One governed path: physical signal is perceived, interpreted, resolved against operating context, decided under policy, then recorded as an authorized action with lineage held from sensor frame to signature.

Illustrative operating records arranged in perspective for an accountable review

Operational + physical AI · Mission-critical systems

From signal to accountable action. Operational and physical AI that turns enterprise data and real-world signals into governed decisions.

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Applications

From enterprise data to real-world action.

Start with the operating question. Connect the evidence. Put a useful decision in front of the person accountable for it.

View every application
A representative enterprise review setting: schedules, cost records and contracts gathered for one decision
Needs attention

Delivery, cost and risk are moving apart on one program.

Schedule, financials and staffing all contribute. Contracts and risk do not.
Proposed next action

Review the variance with its schedule, cost and staffing context still attached.

Route the follow-up to the accountable owner.

A person reviews and decides. Ridgeline proposes; it does not act on its own.

What changes

One review path replaces disconnected schedule, cost, contract, and risk checks.

Program Performance Cockpit

Where are delivery, cost, and risk diverging?

Connect schedules, financials, contracts, staffing, and risk so leaders can review variance against the operating baseline before the next program decision.

Next step

Review the decision with the source context still attached.

Explore Program Performance Cockpit
A representative executive review room with program records set out for one decision

Business / executive

Where are delivery, cost, and risk diverging?

Program Performance Cockpit

Connect schedules, financials, contracts, staffing, and risk so leaders can review variance against the operating baseline before the next program decision.

One review path replaces disconnected schedule, cost, contract, and risk checks.

Review the decision with the source context still attached.

Explore Program Performance Cockpit
Illustrative scene: a technician walking a bright industrial pipe hall

Industrial / infrastructure

What should the team inspect next?

Maintenance Intelligence

Relate work history, condition signals, inspections, and parts to asset criticality so maintenance teams can challenge and prioritize the next action.

Maintenance priority reflects condition, consequence, work history, and readiness together.

Move a traceable recommendation into the maintenance plan.

Explore Maintenance Intelligence
Illustrative scene: a field operator with a tablet beside staged mission equipment

Field / readiness / logistics

What keeps the operation from moving?

Field Operations Copilot

Give field teams the job plan, asset history, procedures, and current exceptions at the point of work, then route findings back to the accountable owner.

The point of work gains current context while findings return to the accountable owner.

Keep the field decision and its closeout connected.

Explore Field Operations Copilot
A representative multi-site property environment

Facilities / portfolio

Which sites are drifting, and why?

Portfolio Intelligence

Bring occupancy, maintenance, finance, and compliance into one portfolio view so operators can trace variance to sites, assets, and operating conditions.

Portfolio variance becomes a traceable site-level operating question.

Focus the next intervention on the driver, not the symptom.

Explore Portfolio Intelligence
Illustrative operator portfolio at dusk

Illustrative

FLAGSHIP APPLICATION

VULCAN

The operating system for Operational + Physical AI across eight industries.

Connect enterprise evidence, physical signals and authorized action.

Start with the diagnostic

ESTABLISHEDSelected into Palantir Startup Fellowship Cohort 003.Read the announcement

Industries

Eight operating worlds. One accountable discipline.

Each industry brings its own decisions, constraints and physical reality.

Explore all industries →

Why Ridgeline

Built with the operation, not around it.

Enterprise evidence. Physical context. Human authority.

  1. Approved cameras, lidar, thermal, acoustic, vibration, GNSS, and OT/ICS telemetry can be captured where the work happens.

  2. Edge inference can turn raw signal into detections, states, and anomalies before anything leaves the site, where the deployment requires it.

  3. Detections can resolve against the mission ontology - the asset, the crew, the location, the procedure, the risk - when source mappings are approved.

  4. Reasoning is bounded by policy and evidence, and produces a recommendation an operator can interrogate.

  5. Approved actions can route into systems of record with lineage from sensor frame to signature, when writeback is in scope.

Physical signalGoverned evidenceAuthorized action

01 / Perceive

Environment, assets, and OT signal → Time-stamped raw capture

Fixed and mobile sensing · Mobile capture · OT / ICS signal · Inspection media

02 / Interpret

Raw capture → Detections, states, anomalies

Computer vision · Anomaly detection · Sensor fusion · On-device inference

03 / Model

Detections → Resolved asset and mission context

Asset resolution · Spatial context · Condition history · Procedure linkage

04 / Decide

Context and policy → Evidence-backed recommendation

Bounded autonomy · Evidence-backed · Operator review · Escalation paths

05 / Act & prove

Authorized recommendation → Record of action with lineage

Work order write-back · Task dispatch · Audit trail · Replay and reversal

Domain-shaped applications

Built for the operating decision, not generic chat.

Real workflow integration

Connect records and signals to the people doing the work.

Physical-world engineering

Field conditions shape decisions about assets and crews.

Accountable delivery

Source context, permissions and human review stay connected.

Conceptual operating model. Sector coverage is not a claim of deployed customer results.

Sovereignty · who owns what

Your operating record stays yours.

Keep your data, models and decision rights portable across seven governed layers.

Review the sovereignty model →
01 / Data boundary

Operational records Data location, retention and access remain explicit

  • Ownership
  • Data location
  • Export

Seven layers build from data boundary through compute, model choice, semantic definitions, workflows and decision rights to a retained operating record.

1 / 7

01 / Data boundary

Operational records → Data location, retention and access remain explicit

Ownership · Data location · Export

02 / Compute & runtime

Approved environments → Workloads execute inside the chosen boundary

Hosted · Dedicated · Your cloud

03 / Model choice

Client-selected models → Models remain replaceable inputs, not the record

Portable · Versioned · Replaceable

04 / Semantic definitions

Metrics, entities and rules → Meaning is governed outside any single model

Ontology · Definitions · History

05 / Governed workflows

Context and operating procedure → Recommendations follow bounded, reviewable paths

Context · Procedure · Lineage

06 / Decision rights

Roles, permissions and thresholds → Consequential action stays with authorized people

Authority · Approval · Audit

07 / Operating record

Sources, decisions and outcomes → A portable record the organization retains

Evidence · Decisions · Outcomes

Start with one operating decision

Bring us the decision that matters.

Tell us what must change, who owns the outcome, and where the evidence lives. We’ll start there.