Fix broken RAG retrieval and ship document AI that shows its sources — with citation accuracy you can measure, not just hope for.
Every answer is graded for faithfulness, context precision, and citation accuracy — before your users ever see it. RAGAS metrics drive every decision.
See exactly which chunks were retrieved, reranked, and used — and trace why a wrong answer happened. No more guessing which layer failed.
Citation regressions block deploys like failing unit tests. Faithfulness drops below 0.90? The pipeline won't ship. Hallucinations never reach production.
Most "RAG fixes" tweak prompts and hope. We rebuild the four layers that actually determine answer quality.
Vector search misses exact identifiers — product codes, error numbers, policy references. We combine semantic + keyword search so nothing falls through.
Retrieve 50 candidates, rerank down to the best 5. Better accuracy, lower token cost, fewer tokens for the model to get confused by.
Stale documents poison answers. Date metadata, recency filters, and retrieval-time permission checks keep citations current and compliant.
Answers carry inline citations down to the page and paragraph. A verification layer checks that each cited source actually supports the claim — fabricated citations get flagged, not shipped.
Every factual claim links to its source document, page, and section. Users can verify any answer in one click.
An automated checker confirms each citation actually supports its claim. Accuracy rate reported on every answer.
When your documents don't contain the answer, the system says "I don't know" — measured as a feature, not a failure.
Our evaluation harness turns "the chatbot feels wrong" into numbers your stakeholders can sign off on. Baseline score, prioritized fixes, and a verified after-state — in weeks, not months.
50–200 real questions run against your current pipeline. You get a scorecard, not vibes.
Chunking, hybrid search, reranking, metadata — ranked by impact on your score.
Same questions, same grading, new numbers. Proof you can put in front of a client or board.
Enforced at retrieval time — never in the prompt
Deploy in your region, your cloud, your rules
Documents never train external models
Enterprise identity, audit logs included
Permission filters enforced at retrieval time — never in the prompt. Audit logs, SSO, and data residency options for regulated teams.
"Our RAG chatbot was confidently citing a refund policy we'd retired eight months ago. They found it in a day, fixed the pipeline in a week, and now every answer ships with an accuracy score."
Those are frameworks — this is the finished, evaluated system built on them. You get measurable citation accuracy, not a toolkit you assemble yourself.
A golden dataset built from your real documents, baseline scores across faithfulness/precision/citation accuracy, and a prioritized fix list. Fixed price, delivered in days.
Most engagements are fixes, not rebuilds. We work inside your current stack (LangChain, LlamaIndex, custom) and replace only what's dragging the score down.
GPT-4o, Claude, Command R+, and open-source models via Ollama — chosen per workload based on measured faithfulness, not hype.
Permission filters apply at retrieval time, so the model never sees chunks a user isn't allowed to access. Zero-retention and on-prem options available.
Optional monthly retainer: continuous evaluation on live traffic, regression alerts, and a monthly accuracy report your stakeholders can actually read.