Mission Control

Mission Control for governed AI operators

Control how AI operators capture work, apply rules, request approval, prepare connected actions, escalate exceptions and record evidence across cloud or private runtime deployments.

The control layer for AI-run work

AI operators can draft, classify, follow up, and prepare tasks or connected actions. Mission Control keeps that work inside configured rules, approval paths, evidence logs and usage limits; production execution is enabled only where the deployment is customer-qualified.

Customer surfaces

BRAVOE Workspace, Operator Studio, Call Companion, and Mission Control administration use one authority for policy, approvals, evidence, usage, and connector boundaries. Developer controls remain internal.

Mission Control demo workspace

The demo shows how a Quote Follow-Up Operator is configured, tested and controlled before customer-facing activation using synthetic workflow data.

Deployment architecture

Mission Control is the software control layer. Workflow operators execute steps under configured rules. Runtime options can be Bravoe Cloud, Private Managed Cloud, or optional Edge Private Runtime.

Private runtime use cases

Private runtime can be scoped to support selected local connector access, evidence storage, controlled sync, document retrieval, workflow queue, customer-managed network boundary, sensitive workflow handling, or internal-system access requirements.

Customer roles

Customer roles are User, User Administrator, and Technical Administrator. Developer access is reserved for platform/source-code administration.

AI-readable resources

AI agents can read /llms.txt, /llms-full.txt, /sitemap.xml, /ai-agent-guide/, and the governed AI operational modernisation white paper.