We take a high-friction workflow and turn it into a system that produces work your team accepts, shows the evidence behind it, and tracks whether supervision falls over time.
Not a pilot. Not a roadmap. If the workflow is not worth building, we stop before the build starts.
AI pilots die between the workshop and the workflow. The pattern is usually the same.
[01]
The company can't move at the speed of AI.
You can ship a prototype in a week. Deploying it to a team takes a quarter. The quarter is the bottleneck, not the week. Every AI engagement that ignores this produces something that works in the demo and dies in the rollout.
[02]
The people weren't trained for the work AI leaves them.
AI absorbs parts of execution. What remains is judgment, coordination, and systems thinking. Most teams were hired, trained, and promoted for doing the work, not for supervising systems that do parts of it.
[03]
The company runs on meetings, not systems.
AI can operate anything that has an interface. Most company work does not have one. Decisions sit in Slack threads, Google Docs, meetings, and someone's head. You can't point AI at that. You have to build the interface first.
[04]
The deployment was never in scope.
The deck lands. The partner leaves. The prototype needs owners, permissions, review flows, and operating metrics. None of that was priced, staffed, or built.
Work exists in the gap between these four failures and AI in production.
Control is not a feature we add at the end. It is how the system is built.
Two scoreboards. Kept separate.
The system's numbers.
Accepted outputs. Rejected outputs. Edits per output. Cycle time. And the one we manage to: supervision minutes per accepted output. It is the honest cost of AI in production. When it falls without quality dropping, the system is learning. When it stalls, we find out why.
Your numbers.
Revenue, cost, time saved. Measured by you, against the criteria we wrote down before anything was built. We don't let the system grade its own homework.
One engagement. Three gates. Each gate gives you a clear decision: continue, adjust, or stop.
[ I ]
5 to 10 days, fixed fee
We choose the workflow with you, map the friction, and write the acceptance criteria before anything is built. You leave with the map and a clear call on whether the build makes sense. The map is yours either way.
Start with a diagnosis →[ II ]
2 to 4 weeks, milestone pricing
We rebuild the workflow with your team, deploy it into real work, and measure it against the acceptance criteria set during Diagnose. The deployment milestone only invoices if it ships.
Talk about a build →[ III ]
time-boxed, scoped to the workflow
We stay inside the workflow long enough for the system to survive real usage. We help your team run the review queue, tune the system, handle exceptions, update the controls, and measure whether supervision falls without quality dropping. Then we hand it over. Your team owns the workflow. We reduce to an Operating Review only if there is something worth reviewing. Off-ramp, not lock-in.
Talk about embedding →If your workflow is not on this list, that is what Diagnose is for.
And people on your team who understand how the system works, how to review it, and what can be changed without calling us back. That was the point.
A prompt library. A change management workshop with no technology attached. A 120-slide deck. An AI strategy that does not name the thing being built. An ungoverned demo. Hours.
Not ready to engage yet? The Room is the place to start.