Nextnet is an AI‑powered life sciences platform that unifies biomedical data from sources like PubMed, ChEMBL, Google Scholar and Ensembl into a large semantic web. Its Copilot assistant delivers evidence‑backed answers with citations, while the Explorer search engine visualizes connections among genes, drugs, pathways, literature and more, enabling researchers to find and share reliable information quickly.
Funding
$1.3M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Life sciences researchers spend excessive time navigating multiple databases and tools, often encountering fragmented information and AI-generated answers that lack verifiable sources, leading to inefficiencies and risk of misinformation.
Solution
Nextnet provides an AI-powered research platform that unifies biomedical data through a large semantic web and knowledge graph. Its Copilot assistant delivers evidence-backed answers by retrieving and citing verified scientific sources, eliminating hallucinations. The Explorer component offers a connected search engine that visualizes relationships among genes, drugs, pathways, literature, and more, enabling interactive map and list views. Integrated generative AI augments search results with contextual overviews, while the platform’s RAG pipeline and curated ontology ensure up-to-date, reliable information. Collaboration features allow teams to share sessions and curated sources securely, streamlining discovery and decision‑making.
Target Audience
Primary users are researchers in academia, pharmaceutical and biotechnology companies, and life‑science teams that need fast, reliable access to integrated biomedical knowledge for hypothesis generation and project planning.
Features
- Evidence‑backed AI responses with citations to primary scientific sources and excerpt snippets
- Retrieval‑augmented generation (RAG) pipeline leveraging large language models and a biomedical knowledge graph
- Unified data integration from ChEMBL, PubMed, Google Scholar, Ensembl, and additional repositories
- Interactive Explorer maps and list views that visualize connections across literature, genes, drugs, pathways, diseases, institutions, and authors
- AI‑generated overviews that expand insights on search results and suggest related entities
- Team collaboration tools for sharing sessions, managing access, and curating source collections
- Scalable cloud infrastructure with automated crawlers to keep the semantic web continuously refreshed