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AI governance & LLMOps
Getting an AI pilot to work is easy. Getting AI into production, where it touches real customers and real data, is where most efforts stall. AI governance and LLMOps is the discipline that gets you across: one use case at a time, measured before it scales, with security, access control and human sign-off designed in rather than bolted on.
What gets built
The roadmap is phased on purpose. Each use case is evaluated against a baseline before it goes live and measured after, so scaling decisions rest on evidence instead of enthusiasm. Whether it runs on Azure AI Foundry or the stack you already have, every AI system sits on the same rail: access control, audit logging and a human in the loop where it matters.
Security is designed in from the first prompt. Models only see the data the asking user is allowed to see, every interaction is logged, and evaluation runs continuously rather than once at launch. An answer from an AI system ends up as governed as a number on a dashboard.
This is also how you get ready for the EU AI Act. The Act is already in force and its obligations arrive in phases: the bans and AI literacy duties have applied since early 2025, the rules for general-purpose models since August 2025, and the bulk of the high-risk requirements are phasing in now. The practices above, an inventory of your AI systems, logging, human oversight and documented evaluation, are exactly what the Act expects. Building them now is cheaper than retrofitting them under a deadline.
How it works in your team
I work embedded in your team, end to end. We pick the first use case together, usually something small with a clear owner and a measurable outcome, and take it to production on the governed rail. Your team learns the pattern by shipping with me, not from a slide deck. The evaluation harness, the policies and the documentation all land in your repositories, so use case two is faster than use case one and does not need me.