Ridgeline AIAn Element 29 Company
Platform

An operating layer for operational and physical AI.

Connect fragmented systems and physical sensing. Model the operation. Deploy governed AI workflows. Keep operators in control.

// stack
01 Connect
02 Model
03 Act
04 Govern
operator-in-the-loop
01 / four jobs · one operating layer

Connect. Model. Act. Govern.

01

Connect

Integrate systems, data, sensors, documents, field reports, APIs, and workflows.

ERPCMMSSCADAGISSensorsDocs
02

Model

Build the operational ontology around assets, people, places, tasks, risks, costs, approvals, and decisions.

AssetsPeoplePlacesTasksRisksCosts
03

Act

Deploy copilots, alerts, summaries, recommendations, approvals, decision rooms, and workflow surfaces.

CopilotsAlertsRecommendationsDecision rooms
04

Govern

Maintain human review, access controls, audit trails, permissions, and decision lineage.

Human reviewRBACAudit trailLineage

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

Assets
People
Places
Tasks
Risks
Costs
Approvals
Decisions

Every recommendation ties back to source data, the human who approved it, and the decision it shaped.

Connect → model → govern → decide.

// 02 / built for governed operations

Built for governed operations.

Ridgeline Mission Operations is being productized on Palantir Foundry and AIP to unify enterprise data, operational systems, field activity, documents, media, and physical signals within a governed operational ontology.

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, Databricks, cloud platforms, files, APIs, and operational systems, without making those upstream systems the user experience.

01

Operational Ontology

Connect assets, facilities, people, work, risk, evidence, obligations, and decisions in one shared operational model.

02

Governed AI Agents

Investigate cross-system issues, assemble supporting evidence, recommend interventions, and operate within defined permissions.

03

Human-Authorized Action

Route consequential actions through accountable users, approvals, audit history, and system-of-record writeback.

04

Mission Applications

Deliver executive command views, manager workspaces, field workflows, inspections, and mobile operational experiences.

Swipe capabilities · 01 / 04

// Architecture

signal → evidence → authorized action

  1. 01

    Operational data sources

    ERP · CMMS · CRM · Domo · Databricks · cloud · files · APIs · sensors

  2. 02

    Palantir Foundry / AIP

    Governed data, ontology, and agent runtime

  3. 03

    Ridgeline Mission Operations

    Product · domain logic · applications · experience

  4. 04

    Operators, managers, executives

    Human-authorized action with audit history

Ridgeline AI is an independent company. Palantir, Foundry, AIP, and Databricks are trademarks of their respective owners. No partnership, endorsement, or sponsorship is implied.

// 02b / build evidence

Ridgeline Mission Operations on Foundry and AIP.

Active development work, described plainly. Ridgeline builds the ontology, the domain logic, the agent boundaries, and the operator experience; Foundry and AIP provide the governed data and agent runtime underneath.

01

In build

Ontology objects

Assets, facilities, work orders, inspections, obligations, risks, evidence, and decisions modeled as linked objects rather than tables.

build maturity — indicative

02

In build

Pipelines and connections

Ingest patterns for ERP, CMMS, CRM, historians, Domo, Databricks, document stores, media, and sensor feeds into a governed operational layer.

build maturity — indicative

03

In build

AIP agents with bounds

Investigation and triage agents scoped to specific object types, tools, and permissions — with retrieval limited to what the user may already see.

build maturity — indicative

04

In build

Actions and writeback

Object actions that create work, escalate risk, or update records only after an accountable human authorizes them.

build maturity — indicative

05

In build

Operator applications

Command views, manager workspaces, inspection and field workflows, and mobile-first surfaces built for point-of-work use.

build maturity — indicative

06

In build

Audit and lineage

Decision history, approver identity, model inputs, and source lineage retained end to end for review.

build maturity — indicative

Swipe build notes · 01 / 06

Development status, not a certification or endorsement. Deployment patterns vary by client systems, permissions, and security requirements.

03 / what the platform produces

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.

04 / human-supervised

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.

Extension / physical AI

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 AI

01

Perceive

Cameras, lidar, thermal, acoustic, vibration, GNSS, and OT/ICS telemetry captured where the work happens.

02

Interpret

Edge inference turns raw signal into detections, states, and anomalies before anything leaves the site.

03

Model

Detections resolve against the mission ontology - the asset, the crew, the location, the procedure, the risk.

04

Decide

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

05

Act & prove

Approved actions route into the systems of record with full lineage from sensor frame to signature.

Swipe the stack · 01 / 05

Governance wraps every layer · perception is evidence, action is authorized

05 / deployment-flexible

Built around the stack you already run.

Deployment-flexible across client-owned environments, cloud data stacks, edge/OT environments, and enterprise AI platforms.

API-firstDesigned to integrate, not replace.
Cloud warehouse-readyWorks with modern data stacks where the client has selected one.
Edge / OT-awareOperates where data, devices, and decisions actually live.
Perception-readyIngests vision, lidar, thermal, and acoustic output alongside enterprise data.
Client-controlled environmentsDeployment patterns shaped to client security and data-residency requirements.
Enterprise AI-compatibleWorks with client-selected enterprise AI and data platforms.
Data-platform readyIntegrates with the data platforms the client already uses or selects.

See the platform applied to your operation.

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