Retrieval, evals, and guardrails — the parts that separate a demo from a feature people rely on. I build AI into products so it holds up under real load, real costs, and real adversaries, and I measure “better” with numbers instead of vibes.
Typical span3–10 weeks
Who builds itShivam, end to end
QADedicated tester
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; Chroma DB or whichever vector store already sits next to your data; 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 held up at 200k+ users
Vector retrieval over Chroma DB behind a low-latency AI embed, built and scaled as the second engineer on the team. The full approach lives with the case study.
