Use AI where privacy, routing, and governance cannot be afterthoughts.

The Problem

Sensitive teams need more than a policy telling employees to be careful.

They need infrastructure that supports:

• Local-first storage
• Self-hosting on customer-controlled servers, private cloud, or VPC
• Qate multi-user workspaces for governed team access
• Controlled model routing
• RBAC for Admin, Developer, Operator, Auditor, and Viewer access
• User and admin permissions
• Device and account controls
• Tamper-resistant audit logs for security review and compliance
• Human approval gates
• Tenant boundaries where hosted contexts apply
• Clear separation between company context and external model access

What QUI Enables

QUI lets private or regulated teams design AI workflows around control from the beginning.

Cloud models can still be used when appropriate, but sensitive memory, knowledge, agent configuration, and workflow logic remain inside the controlled runtime.

Qate helps regulated teams organize shared workspace membership, role assignment, access boundaries, approval responsibility, and audit visibility around that controlled runtime.

That deployment model matters when teams need stronger control over data sovereignty, privacy, security policy enforcement, latency, and regulatory obligations.

RBAC supports that control by defining who can create agents, change prompts, access memory, use certain models, view conversations, approve workflows, or alter infrastructure.

Audit logs record who logged in, changed configuration, accessed data, deployed an agent, changed permissions, and when, giving enterprises evidence for investigations, accountability, security monitoring, and compliance.

QUI is designed around the security and governance principles reflected in ISO/IEC 27001 and ISO/IEC 42001. We are actively building the policies, controls, and evidence required to support future independent certification. Certification is planned; QUI is not currently ISO-certified.

Example Workflow

1. Admin defines approved model routes and integrations.
2. Qate workspace roles define who can create agents, view conversations, access memory, approve workflows, or change settings.
3. Sensitive agents are configured with limited tools and private knowledge.
4. Workflows include mandatory approval gates.
5. Local or private inference is selected for high-sensitivity tasks.
6. Evidence is kept for review.
7. Policies can evolve without rebuilding the whole agent layer.

Outcome

Teams can adopt agentic AI without losing sight of where intelligence lives, what it can access, and who remains accountable.