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Semiconductor fabs, energy systems, manufacturing plants, aviation MRO, logistics facilities, and other complex sites.
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Executable Industrial Intelligence
FactVerse
Dual-Engine Platform for Executable Digital Twins
The dual-engine industrial intelligence platform from DataMesh. FactVerse combines a 3D twin execution engine with an AI decision engine so complex facilities can connect data, validate actions, and move from analysis to executable operations.
Connected data
Bring plant systems, enterprise records, and documents into one model.
Twin execution
Validate decisions in the operational 3D environment before release.
AI decisions
Run what-if analysis and next-best-action workflows with real context.
Deployment
Deploy in cloud, private, or hybrid environments while keeping data access and operational boundaries under control.
Engine One
Twin Engine executes in space
Twin Engine turns geometry, assets, telemetry, and workflows into an operational runtime. It is the environment where teams can visualize systems, validate decisions, and deliver executable digital twin applications.
Engine Two
AI Agent reasons over operations
AI Agent turns operational questions into what-if analysis, scenario comparison, and recommendations. It gives FactVerse a decision layer that can still be checked against the real facility before action.
Core Capabilities
The platform stack behind industrial digital twins
FactVerse combines data connectivity, twin runtime, AI decision support, and frontline applications in one operational system.
Twin Engine
- The 3D execution engine that turns geometry, assets, telemetry, and workflow logic into an operational digital twin.
- It provides the spatial runtime where teams can visualize systems, validate plans, and run scenario-aware applications.
AI Agent
- The decision engine that turns operational questions into what-if analysis, recommendations, and next-best actions.
- It connects AI agents, analytics, simulation, and optimization with the real facility context.
DFS Connectivity Layer
- Data Fusion Services connects SCADA, MES, ERP, historians, IoT platforms, documents, and enterprise systems so the platform can work with operational data instead of isolated demos.
Designer and Workflow Authoring
- Designer gives teams a way to model scenes, assets, behaviors, SOPs, and operational interfaces without rebuilding every application from scratch.
Open Application Layer
- Build frontline applications such as Director, Inspector, Checklist, Simulator, and robotics workflows on the same shared platform model and service layer.
Flexible Industrial Deployment
- Deploy as SaaS, private cloud, or hybrid architecture.
- FactVerse is built for facilities that need both enterprise integration and strict control over data, networks, and execution boundaries.
How It Works
Move from disconnected systems to executable operations
Step 01
Connect industrial systems
Use DFS to connect plant data, enterprise records, documents, and operational signals into one platform context.
Step 02
Build the twin runtime
Use Twin Engine and Designer to model scenes, assets, relationships, and executable workflows.
Step 03
Reason with AI
Use AI Agent to ask questions, compare scenarios, and generate recommendations across complex operations.
Step 04
Validate and execute
Check recommendations in the twin, then deliver them through applications and frontline workflows.
Overview
Built for complex facilities
FactVerse is no longer best described as a single digital twin engine. It is the full operating platform that connects data access, scene execution, AI reasoning, and application delivery for complex industrial environments.
That shift matters because most facilities do not struggle with one missing tool. They struggle with disconnected layers: data in one place, geometry in another, workflows in another, and decision support somewhere else. FactVerse brings those layers together under one architecture.
Overview
Dual-engine architecture
The center of the platform is a dual-engine model:
- Twin Engine is the execution engine. It creates the spatial runtime where assets, telemetry, zones, workflows, and simulations become operationally meaningful.
- AI Agent is the decision engine. It turns questions into analysis, scenario comparison, and recommended actions.
Together they move teams from "what happened" to "what should we do next" and then to "can this really work in the physical environment?"
Overview
Twin Engine
Twin Engine is the part of FactVerse that understands the physical world. It binds 3D scenes, asset relationships, process logic, and real-time signals into an executable runtime. That gives the platform a place where plans can be visualized, checked, and turned into guided operational experiences.
This is why Twin Engine is more than a viewer. It is the environment where maintenance, inspections, simulations, training, and operational validation can all run against the same facility model.
Overview
AI Agent
AI Agent is the part of FactVerse that helps teams decide. It supports natural language interactions, what-if analysis, optimization, forecasting, anomaly detection, and other decision workflows that are difficult to manage through dashboards alone.
Instead of stopping at an answer, AI Agent hands the recommendation back into the FactVerse environment so it can be reviewed against spatial and operational constraints before action is taken.
Deployment
Built to fit industrial security, data, and rollout requirements
Deployment Consideration
Connected data and authoring
FactVerse also includes the layers that make the two engines practical in real deployments:
- DFS connects enterprise and industrial systems to the platform.
- Designer models scenes, behaviors, interfaces, and operational workflows.
- Applications such as Director, Inspector, Checklist, and Simulator reuse the shared platform model rather than rebuilding data and twin logic each time.
This is what turns FactVerse into a platform architecture rather than a product demo.
Deployment Consideration
From insight to execution
The platform is designed to support a continuous loop:
- Connect plant, enterprise, and document data through DFS.
- Build and maintain the operational twin in Twin Engine.
- Ask questions and generate recommendations through AI Agent.
- Validate the result in the twin before release.
- Deliver actions through applications, workflows, and frontline interfaces.
That loop is what allows FactVerse to support the full decision-to-execution chain instead of stopping at visualization or analysis alone.
Deployment Consideration
Deployment for complex industrial environments
FactVerse is built for facilities where deployment choices matter as much as features. Some teams need a public cloud rollout. Others need private infrastructure, local data access, or hybrid connectivity between secure plant systems and shared authoring environments.
Because the platform spans data, simulation, AI, and operational interfaces, it can be introduced progressively: start with connectivity and twin visibility, add guided workflows, and then layer in AI-native decision support as teams are ready.
Use Cases
Where FactVerse creates measurable operational value
AI-Native Facility Operations
Give operations teams one platform for monitoring conditions, asking questions, running what-if scenarios, and validating the result inside the actual facility context.
Predictive Maintenance and Work Planning
Combine equipment health, real-time status, and spatial context so teams can decide what to service first, when to intervene, and how to execute the work with fewer surprises.
Executable Digital Twin Applications
Build guided operations, inspections, training, planning, and optimization workflows on a shared twin and data foundation rather than separate point tools.
FAQ
Common questions about the FactVerse platform
What is FactVerse now?
FactVerse is now the umbrella platform for DataMesh's dual-engine architecture. It combines Twin Engine for spatial execution and AI Agent for decision intelligence, supported by DFS and Designer.
Why position FactVerse as a platform instead of a single product?
Industrial teams do not need a disconnected viewer or a disconnected AI tool. They need a platform that can connect data, model the physical world, reason over decisions, and support execution across applications.
How do Twin Engine and AI Agent work together?
AI Agent analyzes questions, compares alternatives, and generates recommendations. Twin Engine provides the physical runtime where those recommendations can be checked against layout, assets, process dependencies, and operating limits before execution.
Is FactVerse only for visualization?
No. Visualization is one outcome, but the platform is designed for executable operations: inspections, maintenance planning, scenario analysis, training, optimization, and AI-guided decision workflows.
What deployment models does FactVerse support?
FactVerse supports cloud, private, and hybrid deployments so enterprises can match the platform to plant connectivity, cybersecurity, compliance, and regional rollout requirements.
Next Step
Connect data, validate in 3D, and operationalize AI in one platform
FactVerse helps industrial teams close the loop from data access to decision support, physical validation, and frontline execution.
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