AI governance consulting for agents in production.

AI governance consulting is a services engagement that puts working controls around a company’s AI systems: who owns each system, what the agents are allowed to do, how their decisions get logged and reviewed, and how they get fixed when they drift. Most of the market sells the policy layer — risk frameworks, committee structures, model-inventory templates — aimed at enterprises with regulators watching. For a mid-market operator the practical version is operational: AI agents run in production, someone in-house owns them, a human reviews the exceptions, and a written plan exists for winding a wrong action back. Done properly, it is the difference between an AI capability your company runs and one that runs on a vendor’s goodwill — and it includes the change management that gets your team to actually use the systems.

A study maps the opportunity and hands you a roadmap; execution, adoption, and ownership are someone else’s problem. We build the systems, deploy them in your cloud environment, stand up the in-house ownership model, and manage adoption to the last user. The fee buys working software your team owns — not a document.

Your team, with a model we stand up together: named owners, exception queues with a human in the loop, decision logs, and a rewind plan. We stay on supervision through the handoff, then step back — the goal is governance you run without us.

With visibility: an inventory of what is actually in use, what data it touches, and which workflows are already quietly automated. That becomes the governance baseline — sanctioned AI agents in your environment replacing the paste-into-a-chatbot versions, with logging your security team can read.

That is the design goal. Every system ships with an owner’s manual, named in-house owners, and the documentation to run governance and routine maintenance internally. Support stays available, but the autonomy is real — you own the IP, the rules, and the runbook.

What employees are allowed to do with AI tools and what they are not; which company data can and cannot reach an outside model; when a human must review an agent’s output before it acts; and who owns each system. Some teams call the same document an AI acceptable use policy. We write it against the real usage found in the readiness step, then encode it into the AI agents themselves — so the policy is enforced by the system, not stapled to the intranet.

If people already paste company data into chatbots — and they do — the policy question is live whether or not a project has started. A 40-year CFO at a branded-products manufacturer asked it exactly right before his company’s first AI move: what are people allowed to do? Writing the corporate AI policy first does not slow the first build down; it tells the build what the agents may do, what they must log, and where a human stays in the loop. Policy before product is the cheaper order.

Data governance consulting is the foundation layer — ownership, quality, and lineage of the data your systems read. AI governance adds what sits on top: what the models and agents are allowed to do, who is accountable for them, and how their decisions get supervised. We do both, custom-built, and engagements often start on the data side — an agent is only as governable as the data it reads.