An operating layer for mission-specific AI.
Connect fragmented systems. Model the operation. Deploy governed AI workflows. Keep operators in control.
Connect. Model. Act. Govern.
Connect
Integrate systems, data, sensors, documents, field reports, APIs, and workflows.
Model
Build the operational ontology around assets, people, places, tasks, risks, costs, approvals, and decisions.
Act
Deploy copilots, alerts, summaries, recommendations, approvals, decision rooms, and workflow surfaces.
Govern
Maintain human review, access controls, audit trails, permissions, and decision lineage.
The Mission Model
A living operational graph behind every governed decision.
- 01Assets
Equipment, facilities, systems, sites, infrastructure.
- 02People
Operators, engineers, field teams, approvers, stakeholders.
- 03Places
Sites, plants, bases, regions, facilities, zones.
- 04Tasks
Work orders, inspections, procedures, corrective actions.
- 05Risks
Failure modes, cyber exposure, schedule drift, readiness gaps.
- 06Costs
Budget, spend, procurement, downtime, resource impact.
- 07Approvals
Human review, authority, compliance, escalation.
- 08Decisions
Recommended action, rationale, outcome, audit trail.
Operational ontology
Every recommendation ties back to source data, the human who approved it, and the decision it shaped.
Connect → model → govern → decide.
Four surfaces. Built around the decisions operators actually make.
01
Risk Cards
Prioritized signals with severity, confidence, and recommended action.
02
Decision Rooms
Cross-source rationale and approvals against the active operation.
03
Executive Briefs
Readiness rollups, blockers, and pending decisions.
04
Field Workflows
Asset history, safety, instruction, and closeout at point-of-work.
Operators stay in control.
Governance is first-class. Every consequential action is operator-in-the-loop, with role-based access, audit trails, and decision lineage end-to-end.
Human-in-the-loop
Every consequential action requires operator review and approval.
Role-based access
Permissions modeled around the operation, not a generic CRUD grid.
Audit trail
Inputs, model output, approver, time — captured against every decision.
Decision lineage
Trace any recommendation back to the source data and the people behind it.
Built around the stack you already run.
Deployment-flexible across client-owned environments, cloud data stacks, edge/OT environments, and enterprise AI platforms.