Operational signal analysis
Analyze sensor streams, alarms, maintenance records, inspections, and operating context to identify emerging risks and abnormal patterns.
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Physical AI Decision Intelligence
FactVerse AI Agent
From operational signal to traceable action.
Physical AI decision intelligence for operational assets. FactVerse AI Agent analyzes live and historical signals, connects them with asset context, and turns findings into validated actions and Inspector work orders.
Decision Intelligence Layer
Prediction, anomaly analysis, evidence review, and execution workflows in one operating loop.
Growing AI Toolset
Natural-language access to prediction, analysis, and reporting workflows.
Always-on
Every asset gets its own continuously available AI data scientist.
Platform Capabilities
A Physical AI data scientist for every operational asset
Not a dashboard. Not a chatbot. A complete Physical AI workflow from data ingestion to model training to decision recommendation, automated and running around the clock.
Knowledge graph intelligence
Connect equipment, sensors, alarms, locations, procedures, and work orders so recommendations are grounded in asset relationships and site context.
Predictive maintenance support
Use trend analysis, anomaly detection, reliability signals, and maintenance history to prioritize assets that need attention.
Natural language operations
Let operators ask questions, review evidence, and generate operational summaries without leaving the decision workflow.
Asset-level intelligence
Move analysis from centralized dashboards to individual assets, equipment groups, facility zones, and workflows.
Decision center and closed loop
Move from anomaly to recommendation, human approval, Inspector work order, field execution, documentation, and verification.
How It Works
From raw data to validated action in three steps
Step 01
Connect your data
Connector options include REST, MQTT, OPC UA, BACnet, Modbus, JDBC, CSV, Fabric, and Templates. Pre-built integrations cover Siemens, Honeywell, Kepware, PI, Azure, and more.
Step 02
AI analyzes and recommends
AI Agent reviews trends, anomalies, asset relationships, and operating context. Knowledge graph reasoning traces cross-system causality, and each recommendation includes supporting evidence.
Step 03
Validate in the twin, then act
Twin Engine checks spatial conflicts, equipment logic, and workflow constraints in 3D before approved actions flow into execution systems.
Deep Dive
A Physical AI decision layer for operations
FactVerse AI Agent helps operations teams move from scattered signals to traceable action. It does not replace the systems that already run a facility. It connects their signals, adds asset and spatial context, and helps teams decide what needs attention.
Deep Dive
What it does
- analyzes alarms, sensor trends, maintenance records, and inspection findings
- connects findings with asset relationships and facility context
- helps prioritize maintenance and operational response
- summarizes evidence for human review
- routes validated findings into Inspector work orders
- preserves the record needed for follow-up and learning
Deep Dive
How it works with Designer
Process simulation, layout planning, virtual planning, and physics-based validation belong to FactVerse Designer. AI Agent can consume the context produced by those workflows, but it should be described as the decision intelligence layer, not as the owner of Designer simulation.
Deep Dive
Closed loop from insight to work
Every recommendation should move through a clear operating loop: signal ingestion, AI-assisted analysis, twin-context review, human approval, Inspector execution, and verification. This keeps Physical AI grounded in real operational work instead of disconnected analysis.
Industry Scenarios
One platform, any operations-intensive industry
The same decision intelligence layer can be deployed with industry-specific operating context while keeping execution workflows consistent.
Facility operations
Correlate alarms, environmental signals, equipment status, and maintenance history so facility teams can act earlier and with better context.
Predictive maintenance
Identify degradation trends, rank maintenance priorities, and route validated findings into Inspector work orders and field tasks.
Data center operations
Connect thermal signals, power data, cooling equipment, alarms, and operational context to support risk review and maintenance planning.
Semiconductor facility operations
Support cleanroom drift response, utility equipment monitoring, predictive maintenance, and Inspector execution workflows.
Why FactVerse
The only platform built for Physical AI with physics twin engines
Others can show, guess, or render. FactVerse can show, compute, validate, and execute Physical AI operations in one closed loop.
| Capability | BI / Dashboard | IoT Platform | AI Consulting | 3D Digital Twin | FactVerse |
|---|---|---|---|---|---|
| See problems | |||||
| Understand causes | |||||
| Predict trends | |||||
| AI-assisted review | |||||
| Physics validation | |||||
| 3D visualization | |||||
| Decision support | |||||
| Risk review | |||||
| Execution handoff |
ROI at a Glance
Measurable impact without replacing your operations stack
Actual outcomes vary by site, asset condition, workflow maturity, and integration scope.
Maintenance noise
Lower
with context-aware triage
Manual coordination
Less
with work order handoff
Response cycle
Faster
from finding to action
Decision review
Shorter
with twin context
* Outcomes depend on site scope, data availability, asset condition, and operating process maturity.
FAQ
Common questions from operations and transformation teams
It is the decision intelligence layer of FactVerse. It analyzes operational data, reasons over asset context, recommends next actions, and connects validated findings to execution workflows.
BI and IoT systems primarily show status. FactVerse AI Agent helps teams understand why a situation matters, what action should be reviewed, and how that action should move into a traceable workflow.
No. Process simulation and virtual planning are centered on FactVerse Designer. AI Agent can use context and results from Designer-led workflows, but Designer owns layout planning, process logic, scenario authoring, and physics-based validation workflows.
The twin adds asset relationships, spatial context, operational history, and execution context so recommendations can be reviewed before work is dispatched.
Data Fusion Services can connect BMS, SCADA, CMMS, EAM, IoT sensors, historians, databases, APIs, and other operational systems through standard interfaces.
Next Step
Decide with operational context
FactVerse AI Agent is built for teams that need Physical AI decisions, not just dashboards. Start with a focused operational workflow using your real data and facility context.
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