Models that answer from your data, with citations.
Retrieval, vector search, and knowledge graphs designed for enterprise compliance. The model answers correctly, or it says it doesn't know.
- Pilot to production
- 0-8wk
- Citation accuracy
- >0%
- Retrieval P95
- <0ms
- Hallucinations on out-of-corpus questions
- 0
Retrieval-grounded answers, not retrieval-themed hallucinations.
RAG done badly is a vector database glued to a chatbot. We design the retrieval, the chunking, the rerankers, the citation surface, and the eval suite that proves the system answers from your corpus, and refuses when it can't.
- Hybrid retrieval, dense embeddings, BM25, and reranking
- Knowledge graphs for relationship-heavy domains
- Citation-grade answers with source provenance
From discovery to production.
- 01
Discover
Audit the corpus, the questions users actually ask, and the compliance constraints. Pick the retrieval architecture that fits.
- 02
Prototype with evals
Build a labeled QA set first. The system passes when answers cite the right sources, not just when they sound good.
- 03
Deploy
Shipped to your cloud with chunking, embedding, retrieval, and answer generation all behind your auth boundary.
- 04
Operate
Continuous evaluation against new traffic, drift detection, and re-indexing pipelines that keep the system fresh.
RAG demo working in the room but failing in production?
Book a 30-min consultWhat you get.
Refusal is a feature.
We tune the system to say 'I don't know' when the corpus doesn't support the answer. We measure citation accuracy, not just answer plausibility. We deploy on your cloud so the corpus never leaves your perimeter.
- Tuneable refusal thresholds, fewer hallucinations, fewer false refusals
- Source-attribution evals, does the cited passage actually support the answer?
- Bring-your-own-VPC; corpus indexing happens in your infrastructure
Common questions.
Build a RAG system your compliance team will sign.
Free 30-minute consultation. We'll tell you whether your corpus is ready.
Schedule consultation