What we get called in for

You have a proof of concept that impressed the board and cannot survive real documents.
Your team can call an API but has no way to prove a prompt change improved anything.
Legal will not approve the feature until someone can explain where an answer came from.
You need a copilot inside an existing product, not a chat window bolted onto the side.
You are paying per token for work a small fine-tuned model or a plain classifier could do.

How we build it

Ground the use case in a graded set

Before any architecture, we build 100–200 real examples with expected outputs, graded by your subject-matter experts. It becomes the acceptance criteria, the regression suite and the argument-settler.

Choose the smallest thing that works

Classifier, retrieval, tool-calling agent, fine-tune, or none of the above. We have shipped features where the winning answer was a lookup table and a rules engine, and said so.

Build the retrieval layer properly

Chunking that respects document structure, hybrid search, metadata filters, and re-ranking. Most quality complaints are retrieval problems wearing a model costume.

Wire evaluation into CI

Every prompt, model or index change runs the graded set and reports drift on faithfulness, answer coverage and refusal rate before it merges.

Make it observable and reversible

Per-request traces with the retrieved context, cost and latency attached. Model version pinned. A flag that puts the previous behaviour back in under a minute.

What you get

Reference architecture and data-flow diagram, including where PII stops
Graded evaluation set, scoring harness and CI integration
Retrieval or fine-tuning pipeline with reproducible index builds
Guardrails: input filtering, output validation, refusal handling, rate and cost caps
Human-review console for the people who will correct the system in production
Cost model per 1,000 requests, with the levers that move it

AI, LLM and GenAI engineering