AI and machine learning

Retrieval-augmented generation (RAG)

Answering from your own documents by retrieving the relevant passages first, so the answer can cite where it came from.

What we reach for first

When we reach for it

Knowledge that changes, is private, or must be attributable — support material, policies, contracts, internal documentation. Citations are why this is the default for the work: an answer a reader can open and check is a different product from an answer they are asked to trust.

When we would argue against it

Small, stable corpora. If the whole body of knowledge fits in a prompt, a retrieval layer is machinery earning nothing. And where the documents contradict one another, retrieval surfaces the contradiction rather than resolving it — a content problem wearing a technical costume.

What it looks like in delivery

Chunking and retrieval evaluated separately from generation, because most bad answers are retrieval failures wearing a model’s voice. Retrieval is scored against a query set drawn from what people actually asked, and “I do not have that” is a designed answer rather than a failure.

Where this appears on the site

Nothing on this site names it yet

We work in Retrieval-augmented generation, and no case study or service page currently published on this site prints it in its stack. Rather than describe an engagement you cannot check, this space stays empty until one does. Ask us and we will talk you through it directly.

Working in Retrieval-augmented generation?

Tell us what it is running, what it costs you today, and what you need it to do next. A senior engineer will tell you what we would keep and what we would change.