Your people are already using AI. AI governance consulting for teams who cannot yet say what goes in, what comes out, or what it puts at risk.
Employees are pasting customer records into chat tools, three departments bought three different AI products, and the committee finds out after the fact. Nobody set out to create risk, and nobody can currently describe it either. That is the problem this work solves. We find out what is actually in use, decide what is worth fixing, and put the controls around it so the fix survives the next rollout. Org-wide work starts with the $4,500 Audit on the Build page, which credits 100% toward what follows.
Most AI governance work ends at a 40-page policy nobody reads. We build the operational layer underneath. The intake process. The risk classification. The audit log. The review cadence. The escalation path. The actual evidence you hand to a regulator, a board, or a customer who asks how this works.
For healthcare, government, legal, financial services, and any team whose AI use is going to be scrutinized by someone with the authority to stop it.
AI governance is not a policy problem. It is a systems problem.
Almost every regulated team we meet has the same three artifacts. A responsible-AI policy somebody wrote in 2024. A model inventory spreadsheet that nobody owns. A risk-and-compliance committee that hears about new AI tools after they have already shipped. None of those three things stop a bad outcome.
The gap is not the policy. The gap is between the policy and the work. We build the operational layer that closes it.
- Every AI use case has a clear owner, a risk tier, and a review path before it touches production.
- Every model output that matters is logged with enough context that an auditor can reconstruct what happened.
- Every vendor that brings AI into your environment is evaluated against the same checklist, not whichever procurement officer they got to first.
- Every quarter, the governance committee gets a real report, not a status update.
For teams that have to show their work.
Four ways we stand up AI governance that actually holds.
We map your AI practice to the rules your reviewers actually use.
Picking the right framework matters less than implementing one of them well. We routinely work with NIST AI RMF, ISO 42001, the EU AI Act, HIPAA, SOC 2, OMB M-24-10 for federal teams, model-risk guidance like SR 11-7 for financial services, and state AI laws including the Colorado AI Act and NYC Local Law 144. If your reviewer cares about a different one, we work in that too.
The deliverable is never the framework. The deliverable is a governance practice that a reasonable auditor would call mature.
Mature AI governance, described in plain language.
- A current inventory of every AI system in use, with an owner, a risk tier, and a last-reviewed date.
- An intake process new AI use cases actually go through, not around.
- A vendor evaluation that asks the same questions every time and stores the answers somewhere searchable.
- Audit logs that capture the prompt, the output, the user, the model version, and the downstream decision.
- Human-in-the-loop checkpoints scoped to risk tier, not applied uniformly to everything.
- A quarterly review the executive team actually reads, including incidents, near-misses, and use cases retired.
- A documented path for shutting an AI use case down quickly when something goes wrong.
AI governance consulting, plainly answered.
What is AI governance consulting?
Helping an organization decide which AI it will use, how, by whom, with what oversight, and what evidence it can show when asked. It spans policy, process, technical controls, and the operating cadence that keeps all three honest.
Is this the same as responsible AI consulting?
Mostly yes. Responsible AI is the goal. Governance is the operational system that produces it. We use both terms because clients use both.
We have a policy nobody follows. Do you rewrite it or build what goes under it?
Both options exist. Most clients have a draft. We rewrite it to be implementable, then build the process underneath it. Pure review engagements are available for teams who only need a second set of eyes.
Our auditor asked which AI framework we follow and we do not have an answer. Which one?
Whichever your auditors, regulators, or biggest customers will ask about first. For most US enterprises, that is NIST AI RMF plus ISO 42001. For healthcare, add HIPAA and any applicable FDA guidance. For EU exposure, the AI Act. We help pick.
How long until we have something we can show a reviewer?
Timelines are set in Discovery and written into the Blueprint. Audit Readiness is scoped to the deadline.
We are a startup about to sell to a regulated enterprise. Is this overkill?
No. Startups preparing for a regulated enterprise sale, a SOC 2 audit, or an ISO 42001 certification get a compressed version of the same work.
Employees are putting company information into AI tools and we are not sure what risks we are creating. Where do we start?
Start with the free Vibe Check, then the $4,500 Audit if it is an org-wide question. The Audit finds every AI use in the organization, sorts each by risk, and maps the gap between current practice and the framework you are accountable to. You leave with a ranked plan, and the Audit credits 100% toward what follows.