RAG development and AI knowledge systems
Retrieval-augmented generation is what makes an AI answer from your documents instead of from general internet knowledge. Every answer cites the file it came from, so a claim can always be checked — the difference between an AI that quotes your real terms and one that confidently invents them.
From $999 · most projects land $999 – $6,500
Includes ingestion, retrieval, evaluation and one interface. Larger or regulated corpora are quoted after a content audit. Larger scopes — more channels, integrations, languages or compliance work — are quoted above that band after the discovery call.
What we actually build
Retrieval-augmented generation grounds every answer in your own documents, with citations, so nothing is invented. We handle ingestion, chunking, embeddings, vector search, re-ranking and evaluation — then wire it into whatever interface your team already uses.
Answers with citations
Every response points back to the source document, page and clause, so your team can verify in one click instead of trusting a paragraph.
A pipeline built for messy reality
Scanned PDFs, twelve versions of the same policy, spreadsheets with merged cells. Ingestion, chunking, embedding and re-ranking are tuned to your corpus, not to a demo.
Measured, not assumed
We build an evaluation set from your own questions and score retrieval and answer quality against it, so accuracy is a number you can watch rather than a feeling.
Who this is for
Professional services
Contracts, precedents and past matters searchable in plain language.
Manufacturing
Machine manuals, SOPs and safety documents answered on the shop floor.
Finance and insurance
Policy wording and compliance rules with the clause attached to every answer.
Support organisations
Ten years of tickets turned into an answer engine for new agents.
RAG & Knowledge Systems — common questions
What is RAG, in one sentence?
Retrieval-augmented generation means the system first retrieves the relevant passages from your own documents, then asks the language model to answer using only those passages — so the answer is grounded in your material and can be cited.
Does our data leave our environment?
Only if you want it to. We can run ingestion and vector storage inside your own cloud account, and for regulated workloads the whole pipeline including the model can be deployed in your environment.
How much content do we need before this is worth it?
The threshold is repetition, not volume. A few hundred pages that people search every day pays back faster than a ten-thousand-page archive nobody opens.
Thirty minutes. One honest answer.
Tell us what is slowing your business down. You leave with a written recommendation and a fixed price — even if the answer is that you do not need us yet.
No sales deck. No obligation. Prefer email? [email protected]