The problem this solves
AI initiatives inherit every data problem the company already has, then add new ones: models grounded on stale or wrong records confidently produce wrong answers, nobody can say which customer data flows into which tool, personal data lands in prompts without anyone checking the legal basis, and when something goes wrong there is no log to reconstruct what the system actually did. Trust lost to one incident is expensive to rebuild.
How we work
The data layer comes first: which sources ground your AI, from CRM records to knowledge bases and documents, what quality and freshness each use case requires, and the pipeline design that keeps context current, because AI output quality is capped by input data quality no matter which model runs on top.
Then the governance layer: an access model defining who may use which AI capability on which data, personal data handling rules for prompts and outputs, review and approval flows for sensitive output categories, and logging and audit design that makes AI activity reconstructable after the fact.
We keep the controls proportional to actual risk per use case, so governance becomes the thing that lets you deploy confidently rather than the reason nothing ships.
Deliverables
- AI data source map with quality and freshness requirements
- Grounding and context pipeline design
- Access and permission model for AI capabilities
- Personal data handling rules for prompts and outputs
- Output review and approval flow design
- Logging and audit specification
What buyers ask before scoping.
Is this GDPR compliance for AI?
It covers the system side of it: designing the flows so personal data use in AI is deliberate, logged, and deletable, which is what makes compliance possible in practice. Legal interpretation belongs to your counsel; we make sure the systems can actually follow what your counsel decides.
How does this relate to AI Agent Strategy & Design?
The agent module designs specific agents; this module designs the shared foundation all your AI uses: data grounding, access rules, review flows, logging. If you plan more than one AI capability, designing the shared layer once prevents re-solving governance from scratch on every project.
Will governance like this slow every AI project down?
Designed well, it does the opposite. Clear rules per risk level mean low-risk uses proceed without case-by-case debate, and high-risk ones have a defined approval path instead of a standoff between enthusiasm and legal. The slow version of governance is the improvised one that starts after an incident.
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