AI & RAG Systems
Production RAG and agent systems grounded in your own data, with retrieval quality, evaluation, and cost per query treated as engineering problems.
What this actually involves
Retrieval-augmented generation is an engineering problem, not a prompt. Answer quality is set by chunking, embedding, retrieval, and reranking long before the model sees anything.
We build the pipeline with evaluation attached from the start, so changes can be measured instead of guessed at, and so cost per query stays a number you control.
What you end up with
- An ingestion and embedding pipeline over your sources
- Hybrid retrieval with reranking
- An evaluation harness with regression tests
- Citations, guardrails, and defined fallback behaviour
In scope
The concrete pieces of work this engagement covers.
Ingestion, chunking, and embedding pipelines
Hybrid retrieval and reranking
Evaluation harness and regression suites
Guardrails, citations, and fallback handling
Questions we get asked
How do we know the answers are correct?
You measure them. We build an evaluation set from real questions and score retrieval and generation separately, so you can see which half is failing.
Does our data go to a model provider?
Only if you choose that. We can deploy against a private or in-VPC model where data residency requires it.
Can it say it does not know?
That is a design requirement, not a nice-to-have. Abstention and citation behaviour are specified and tested.
Related services
Need ai & rag systems?
Send a short brief. You will get a real technical response, not a calendar link and a discovery gauntlet.