The same arc, every engagement.

Understand the problem before proposing the system

One to two weeks. We map the problem, your data, your constraints, and what "working" means in numbers. We interview the people who do the work today — they know where the bodies are buried, and they're the ones an AI system has to actually help.

Discovery ends with a written brief: the problem statement, the proposed approach, the risks, an honest feasibility read, and a fixed price for the next step. If AI is the wrong tool, discovery ends with that finding instead — some of our best advice has been "don't."

A narrow, honest slice on your real data

Two to six weeks. Not a demo on cherry-picked examples — a thin version of the real system running on your real data, with an evaluation harness built first. Quality is a number from the first week, so "is this good enough?" is answered by measurement, not by whoever saw the most impressive demo.

The prototype exists to answer the go/no-go question cheaply. If the numbers don't clear the bar, you've spent a fraction of the budget learning that — which is the point.

The unglamorous work that makes it dependable

Four to twelve weeks, scope depending. Hardening, observability, cost controls, rate-limit and failure handling, human-review routing for the cases the model shouldn't decide alone, and documentation your team can operate from.

This is also where model-agnostic architecture pays off: the model sits behind an interface with the evaluation harness watching it, so the day a better or cheaper model ships, switching is a measured configuration change — not a project.

Both are good outcomes

Your team owns everything we build — code, prompts, evaluation suites, documentation — from day one, not at final invoice. At the end we either hand over cleanly, with a walkthrough and a transition period, or continue as an engineering partner: monitoring, quarterly model re-evaluations, cost optimization, and new capability work on a monthly retainer you can end any month.