AI governance
Once AI moves from answering questions to taking actions in enterprise systems, governance has to work at the moment of execution, not only in a policy document.
What it covers
- 01
Policy design
Which AI clients may request which capabilities, against which systems and for which roles, written as rules that can be tested.
- 02
Approval workflows
Human sign-off for actions that warrant it, routed to the right owner with the full request in view.
- 03
Data controls
Rules on what data may leave, and to which model. Sensitive fields are handled before a prompt is sent.
- 04
Audit and evidence
A tamper-evident record of who asked, what was decided, what ran and what it cost, available to security and audit.
- 05
Cost governance
Spend attributed by team, client and use case, with limits that hold.
Governance that runs at the moment of execution
We put these practices into operation with Nucleus, the control layer between AI clients and your enterprise systems. Downstream systems keep their own authorization and enforcement.
Where this sits
Every engagement follows Strategy, Build, Govern and Operate. This capability does most of its work in the highlighted stage.
- 01 · Strategy
Find where AI pays
We map processes, volumes and systems, then rank use cases by value and feasibility.
A funded roadmap and an AI CoE operating model
- 02 · Build
Embed engineers, ship systems
Forward deployed engineers work inside your teams and connect models to SAP, CRM, identity and data.
Production software in your environment, not a demo
- 03 · Govern
Control every AI action
Identity, policy, approvals, data controls and audit apply before anything executes.
A system your security and audit teams can sign off on
- 04 · Operate
Stay accountable after launch
Monitoring, evaluation, cost management and steady improvement against the original target.
Named owners, runbooks and measured outcomes