Most RAG systems are never measured, which is why most of them quietly return the wrong passage. We build retrieval against a labelled set drawn from your real questions, so recall is a number you can look at. Hybrid search where keywords still matter, reranking where meaning does, chunking tuned to your documents rather than to a default, and a refusal path when nothing relevant is found.
01 / RAG
For answers that have to come from your data. Hybrid search, reranking and chunking tuned against a real eval set, built to refuse rather than improvise.
Usually shipped with
Not a bundle to buy. Whichever you start from, the engagement covers what the build actually needs.
02 / Scope
03 / Outcomes
04 / Deliverables
05 / Stack
06 / Why us
Most RAG debugging is prompt work on what is actually a search problem. Splitting the two saves weeks: if recall at ten is seventy percent, no model can exceed seventy percent, and swapping models will not help.
On Brandiligence the model may only cite firm-approved authorities, enforced by a validator rather than by instruction. A system that cannot decline is a system that will invent.
On DocVerse, filings are chunked so a metric keeps the page it came from. Character-count chunking would have severed exactly the lineage the client needed.
Week 1
Fifty to a hundred real questions with the passages that answer them. Without this there is no accuracy number, and every later change is a guess.
Week 1-3
Hybrid search, chunking fitted to your documents, and reranking, tuned until recall at k is where it needs to be. No prompt fixes a passage that was never fetched.
Week 3-4
Grounding instructions, citation format, and a refusal path when the index holds nothing relevant.
Week 4-6
Incremental reindexing, freshness, and the monitoring that tells you when retrieval quality drifts.
Market intelligenceIn production
A reasoning-first prospect intelligence engine that turns a LinkedIn or company URL into a structured, intent-aware profile. Multi-source ingestion and hybrid RAG uncover ICP fit, strategy, and risks, with every claim locked to a real source rather than invented.
ESG & sustainabilityIn production
An ESG-intelligence pipeline that ingests dense, unstructured filings at scale, extracts framework-aligned metrics with full source lineage, connects them across reports and years, and serves them through a governed analytics layer analysts can actually trust.
Legal & IPIn production
A fine-tuned LLM with retrieval over a curated trademark and IP library, built so the model can only cite firm-approved authorities and every draft holds a fixed legal structure an attorney can review and sign.
The corpus lane runs once, offline: chunk, embed, and build an index that holds both keyword and vector representations, because pure vector search misses exact terms your users actually type. The query lane runs per request: rewrite the question, retrieve candidates, rerank them with a cross-encoder, and ground the answer so every claim cites its span. Most failed RAG builds have collapsed these two into one.
Production-proven
Built by engineers who've already shipped this in production.
We cannot tell you before we measure, and anyone who does is guessing. The first two weeks produce a recall number against your own questions, and that number is the ceiling on answer accuracy.
Sometimes. Plenty of corpora are served better by a good keyword index with reranking. We pick after measuring, not before.
It refuses. That is the behavior we build for deliberately, because a system that always answers is a system that invents when it should decline.
Yes, with a data boundary in front or with open models on your own infrastructure. Both are things we build.
Document extraction and search for teams who have to show their source.
View serviceIngestion and preparation, for data that is not retrieval-ready.
View serviceEval suites and regression testing, for teams shipping on opinion instead of a number.
View serviceTell us where you are and what you're trying to ship. We'll come back with a concrete plan, the right engineers, and a path to production, not a generic pitch.