole paper at once.

The product positions itself as a bridge between research institutions and the commercial world, with a stated roadmap toward intelligent matchmaking that connects researchers with investors, industry partners, and licensing opportunities. Pricing tiers are published on the site, and new users can try the assessment flow before subscribing.