Solutions
AI Governance
Build the record an internal reviewer, auditor, or customer security team will ask for: an inventory of AI systems, risk classification, evaluation evidence, human oversight, and a logged decision trail.
Inside the delivery
Record each AI use case, provider, data boundary and accountable business owner. The flow below shows the main delivery stages and the evidence produced at each step.
Record each AI use case, provider, data boundary and accountable business owner.
Output
AI inventory and ownership register
Identify affected users, failure consequences and the controls each use case needs.
Output
Use-case risk and control matrix
Define evaluation evidence, reviewer responsibilities and release approval requirements.
Output
Review workflow and evidence checklist
Track model changes, reported failures and overdue reviews against named owners.
Output
Monitoring and change-review procedure
Controls across the workflow
Before you commit
Overview
Most AI governance work produces a document. The question that follows is harder: can you show which AI systems are running, what each one decides, what it was tested against, who reviews its output, and what happened the last time it was wrong. We implement the controls that make those answerable, in the systems themselves.
Capabilities
Select the work that addresses your constraint. Responsibilities and acceptance criteria are agreed before delivery.
Establish what AI is in use across the organization, including the systems bought rather than built, and rank them by consequence.
Define what acceptable behavior means for each system and produce the record that shows it was checked.
Put a person in the loop where the decision warrants it, with the interface and authority to actually intervene.
Keep the record current after launch, because a governance position established once decays like anything else.
Integrations
Tools are chosen around your existing systems, access requirements and operating constraints.
Frequently asked questions
No. This work establishes technical and operational governance. Legal interpretation, regulatory obligations and independent certification require the appropriate qualified reviewers.
No. We are engineers, not counsel. We implement the technical controls and produce the evidence your legal, risk, or compliance function needs, and we work to the requirements they set rather than interpreting regulation on your behalf.
The inventory step usually answers that, and it frequently finds more systems than expected once purchased tools with AI features are counted. Even at small scale, knowing what is running and who owns it is inexpensive and useful.
Data governance concerns the data itself: ownership, quality, access, and lineage. AI governance concerns the decisions made from it: what a system is allowed to decide, how it was evaluated, who reviews it, and what evidence exists. They overlap and are usually best sequenced together.
Proportionate ones do not. Classification exists precisely so that low-consequence systems stay light while the high-consequence ones get real scrutiny. Uniform controls are what make governance feel like an obstacle.
Yes, and they usually need it most, because the evaluation and logging are outside your control. The work concentrates on inventory, boundary controls, human oversight, and what evidence the vendor can actually provide.
AI inventory, provider terms, data flows, existing policies, evaluation records and the people authorized to accept risk.
The agreed delivery includes monitoring and change-review procedure. Track model changes, reported failures and overdue reviews against named owners.
We review the available inputs before estimating: AI inventory, provider terms, data flows, existing policies, evaluation records and the people authorized to accept risk. The proposal identifies dependencies, review milestones and excluded work; the scope determines the schedule.
Start a conversation
Share your current situation and the constraint you need to resolve. We will use the discovery inputs above to define a practical scope for AI Governance.