The graveyard of AI pilots is full of impressive demos that never shipped. The gap between a compelling prototype and a trustworthy production feature is almost always about evaluation, not modeling.
We start every AI engagement from the business outcome and a set of concrete evaluations — a labelled dataset that defines what 'good' means before we write a line of prompt or pick a model. This turns a subjective 'it feels smart' into a measurable target.
From there we prototype quickly, measure against the evals on every change, and add guardrails and monitoring so regressions surface before customers see them. The result is an AI feature the team can maintain and improve with confidence.
AI is a software engineering discipline. Treat it like one — with tests, observability, and clear success metrics — and it ships.
