Four phases, roughly eight weeks.
You see working software at every stage rather than a deck describing one.
The order matters more than any individual step. Most failed AI projects were built in the wrong sequence: a tool was chosen before the problem was understood, or a pilot went live before anyone decided who would own it.
- 01
Discover
We sit with the teams doing the work and map it as it actually happens, not as the process document describes it. The output is a shortlist of candidates scored on hours saved against effort to build. You approve that list before anything gets built.
- 02
Prototype
A working slice on your real data inside two weeks. Real data matters: synthetic examples hide exactly the messiness that decides whether something works. This stage is designed to be cheap to kill.
- 03
Harden
Evaluation sets, permissions, guardrails, monitoring, alerting and cost caps. This is the unglamorous half that separates a demo from something a team can depend on during a busy week.
- 04
Go live
The automation moves into its own project on our infrastructure and starts doing the work. Your team gets documentation and a live session on using it and reading its output. Running it, from that point on, is our job rather than something we add to somebody's week.
We run it. You use it.
Automation does not stop needing attention once it works. Models get deprecated, APIs change, credentials expire and a run fails quietly at two in the morning. We keep all of that off your team's desk, because a system nobody is watching is the one that dies six months in.
- Your workflows sit in their own project, isolated from every other client
- Hosting, monitoring, alerting and cost caps are ours to watch
- Model deprecations and API changes are absorbed by us, not by your team
- Failures reach us before they reach you, with a run history you can see
- Changes and new workflows are handled inside the retainer
A setup fee, then a monthly subscription.