Cognesys turns your documents into grounded, cited answers — fusing vector, keyword, and knowledge-graph retrieval, scoring every response across five dimensions, and running A/B experiments that promote the winners automatically. Bring your own models.
Bring your own keys Encrypted at rest Per-user isolation No credit card
Reciprocal-rank fusion combines the dense and keyword result lists by summing1the reciprocals of each document’s rank, so passages strong in either signal rise to the top2.
Each layer is observable, swappable, and scoped to your data.
Dense vectors (Qdrant) fused with BM25 keyword search via reciprocal-rank fusion — semantic recall and exact-match precision in a single query.
A neural reranker re-scores the top candidates so the most relevant passages reach the model — not just the closest embeddings.
Build knowledge graphs from your documents, then let entity and relationship links surface connected context that pure vector search misses.
Multi-turn chat that rewrites each follow-up against the conversation, so questions like “what about its pricing?” just work.
Every answer is scored for relevance, hallucination, citation, quality, and context — automatically, in parallel, on every response.
Evaluation trends spin up A/B experiments and promote the winning configuration on their own. Your retrieval stack tunes itself.
OpenAI, Anthropic, Google Gemini, Azure, Cohere, or local Ollama — for both LLMs and embeddings. Keys are encrypted at rest, scoped to your account.
Every query flows through these stages — and every answer feeds the next improvement cycle.
Switch providers per user from Settings — no redeploy. Your API keys are Fernet-encrypted and used only for your own queries.
Create an account, connect a model, and upload your docs. Cognesys retrieves, evaluates, and improves — automatically.