Case Studies.
Real-world enterprise use cases.
How regulated teams put DataStrict into production - the controls they enforce, and the evidence they produce.
In production
Governed AI, shipped under scrutiny.
Support copilots
Customer-facing assistants that answer fast and never leak regulated data - PII redacted at the boundary, every turn logged.
Internal RAG
Retrieval bound to entitlements: each chunk is checked against the asker's access before it reaches the model, end to end.
Agentic workflows
Autonomous tool calls evaluated against policy before they run - so an agent can act without acting out of bounds.
What changes
The pattern behind every rollout.
Ship months sooner
Governance moves from a quarters-long review into a policy you author, simulate, and enforce - so AI projects clear risk and clear legal without stalling in committee.
Shrink the leak surface
Sensitive data is redacted before it reaches a model and outputs are inspected on the way back, so the failure modes that block production never get the chance to happen.
Answer the auditor in minutes
Every decision lands in an immutable Ledger with its policy version and finding - so an evidence request is a query, not a fire drill.
How teams get there
Same control layer, mapped to your obligations.
None of these are bespoke builds. A bank, a hospital, and a public-sector team run the same enforcement engine - what differs is the policy, mapped to the rules each one answers to. The starting point is your industry and the frameworks on the compliance page.
From there it is one pattern: author policy, validate it in Simulation, and promote it to enforcing in the request path.
Govern AI like infrastructure.
Talk to our team about deploying DataStrict across your enterprise stack.