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Software companies, cloud providers and organisations embedding AI into products.
Technology & SaaS
AI shipped inside products, where customer assurance obligations flow back into engineering.
The challenge
What makes governance hard here.
- AI features ship inside products, creating obligations towards enterprise customers.
- Customer assurance questionnaires demand evidence that engineering does not hold centrally.
- Model, data and infrastructure dependencies change continuously.
How Regurai helps
What changes with a connected model.
- Maintain a product-aware register of AI systems, models and their dependencies.
- Connect customer assurance obligations to controls and evidence.
- Keep the governance position aligned with a continuously changing estate.
Use cases
Decisions Regurai supports in this sector.
Answering enterprise customer AI assurance requests with traceable evidence
Assessing risk from third-party model and infrastructure dependencies
Governing AI feature releases and autonomous agent behaviour
Evaluating the economics of AI capability investment
Relevant capabilities
Capabilities most used here.
- AI & Model Inventory
- Dependency Mapping
- Policy & Control Library
- Audit Trail & Evidence
- Continuous Monitoring
Governance considerations
What oversight typically focuses on.
- Customer-facing AI assurance commitments
- Third-party model and infrastructure dependency oversight
- Release and change governance for AI features