A delivery process refined across dozens of production AI engagements. Every phase has a clear scope, a written outcome, and a defensible reason to exist.
Before a line of code, we map the business problem, the data shape, the users, and the constraints that nobody usually writes down — compliance asks, legacy systems, internal politics. The goal is a clear, scoped picture of what good actually looks like.
We design the system end-to-end: model choice, infra, evaluation strategy, cost envelope, deployment surface. You leave this step with a defensible roadmap — every major call has a written rationale you can point at six months later.
Agile sprints with CI from day one. Working software every Friday. Code review, automated tests, evaluation harnesses for AI components — quality compounds because we keep the feedback loops short.
We ship behind feature flags, instrumented for observability. When the engagement demands it, HIPAA / SOC 2 / GDPR audit prep happens here — not as an afterthought, but as part of the production readiness review.
Post-launch is when AI systems actually find out who they are. We stay on call for tuning, retraining, optimization, and new features. Quarterly reviews keep the system honest about what's working and what isn't.
Every AI system we ship has an evaluation harness on day one — golden sets, regression tests, drift monitors. 'Is this better?' has an answer.
When the industry demands it — HIPAA, SOC 2, GDPR, ISO 27001 — we plan for it at design time, build evidence as we go, and review before we ship.
Observability, error handling, cost controls, and deploy pipelines aren't 'phase 4 work.' They live alongside the code from the first sprint.
Whether you have a brief or just an idea, we'll help you scope a path from concept to production — with compliance built in.
