AI & Data Governance

Introduce AI into your operation with clear ownership, evaluation, and human review — so it helps your team without creating risks nobody can see.

The operating problem: AI is arriving in the operation without clear rules or ownership.
What changes: Intelligence you can explain, review, and control.
How it stays accountable: Set decision rights, test behavior, and record sources, approvals, and recovery paths.

What this can do for your business

Clear responsibility
Every AI-assisted decision has an owner and a review point.
Evidence you can show
A record of what the system used, produced, and who approved it.
Fewer surprises
Known limits, tested behavior, and a way to recover when something goes wrong.

If some of these sound familiar, this is where we would start.

Review and approval flows
Human checkpoints where decisions carry consequences.
Evaluation frameworks
Testing AI output against real cases before and after launch.
Data governance foundations
Ownership, definitions, lineage, and quality rules for the data AI depends on.
Access and permissions
Who and what can see, change, or act on each type of information.
Audit trails
Interpretable records of sources, outputs, and approvals.
Operating guidelines
Practical policies your team can follow day to day.

Tell us where the work gets stuck. We start by understanding it.