1. Control plane setup
Define tenants, apps, role boundaries, and API/admin key strategy in the gateway and console.
Architecture
How DataSafeHouse works in production
The platform architecture is designed for governed AI application delivery from control-plane setup through integration, runtime enforcement, and operational monitoring.
How the Platform Operates
A governed workflow that moves from secure ingestion through controlled application delivery and ongoing operational oversight.
2. Model catalog and policy
Configure logical models and app overrides, then apply provider/model and token policies at tenant and app scope.
3. Data and connector integration
Install and validate connectors, manage secrets references, and control outbound host access where required.
4. Grounding pipelines
Ingest transcripts and context documents, run extraction/chunking/embedding pipelines, and expose app-scoped grounded query endpoints.
5. Runtime governance and telemetry
Enforce limits in the request path and monitor usage, error rates, policy changes, and connector events for continuous operations.
Deployment options
Enterprise cloud deployment
Deploy in enterprise cloud environments with approved provider integrations and centralized platform operations.
Customer-managed deployment
Operate in customer-managed infrastructure with environment-specific controls, connector allowlists, and local provider options.
Hybrid rollout model
Combine centralized control-plane practices with domain-specific application deployments based on data locality and policy requirements.