AI governance consulting for agents in production.
We build your AI systems, stand up the team that owns them, and run the change until your people actually use them.
Official services partner of the platforms defining AI
NVIDIA
Anthropic
AI change management is the impossible part.
From an education-technology reseller we sat with: three merged companies, a hundred field sellers moving from relationship selling to a structured, data-driven process. The build was the easy half.
Pilot purgatory
Proofs of concept pile up, each one promising, none in production. The AI strategy lives in demos because nobody owns the last mile.
Agents nobody owns
A vendor built it, a consultant tuned it, and the person who understood it left. When the agent drifts, everyone looks at each other.
A hundred sellers, no shared process
Regional relationship selling became three merged companies with no common way of working. Adoption was the war before the war.
Shadow AI with no visibility
Reps already paste customer data into chatbots to save an hour. There is no policy, no logging, and nobody watching what leaves.
The dependency question
Every proposal ends the same way: who owns this after you leave? Nobody wants a core system they cannot run without a third party.
An unowned AI system is a liability, not an asset.
What AI without an ownership model looks like at a mid-market company. Your numbers will differ — the first week of an engagement puts figures on yours before anything gets built.
From anonymized engagements — an education-technology reseller and mid-market operators carrying AI pilots
AI transformation consulting that ships working systems.
The strategy firms sell the study. We build the system, stand up the ownership model, and stay until your team runs it without us.
- 01
Build the system in your environment
We ship AI agents against your rules, in your cloud environment — the working system, not a roadmap describing one.
- 02
Stand up the ownership model
Named in-house owners, the owner’s manual, supervision, and a rewind plan for when agents get it wrong — governance your team can run.
- 03
Run the change to the last seller
Adoption is managed like a rollout, not an email: team by team, workflow by workflow, until the tool is how the work gets done.
AI governance consulting should end in an ownership model, not a deck.
The big firms sell AI governance frameworks at strategy prices: risk taxonomies, committee charters, policy documents. Then they leave, and the framework meets an organization that never adopted the tools in the first place.
We are an AI implementation services firm. Governance here is operational: who supervises the agents in production, who owns the system after the build, and who can run it without calling us.
Led by Peter Enestrom.
Asked by the person who will own this.
The straight answers, before you book anything.
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.
Start with one workflow.
Tell us where your team loses hours. We will come back with a straight answer on whether AI can help, what it would take, and what it would pay.