“How does your AI engagement actually work?” comes up 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 one 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 runs today, find 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 is 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 is 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 is 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.