Deploy AI in environments where your organization controls data, access, infrastructure, and operational boundaries.
Private AI is a technical boundary, not a marketing label.
The same core platform, scoped to your boundary.
Route requests through a controlled gateway rather than directly to a public API.
RAG and enterprise search scoped to the documents a given user or agent is allowed to see.
Single sign-on and role-based access control integrated with your existing identity provider.
Access scoped to what a role actually needs, nothing broader.
Request-level logging across models, agents, and data connectors.
Structured, permission-aware access to your internal data sources.
Ongoing monitoring of model behavior, drift, and output quality.
Managed model and infrastructure updates without unplanned downtime.
Not necessarily. Private AI can run on-premise, in a private cloud, or hybrid — the requirement is a controlled boundary, not a specific location.
No. Private AI is about technical control within a boundary. Sovereign AI is broader — control across data, models, compute, operations, and jurisdiction.
Yes, where that fits your requirements — the architecture is provider-flexible.