Store and transact.
- Maintain records
- Run defined processes
- Capture completed activity
- Require people to connect context
Platform / AI operational intelligence
One intelligence layer.
Across the entire operation.
PrometheonaIQ connects data, physical assets, people and workflows so an organization can observe change, understand cause, predict consequences, decide deliberately, execute with control and learn from verified outcomes.
Not another dashboard.
An operating layer.
Most enterprise systems are designed to store records, execute transactions or present historical metrics. They rarely preserve the full chain from an operational signal to the decision it creates and the outcome it produces.
PrometheonaIQ is designed around that missing chain. It combines integration, an operational knowledge graph, real-time sensing, causal analysis, forecasting, simulation, planning, workflow automation, frontline guidance, evidence verification, AI agents and organizational memory.
The result is a shared intelligence model that sits across existing systems rather than replacing them. ERP, CRM, SCADA, IoT, GIS, financial platforms, documents and field tools remain systems of record. PrometheonaIQ becomes the system for connected understanding and governed action.
Explain and answer.
- Present metrics
- Answer questions
- Summarize information
- Stop before controlled execution
Understand, decide and act.
- Connect operational context
- Trace causes and predict outcomes
- Simulate and plan
- Execute, verify and learn
Data becomes useful when it gains context. Context becomes valuable when it improves a decision. A decision becomes intelligence when its outcome is verified.
The continuous intelligence loop
Eleven stages.
One operating memory.
Every stage carries evidence forward and sends the eventual outcome back into the system.
Ingest transactions, telemetry, events, images, documents and frontline reports.
Resolve entities, relationships, time, location and operational context.
Identify unusual behavior, emerging risk and material change.
Trace contributing drivers and competing causal paths.
Estimate likely outcomes, horizons, confidence and key drivers.
Test alternatives and expose their estimated consequences.
Select an option with assumptions, evidence and uncertainty visible.
Translate intent into constrained resources, sequence and ownership.
Coordinate systems, agents, supervisors and frontline teams.
Confirm the right work happened to the right asset in the right way.
Learn is not a final box. It is the connection from every verified outcome back to the next observation.
PrometheonaMemory retains the incident, investigation, decision, action and result.
Explore the operating model
From signal to learning.
Observe
Observe the operating reality.
Connect live and historical signals across systems, assets, events and frontline reports. Preserve source, time, location and quality so downstream reasoning remains traceable.
CORE ELEMENTERP · CRM · SCADA · IoT
CORE ELEMENTEvents · telemetry · documents
CORE ELEMENTFreshness and quality controls
Layered architecture
Built as connected capabilities,
not isolated AI features.
Data & context
Connect systems, resolve entities and build the operational graph.
Intelligence
Sense, detect, explain, predict, simulate and remember.
Decision & planning
Compare options and produce a plan within real constraints.
Execution & evidence
Automate workflows, guide field work and verify the outcome.
| Layer | Primary question | Operational output | Evidence preserved |
|---|---|---|---|
| Connect + Graph | What belongs together? | Shared operational context | Source, entity, relationship, time, location |
| Sense + Detect | What changed and why does it matter? | Prioritized signal | Baseline, deviation, affected entities |
| Cause + Predict | Why did it change and what may follow? | Explanation and forecast | Drivers, confidence, horizon, causal paths |
| Sim + Plan | What should we do? | Scenario comparison and constrained plan | Assumptions, trade-offs, resources, risk |
| Flow + Field + Verify | Did the intended action really happen? | Coordinated work and trusted outcome | Approval, assignee, location, time, images, checklist |
Product ecosystem
Fifteen modules.
Progressively introduced.
Deploy only the capabilities required for the operating model, while preserving a common graph and evidence trail.
Data & Integration Fabric
Operational Knowledge Graph
Real-Time Operational Sensing
Anomaly Intelligence
Causal Intelligence
Predictive Intelligence
Decision Simulation
Planning & Optimization
Workflow Automation
Frontline Operations
Computer Vision Intelligence
Evidence Verification
Organizational Memory
Multi-Agent System
Operational Command Center
Human-in-the-loop by design
Autonomy proportional
to consequence.
Automate within boundaries.
Routine triage, enrichment, notification and low-impact coordination can proceed within defined policies, thresholds and audit controls.
- Explicit tool permissions
- Reversible actions
- Continuous monitoring
Require informed approval.
The system prepares the evidence, explanation, alternatives and plan. An authorized person approves before execution.
- Clear decision owner
- Visible evidence and uncertainty
- Approval captured in the audit trail
Recommendation only.
For decisions with severe safety, financial, regulatory or service consequences, PrometheonaIQ can remain advisory. It organizes the operating context and preserves the human decision without taking the action itself.
Deployment journey
Start with a decision,
not a technology inventory.
Choose an operational question.
Define the decision, its owner, current latency, required evidence and consequence of getting it wrong.
Connect the minimum useful context.
Bring together only the systems, entities and history required to explain and improve that decision.
Model the operating relationships.
Describe assets, sites, customers, events, work and outcomes in the operational graph.
Prove the intelligence loop.
Run detection, explanation, prediction and simulation against historical and controlled live scenarios.
Introduce governed execution.
Add approvals, workflow, field guidance and verification only after the evidence model is trusted.
Expand through reusable context.
Reuse the graph, memory and controls across adjacent decisions instead of rebuilding isolated solutions.
Platform questions.
Does PrometheonaIQ replace ERP, CRM, EAM or SCADA?
No. It is positioned as an intelligence and execution layer across existing systems. Systems of record continue to own their core transactions and data.
Is the platform only for real-time use cases?
No. It combines live signals with historical events, documents, decisions and outcomes. The appropriate latency depends on the operational question.
Can every module be deployed independently?
The modules are conceptual capabilities that can be introduced progressively. Exact packaging and integration depend on implementation scope.
How are predictions presented?
With a forecast, time horizon, confidence, key drivers, evidence and suggested action. They are estimates, never guaranteed outcomes.
How does the system learn?
It retains the investigation, decision, action, verification evidence and observed result in organizational memory for future reasoning.
Where does human approval fit?
Approval is part of the operating model. Low-risk actions may be automated, high-impact actions can require an authorized approver, and critical actions can remain recommendation-only.
See the operating loop
Move from a signal to a verified outcome.
Use the interactive demo to investigate risk, simulate a response, build a plan and advance the decision thread.