Valency provides a secure, AI‑powered search platform that lets researchers query tens of millions of scientific papers and preprints with fast, token‑efficient semantic search and citation‑graph tools. By integrating the service directly into institutional environments, it enables R&D teams to generate well‑grounded reading lists and answers without leaving their secure workspace. The system is built in partnership with national laboratory researchers and continuously updates its corpus with new publications.
Funding
Funding not disclosed
Founders
Product
Problem
Researchers and R&D teams spend days manually searching and reviewing millions of scientific papers and preprints, often encountering outdated or incomplete information that slows hypothesis generation and discovery.
Solution
Valency offers an AI‑driven research platform that integrates large language models with a continuously updated corpus of tens of millions of scholarly articles. Users can perform fast, token‑efficient semantic searches and explore citation‑graph relationships to quickly assemble well‑grounded reading lists. The system operates within the user’s secure environment, keeping internal data isolated while providing up‑to‑date literature context for downstream LLM queries. By automating literature retrieval and relevance ranking, Valency reduces review time from days to minutes, enabling scientists to focus on analysis and innovation.
Target Audience
Primary users are academic researchers, corporate R&D groups, and scientific institutions that need rapid, reliable access to the latest scholarly literature for hypothesis testing and knowledge discovery.
Features
- Semantic search across a growing database of research papers and preprints, optimized for low token consumption
- Citation‑graph navigation tools that surface related works and influence pathways
- Direct integration of large language models with current scientific literature for accurate, hallucination‑free responses
- Secure, isolated deployment that keeps an organization’s internal data private while accessing global knowledge
- Real‑time indexing of newly published content to ensure the corpus stays up‑to‑date
- API and interface designed for both individual researchers and institutional R&D teams