AI governance
Policy, audit trail and human review, written for a regulator.
The problem
What usually brings people here.
A regulator, an auditor or a large customer is going to ask who approved this model, what it was trained on, how you know it is fair, and what a person can do about a decision it made. Assembling that after the fact is far more expensive than recording it as you go.
What we do
How the work actually runs.
We write the policy that matches what you are actually doing, then make the system produce the evidence for it: model cards, an audit trail per decision, a documented human review path, and a register of where AI touches a customer outcome. Written for someone outside the team to read, because that is who will read it.
How this runs
Design, develop, refine.
Design
Two to four weeks. We scope with the engineers who will build it, so the estimate comes from the people held to it.
Develop
Two-week increments, demoed to you, shipped behind flags. You see working software in week three, not a status deck.
Refine
The squad stays. Lead time comes down, the backlog gets shorter, and the same people know why every decision was made.
Proof lives on the work page.
We publish numbers per engagement, cleared by that client, rather than per service line. What is written up there is what we are allowed to show.
Also in AI Engineering
Related services.
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Tell us what you are trying to ship.
You will get a reply from an engineer, not a form handler. If we are not the right firm for it, we will say so and tell you who is.