A model isn't finished when it works in the notebook.
Operating, monitoring, and sustaining AI models in production — with the same auditor's standard we apply to everything else.
The part almost no one plans for until it's already failed.
Getting to production
Taking a model from a development environment to production in a repeatable way, not as a one-time heroic event.
Monitoring & retraining
Models degrade without monitoring. We define when and how to retrain, and who's accountable for that decision.
Retirement plan
Every model in production needs a plan for the day it stops being trustworthy — not something improvised after it already failed.
This is where governance stops being a document.
Our AI governance assessment asks very concrete questions: is there a documented inventory of the systems in use, is there an identified human accountable, is there a retraining or retirement plan? Those same questions are what we solve in practice when we operate a model in production — not as a compliance exercise, but as the engineering discipline that keeps a living AI system sound over time, with the same rigor we've applied to auditing information systems for over a decade.
Take the governance assessment