Retrieval-Augmented Generation (RAG)
Every enterprise has the same two facts: the answers employees need are buried in a million documents, and the chatbot someone piloted last year confidently made things up. RAG done properly fixes both — the model answers from your documents, cites its sources, and says "I don't know" when the corpus doesn't know.
Done properly is the catch. Naive RAG — chunk everything at 512 tokens, embed, pray — demos well and fails in production on the questions that matter. We tune the unglamorous parts: document-aware chunking, hybrid retrieval with reranking, metadata filtering, permission-aware search so people only retrieve what they're allowed to read, and eval suites that measure answer quality against ground truth instead of adjectives.
Production RAG pipelines
Ingestion, chunking, retrieval, and generation tuned to your corpus — not defaults from a tutorial.
Citation-backed answers
Every claim traceable to a source document. Trust is a feature; we build it in.
Permission-aware retrieval
ACL-filtered search so the model never surfaces a document the user couldn't open.
Retrieval evals
Recall, precision, and answer-quality metrics on golden question sets — measured, not vibed.
Continuous ingestion
Pipelines that keep the index fresh as your documents change, without full re-embeds.
Our pilot RAG bot hallucinated. Why would yours be different?
Because most hallucination in RAG systems is a retrieval problem wearing a model costume. When retrieval returns the right passages, grounded prompting plus citation requirements keeps generation honest. We fix retrieval first, measure faithfulness with evals, and gate deployment on the numbers.
Fine-tuning or RAG for our internal knowledge?
RAG, almost always. Your knowledge changes daily; retraining a model daily is madness. RAG updates when a document updates, cites sources, and respects permissions. Fine-tuning earns a place for style and format, not for facts.
How long to a production RAG system?
A grounded, cited, permission-aware assistant over one meaningful corpus typically ships in four to six weeks — including the eval suite that proves it works. The demo takes a day. The other five weeks are why it survives production.
Put Retrieval-Augmented Generation (RAG) to work — in production.
One forward-deployed engineer, embedded in your stack, owning the outcome from discovery to production. Weeks, not quarters.
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