How we build
Production requirements
Six engineering practices we bring to every engagement, and what each one means in practice. They are not certifications or guarantees; they are how we make an AI system boring enough to depend on.
- Production requirement · 01 Evaluation Versioned test sets, offline scoring and online sampling. A number you can reproduce is the only kind we report. What it means to us
- Production requirement · 02 Human oversight Explicit confidence thresholds and review queues. The system recommends; a named person stays accountable for every decision. What it means to us
- Production requirement · 03 Data boundaries Clear rules on what leaves your environment, what is logged, who can see it and for how long — written down before the first model call. What it means to us
- Production requirement · 04 Observability A trace for every model call and tool use, cost and latency per request, and drift dashboards wired into the tooling your team already watches. What it means to us
- Production requirement · 05 Latency & cost Budgets agreed before we build. Routing, caching and smaller models are used wherever they meet the quality bar — and measured when they do not. What it means to us
- Production requirement · 06 Operational ownership Runbooks, on-call notes, and a hand-over your own engineers can run. The goal of every engagement is a system that does not need us. What it means to us