Five phases. The same team that scopes the work deploys and maintains it, so nothing gets lost in a handoff.
What actually happens, who needs to be involved on your side, and what you walk away with at the end of each one.
We map your data, your constraints, and the narrowest use case that proves value fastest. This is where most of the real risk in an AI project gets found and priced out, before any engineering time is committed to a direction that turns out not to work.
A working system built against your real data, not a slide deck or a demo on public benchmarks. We measure it against the metrics agreed in discovery, so whether it's ready to move forward is a decision backed by numbers, not a gut call.
The prototype runs against a limited slice of real usage, real users or real traffic, before we commit engineering time to hardening it for everyone. This is the cheapest place to catch the gap between how a system behaves on test data and how it behaves in the wild.
The system is hardened, monitored, and integrated into your stack by the same engineers who built the prototype, not a different team seeing the code for the first time. That continuity is what keeps a production build from re-litigating decisions the pilot already settled.
We continue monitoring performance, tuning cost, and updating models, staying accountable for how the system runs after launch. This is scoped and priced as its own engagement, agreed before the production build wraps, not an open-ended commitment you discover the cost of later.
The phases are the schedule. These are the principles that decide whether the system that comes out the other end actually holds up.
We agree on evaluation metrics before development starts, specifically so this decision isn't subjective. If the prototype doesn't clear that bar, we tell you plainly, adjust scope or approach for another short cycle, or recommend stopping rather than pushing a pilot that isn't ready. A failed prototype after two to five weeks is a far cheaper outcome than a failed production launch after three months.
Sometimes, for narrow, low-risk use cases where a pilot wouldn't teach us much we don't already know. For anything touching real users, live data, or a regulated process, we recommend keeping the pilot: it's the cheapest place to catch the gap between how a system behaves on test data and how it behaves on your actual traffic.
Discovery needs the most of your time: someone who knows the data and someone who owns the business outcome, for a handful of working sessions over one to two weeks. After that, we run mostly independently, with a short weekly check-in and async access to whoever can answer domain questions as they come up. Production and pilot phases usually need a technical point of contact for integration and access, not a dedicated team.
Performance and cost monitoring with alerting, model or prompt updates as your data or the underlying models change, incident response if something breaks, and a regular review of whether the system is still meeting the metrics agreed on in discovery. It's scoped and priced as its own ongoing engagement, agreed before the production build wraps, not an open-ended commitment.
The week ranges are typical, not fixed. Data access delays, a wider prototype scope than first discussed, or findings during discovery that change the use case can all shift them. We flag a timeline change as soon as we see it coming, with the reason, rather than letting a deadline slip silently.
30-minute scoping call. No deck, just questions.