AI governance in practice
Most companies now have an AI policy. Very few can show it working.
Teams ship code with agents in days, but review, testing and approvals still run at human speed, and nobody is sure who is allowed to decide what. I fix that inside the delivery system, not in a PDF.
The Three-Body Program
Governance fails when only one body moves.
A policy for the AI changes nothing if the pipeline and the decision rights stay as they were. So we take one real piece of work, decide who owns which rules, and build the guardrails where the code ships.
Architecture
Guardrails in the pipeline instead of a target-state diagram. The rules run where the code ships, and every run leaves evidence.
Teams
Decision rights made explicit: who owns the invariants, who owns the mechanism, where teams decide locally, and how exceptions get handled.
AI capability
Agents in production, wired into how the teams build and ship, inside boundaries everyone can see.
For CTOs, VPs of Engineering, Heads of Architecture and Platform, and the leaders who own AI risk. If your AI policy is a document and your agents already ship code, that is the gap I close. This is engineering work, not legal advice. Hands-on, and on the hook for the outcome.
Twenty years from statistician to CTO to hands-on architect.
Where I've done the work
Contact
Let's talk about the gap you're closing.
Tell me where your AI policy stops and your pipeline starts. I read everything that comes in here.