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Private deployment & controls

Everything described in this section runs inside the client’s own environment. The private AI is deployed in their private cloud or on-premise, behind their perimeter, with the controls a regulated organisation needs: GDPR-ready workflows, audit logs, role-based access and policy controls. Data is private by default and is never shared with an external provider. Where a process needs it, agents can be built to run inside the same environment.

Private deployment: the AI workspace runs inside the client's own cloud or on-premise, with audit logs, role-based access and policy controls (illustrative). The workspace runs inside the client’s perimeter. Audit logs, role-based access and policy controls sit around it (illustrative).

What you get

  • Deployment inside the perimeter: private cloud or on-premise, so data stays where the client’s policy requires.
  • Audit logs: a record of what the AI did and what it accessed.
  • Role-based access: control over who can see and do what.
  • Policy controls: guardrails on how the AI can be used across the organisation.
  • Custom agents: agents tailored to specific processes, built and run in the same private environment.

Why it matters

  • It is a clear, compliant alternative to public AI tools, where data leaves the organisation by design.
  • It is what makes the platform usable by banks, the public sector and other security-conscious or regulated clients.
  • The same controls apply across the whole workspace: inbox, meetings, Drive, chat and workflows.

Good to know

This is the work that wins regulated clients that public AI tools cannot serve. Lead with governance and the private-by-default model, then expand into everyday enablement once the controls are in place.