RAG & knowledge systems
Retrieval over your own corpus, measured on answer quality.
The problem
What usually brings people here.
You have twenty years of documents and a search box nobody uses. The first retrieval prototype answered beautifully on the five questions it was demoed with and fell apart on the long tail, which is where the actual value is.
What we do
How the work actually runs.
Retrieval is an information problem before it is a model problem. We look at how the corpus is actually structured, chunk and index for how people ask rather than how the documents were filed, and measure answer quality against a set your own experts graded. Citations back to the source document are a requirement, not a feature.
How this runs
Design, develop, refine.
Design
Two to four weeks. We scope with the engineers who will build it, so the estimate comes from the people held to it.
Develop
Two-week increments, demoed to you, shipped behind flags. You see working software in week three, not a status deck.
Refine
The squad stays. Lead time comes down, the backlog gets shorter, and the same people know why every decision was made.
Proof lives on the work page.
We publish numbers per engagement, cleared by that client, rather than per service line. What is written up there is what we are allowed to show.
Also in AI Engineering
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Tell us what you are trying to ship.
You will get a reply from an engineer, not a form handler. If we are not the right firm for it, we will say so and tell you who is.