Platform · the Ridgeline operating layer
From physical world to governed action, in six layers.
Ridgeline connects fragmented systems and physical sensing, models the operation, issues recommendations operators can interrogate, and only executes what authority permits.
- 01Physical world
- 02Observation
- 03Context
- 04Intelligence
- 05Authority
- 06Action
- operator-in-the-loop · end to end
Each layer only exists to make the next one trustworthy.
01 · Physical world
01 · Physical world
The operation, as it actually exists.
Assets, crews, sites, weather, and load. Most of it never reaches a dashboard, and the parts that do arrive late and out of context.
- Assets
- Sites
- Crews
- Conditions
02 · Observation
Signal captured where the work happens.
Thermal, acoustic, vibration, visual, GNSS, and OT/ICS telemetry from fixed sensors, UAS and UXS platforms, and field reports. Every observation stays attached to the object it describes.
- Sensors
- UAS / UXS
- SCADA
- Field reports
03 · Context
Resolved against the mission model.
The observation joins the asset record, maintenance history, open work, governing procedures, and downstream dependencies. This is the ontology: assets, people, places, tasks, risks, costs, approvals, decisions.
- ERP
- CMMS
- GIS
- Documents
- Procedures
04 · Intelligence
A recommendation an operator can interrogate.
Bounded by policy and evidence. Every recommendation carries what happened, why it matters, what to do next, the supporting evidence, a confidence statement, and what is still missing.
- Evidence
- Confidence
- Next action
- Gaps
05 · Authority
Recommended is never the same as authorized.
Role-based approval, policy checks, and required artifacts gate every consequential action. When an action cannot advance, the system states exactly which condition is unmet.
- Roles
- Policy checks
- Approvals
- Artifacts
06 · Action
Executed in the system of record, with lineage sealed.
The intended architecture writes the authorized change back into the system of record — work order, dispatch, or notification — and preserves the chain from observation to signature for audit, review, and learning. Described here as architecture, not as a demonstrated deployment.
- Work orders
- Dispatch
- Notifications
- Audit trail
The same six layers carry a physical observation from a substation, a pipeline, or a flight line through to a governed action. Walk one on the homepage workflow example.
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 · conceptual
The connections shown are a conceptual illustration of how these domain objects relate, not a customer ontology. In the intended architecture every recommendation ties back to source data, the human who approved it, and the decision it shaped.
Connect → model → govern → decide.
Built for governed operations.
Ridgeline Mission Operations unifies enterprise data, operational systems, field activity, documents, media, and physical signals within a governed operational ontology — the Ridgeline Operational Core.
The platform gives executives, managers, engineers, and field teams a shared operating picture — then uses bounded AI agents, permissioned workflows, and human approvals to move from signal to evidence-backed action.
Ridgeline can connect to existing customer environments, including ERP, CMMS, CRM, Domo, enterprise data platforms, cloud platforms, files, APIs, and operational systems, without making those upstream systems the user experience.
// Architecture
signal → evidence → authorized action
- 01
Operational data sources
ERP · CMMS · CRM · data platforms · cloud · files · APIs · sensors
- 02
Ridgeline Operational Core
Governed data fabric, ontology, and agent runtime
- 03
Ridgeline Mission Operations
Product · domain logic · applications · experience
- 04
Operators, managers, executives
Human-authorized action with audit history
Ridgeline AI is an independent company. All third-party trademarks are the property of their respective owners. No partnership, endorsement, or sponsorship is implied.
The Ridgeline Operational Core.
Ridgeline's proprietary operating layer unifies the mission ontology, domain logic, bounded agents, governance, and operator experience in one coherent system.
Development status is indicative. Deployment patterns vary by client systems, permissions, and security requirements.
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.
The same layer, extended to the physical world.
Sensing, computer vision, autonomy, and edge inference feed the same ontology, governance, and approval model as enterprise data - so perception becomes evidence, not a separate stack.
Physical AICameras, lidar, thermal, acoustic, vibration, GNSS, and OT/ICS telemetry captured where the work happens.
Edge inference turns raw signal into detections, states, and anomalies before anything leaves the site.
Detections resolve against the mission ontology - the asset, the crew, the location, the procedure, the risk.
Reasoning is bounded by policy and evidence, and produces a recommendation an operator can interrogate.
Approved actions route into the systems of record with full lineage from sensor frame to signature.
Physical signal→Governed evidence→Authorized action
Built around the stack you already run.
Deployment-flexible across client-owned environments, cloud data stacks, edge/OT environments, and enterprise AI platforms.
Assurance is a gradient — hosted under zero-retention terms, a dedicated environment, or your own cloud — and your operating record stays yours in every one of them.
See sovereignty · who owns what