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