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AI Governance software.

Engineering for named requirements, connected systems, and the operational evidence that has to survive after launch.

The AI usage your risk register doesn't know about.

Employees are already pasting customer records, source code and internal strategy into consumer ChatGPT, Claude and Gemini accounts that legal, security and IT never approved — usage that shows up in expense reports and browser logs long before it shows up in a risk register. The EU AI Act's governance obligations and existing GDPR processor rules both assume an organisation actually knows which AI systems touch its data; unmanaged shadow AI makes that assumption false from day one.

Detecting it requires more than a firewall rule blocking known AI domains, since sanctioned browser extensions and mobile apps route around perimeter controls. A workable governance layer inventories AI usage patterns across managed devices, classifies the data types involved, and gives compliance teams an approved-alternative path rather than a blanket ban that just pushes usage further underground.

Start with the requirement on your desk.

Each area is scoped around a named buyer problem, then designed outward to cover its systems.

How AI Governance becomes working software.

Three decisions keep the project grounded in operations from the first workshop onward.

01

Make the requirement testable.

Turn source text, technical standards, and deadlines into explicit product decisions.

02

Fit the systems already in place.

Map owners, data sources, approvals, and failure paths before choosing the software shape.

03

Ship evidence with the feature.

Include the operational trail, documentation, and maintainable architecture the requirement demands.

Turn the difficult part into the first milestone.

If ai governance software is on your roadmap, describe what you run today and where the requirement gets stuck.