Retrieval, evals, and guardrails — the parts that separate a demo from a feature people rely on. We build AI into products so it holds up under real load, real costs, and real adversaries, and we measure “better” with numbers instead of vibes.
Typical span3–10 weeks
Team size1–2 engineers
01
What's included
Scope of the practice
ARetrieval (RAG)Chunking, embeddings, and a vector store tuned for recall you can measure — so the model answers from your data, not its imagination.
BEvalsA test harness for model output, so “better” is a number you can point at. Regressions fail the build like any other bug.
CGuardrailsInput and output validation, refusal handling, and cost & rate limits that hold up when someone tries to break them.
DModel integrationProviders behind one interface — swappable, with fallbacks when a model degrades or a vendor has a bad afternoon.
EMonitoringToken spend, latency, and answer quality tracked live, so drift gets caught in a dashboard before a user files a ticket.
02
What you get
Deliverables, itemised
01Retrieval pipeline in your repository
02Eval suite with baseline scores
03Prompt & guardrail configuration
04Cost / latency dashboard
05Model-swap and fallback runbook
Stack we reach for
Python and TypeScript; pgvector or a managed vector store; a provider-agnostic model layer so you’re never locked to one vendor. Evals run in CI, so quality regressions fail the build like any other bug.
Representative work
Kleio AI — retrieval that holds up
A document-intelligence product built on a measured retrieval pipeline, an eval suite, and guardrails tuned against real usage. The full approach lives with the case study.
