Built For
Semiconductor fabs, energy systems, manufacturing plants, aviation MRO, logistics facilities, and other complex sites building Physical AI operations.
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Executable Physical AI
FactVerse
Dual-Engine Platform for Physical AI and Executable Digital Twins
The dual-engine Physical AI 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.
Physical AI decisions
Run what-if analysis and next-best-action workflows with real-world 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 Physical AI 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 Physical AI decision layer that can still be checked against the real facility before action.
Executable Twin
From visualization twin to executable twin
A visualization twin helps teams see assets and spaces. An executable twin connects geometry, live data, operating rules, simulation, and work orders so decisions can be tested, approved, and carried into field execution.
See
Visualization twin
Show asset location, status, and spatial context so teams share the same operating picture.
Test
Executable context
Run scenario checks, AI recommendations, and workflow logic against the current state of the site.
Act
Closed-loop execution
Send approved actions into Inspector, Checklist, Simulator, or enterprise systems with traceable records.
Why it matters for Physical AI, robotics, and the factory brain
Physical AI, world models, and embodied intelligence need to understand how a real factory operates. Visual appearance and dashboard signals are only the entry point; AI and robots also need asset semantics, spatial relationships, process steps, equipment state, safety boundaries, work-order history, and simulation results. An executable digital twin organizes that context into a computable, verifiable, and traceable site model, so the factory brain can use real operating constraints when recommending actions, training robots, or testing scenarios instead of judging only from images and dashboards.
Core Capabilities
The platform stack behind Physical AI digital twins
FactVerse combines data connectivity, twin runtime, AI decision support, and frontline applications in one operational system for Physical AI.
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 Physical AI 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.
Data Fusion Services Connectivity Layer
- Data Fusion Services connects SCADA, MES, ERP, historians, IoT platforms, documents, and enterprise systems so the platform can work with operational data.
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 Data Fusion Services 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 Physical AI in 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 Physical AI 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 Physical AI 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:
- Data Fusion Services connects enterprise and industrial systems to the platform.
- Designer models scenes, behaviors, interfaces, and operational workflows.
- Applications such as Director, Inspector, Checklist, and Simulator share the platform model, data, and twin logic across releases.
Deployment Consideration
Product roles inside FactVerse
FactVerse is the platform umbrella. Twin Engine owns the executable spatial runtime. Data Fusion Services owns connectivity and data preparation. Designer owns scene authoring, process logic, virtual planning, and simulation workflows. AI Agent owns analysis, recommendations, and decision support. Inspector owns asset, inspection, work-order, and evidence workflows. Director owns guided SOP and training content. Simulator owns heavy equipment operator training.
Keeping those responsibilities separate makes the public story clearer: FactVerse connects the products into a Physical AI operating loop, but each product still has a specific role.
This is what turns FactVerse into a platform architecture.
Deployment Consideration
From insight to execution
The platform is designed to support a continuous loop:
- Connect plant, enterprise, and document data through Data Fusion Services.
- 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 across visualization, analysis, and execution.
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 across applications.
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 Data Fusion Services and Designer.
Why position FactVerse as a platform?
Industrial teams 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?
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 Physical 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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