"How does your AI engagement actually work?" gets asked at every kickoff. Most firms answer with a vague "agile" and a marketing diagram. Ours is concrete, and we structure every engagement the same way for a reason — it is what consistently ships.
Phase 1 — Discovery (2 to 4 weeks)
Goal: turn a business problem into a sharp technical scope. We map the workflow as it actually runs today, identify the AI-leverage points and the AI-trap points, agree on what success looks like in measurable terms, and assess data readiness. Deliverable: a written roadmap with a recommended approach, milestone schedule, risks, and a fixed-bid proof-of-concept proposal.
What kills projects here: skipping the data-readiness check. If your data is not in retrievable form, every downstream estimate is fiction.
Phase 2 — Proof of concept (3 to 6 weeks)
Goal: prove the technical bet before either side commits to a full build. Real data, real workflows, real evals, real users in the loop. Not a slide deck. The success criteria from discovery become the eval suite. We tune the system until those evals pass or we conclude the approach was wrong — which is a legitimate outcome of phase 2, not a failure. (For how we choose between RAG, fine-tuning, and prompt engineering during this phase, we have written that up separately.)
What kills projects here: scope creep. Every "while we are at it, can we also..." pulls focus from proving the central bet.
Phase 3 — Production build (8 to 16 weeks)
Goal: ship the system inside your real stack with the things production systems need — guardrails, observability, cost controls, fallback behaviors, role-based auth, and a maintainable codebase. Weekly demos, written change notes, and a launch readiness review at the end. Then a defined post-launch support window where we are on call for the issues that always surface in the first few weeks.
What kills projects here: skipping observability and evals at launch. Without those, the day-two team has no way to safely change the system as models or requirements evolve. We do not ship without them.
Why this rhythm works
Every phase has a clear decision point. After discovery, you can walk away with a written roadmap and zero commitment to a full build. After the proof of concept, you have evidence — not a pitch — for whether the production investment is justified. Nobody is locked in past the next decision, which is the only honest way to run high-risk technology projects.
If your last AI engagement stalled because it never reached a clear stop-or-continue decision, the framework above is probably what was missing. This is how our AI development and consulting practice runs every engagement — and the first 30-minute discovery call is free, so the cost of finding out whether your problem fits is zero.