RAG That Actually Retrieves the Right Thing.
Naïve cosine similarity got you 60% accuracy and you're stuck. We build retrieval systems that combine hybrid search, reranking, and structured context — so the LLM has what it needs.
Retrieval is engineering, not vibes.
RAG is "retrieve relevant context, then generate an answer with it." Sounds simple. The gap between a weekend prototype and a system editors and analysts trust is measured in months of evaluation work.
We build that production layer: chunking strategies tuned to your content, hybrid retrieval (vector + lexical), rerankers, and citation systems that hold up to scrutiny.
You need this if…
- You have a corpus of documents (contracts, support docs, editorial archive) and want grounded Q&A over it.
- Your existing RAG hallucinates or returns the wrong document on long-tail queries.
- You need citations and provenance for compliance or editorial reasons.
- You want internal teams to stop searching and start asking.
How We Build It
Eval first
We build a question/answer eval set from real users before we touch the index. No eval, no science.
Tune retrieval
Chunking, hybrid search, reranking — iterated against the eval until quality compounds.
Deploy with citations
Production endpoint with sub-second p95, citations, and per-tenant isolation.
Tools We Reach For
A pragmatic stack — not a fashion show. We pick what scales.
Recent Engagements
Knowledge Layer for a Media Co.
Indexed 18 years of editorial content. Editors draft researched articles in minutes; citations link to original sources.
Contract Q&A for an Enterprise Legal Team
Cross-corpus retrieval over thousands of negotiated contracts. Counsel gets clause-level answers with citations.
Have a corpus your team should be talking to?
Bring us the documents. We'll bring the retrieval engine.
Book a Strategy Call →