Your company knowledge, answered with citations — a production RAG pipeline with permissions, evals, and monitoring.
The opposite of chat-with-docs: a knowledge system tuned against a golden set you own, so answers are traceable, scoped, and get better instead of silently rotting.
We map your sources and build a golden dataset — the questions that must be answered right — before writing a line of retrieval.
Ingestion, chunking, embeddings, hybrid retrieval, and reranking — tuned against the golden set, not vibes.
A citation format, a permissions model, and the guardrails that keep retrieval honest and scoped.
The eval harness, a feedback loop, and monitoring — so retrieval improves over time instead of rotting.
Your knowledge stays fresh and chunked so retrieval actually finds the right passage.
What you receive
The pipeline that turns your knowledge into cited answers, the permissions that keep it honest, and the evals that keep it improving. A sample is shown; yours runs on your corpus.
The model answers from your sources, with inline provenance — and refuses when it can't.
sources · billing-policy.md §4 · msa.pdf p.12 · confidence 0.91
Recall and precision against a golden dataset, tracked over time.
Ingestion to answer, every stage tuned against the golden set.
What retrieval quality looks like after the feedback loop kicks in.
Answers with citations your team trusts — not a search box that returns ten links.
The retrieval layer a custom agent needs to answer from your data reliably.
Permissioned, cited retrieval that respects who's allowed to see what.
We scope the exact number with you, and sign a mutual NDA before any code or data is shared.
Build logs, agentic engineering decisions, agent failures, evals, and what survives real users. Sent weekly, never more.