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Retrieval-augmented generation that reads your own documents, retrieves the right passages, and answers with citations you can click — not a confident guess. Built to a faithfulness target, evaluated every release, and shipped to production.
A ledger of named systems where the line that moved was a real answer, with a source — deflection, accuracy, time-to-answer, hallucinations avoided. 6 of 30 shown · ledger updates as systems scale.
Ask a question and watch the pipeline run: embed the query, pull the nearest passages from your knowledge base, rerank them so the truly-relevant one wins, then answer with citations. Toggle grounding off and the model guesses from memory — confidently, and wrong.
Quality hides in the stages between the question and the answer — how you chunk, what you embed with, whether you rerank, how you ground. This is the room we work in: each stage measured, each tool chosen for a reason.
Before a vector is embedded, we pin down what users actually ask, what counts as a correct answer, and where the truth lives. Then we build an evaluation set — the questions we'll grade every release against.
Parse the real sources — PDFs, wikis, tickets, code — into clean text, then chunk on structure so a passage holds one coherent idea. Garbage chunks retrieve garbage; this is where most RAG systems quietly fail.
Turn every chunk into a vector with a strong embedding model, then index it for fast nearest-neighbour search. Hybrid from day one — vectors for meaning, keywords for the exact term users actually type.
Top-k by similarity is a first pass, not the answer. A cross-encoder reranks the candidates, then Claude answers strictly from what survived — with native citations, and an honest "I don't know" when the passages don't support a claim.
Run the frozen eval set every change and score it — faithfulness, context recall, context precision, answer relevance. No "looks good in the demo." A number that moves, or the change doesn't ship.
Live with caching for cost, guardrails for safety, and logging on every retrieval. We watch faithfulness as the corpus grows, catch drift before users do, and keep the system answering as your data changes underneath it.
Every eval run feeds the next: failed questions sharpen the chunking, weak retrievals justify the reranker, hallucinations tighten the grounding. Ship-and-forget RAG drifts as your corpus grows stale. Evaluated and tended, faithfulness compounds.
The models, stores, and tools we actually wire together to ingest, retrieve, rerank, ground, and evaluate. No mystery framework — just the kit that keeps answers faithful.
A free retrieval audit to start — bring a stack of your real documents and a list of real questions, and we'll show you what a grounded, cited system would answer, and where today's tool falls down. A prototype, not a pitch.
Get a retrieval audit →