DataOps readiness
Assess whether industrial data is ready for analytics, agents, and digital twins.
The DataOps Readiness Assessment reviews source access, tag quality, asset mapping, schema gaps, governance needs, and the shortest path to an AI-ready product.
Source access
Tag quality
Asset mapping
Governance gaps
API/MCP fit
Activation plan
A practical assessment for the first source and asset class.
Platform in action
One operating layer across data, physics, agents, twins, assets, and work
TwinEdge connects DataOps context, standard profiles, physics-based insights, spatial intelligence, AssetOps recommendations, and governed operational action in one platform experience.
DataOps Workbench
The DataOps workspace shows source health, asset binding, namespace readiness, recommendations, models, canonical graph, standards, and edge fleet.
GIS and digital twin context
The operational map connects layers, asset detail, telemetry, work-order creation, inspection creation, and digital twin context.
AssetOps PM recommendations
O&M-derived PM recommendations show source documents, schedule cadence, priority, confidence, and review status.
Capital planning recommendations
Capital Economist shows cost-risk simulation, active recommendations, scenario comparison, and budget utilization before funding decisions.
Physics-driven insights
Insights prioritize anomalies with severity, energy waste, cost impact, affected assets, and action-ready recommendation states.
Workflow
The operating workflow industrial AI needs
Connect industrial sources, build trusted context, govern recommendations, and turn approved decisions into operational work.
Connect the systems already in place
Use cloud connector, Collector, OT Bridge, or TwinEdge OS to reach SCADA, PLCs, historians, MQTT, SQL, files, REST, and edge systems.
Map signals to physics-aware context
Turn tags, topics, tables, and files into asset models, physics inputs, namespaces, graph relationships, digital twins, and AI-ready data products.
Govern recommendations before action
Agents explain, draft, validate, diff, request approval, and keep replay evidence so industrial teams can trust the output.
Capabilities
Platform capabilities
Industrial DataOps
A workspace for sources, tags, models, physics inputs, instances, pipelines, namespace, graph, catalog, APIs, MCP, and monitoring.
Governed agentic analytics
Agents use operational context, physics model outputs, and operating envelopes to prepare evidence-backed recommendations with dry-run plans and approval gates.
Execution layer
Recommendations move into AssetOps EAM, Field workflows, compliance evidence, APIs, MCP, and enterprise systems.
Engineering controls
Engineering controls for industrial AI.
TwinEdge can show real telemetry, local inference, protocol flows, and agent traces without claiming uncontrolled autonomy or SCADA replacement.
Read-only first
Physical writeback is disabled by default and recommendations pass through approval gates.
Replayable evidence
Plans, diffs, source context, and approval history remain available for review.
Deployment choice
Cloud-connected, local, and offline paths support evaluation without forcing one architecture.
Source system respect
TwinEdge works above SCADA, historians, CMMS, GIS, LIMS, ERP, and data lakes rather than pretending to replace them all.
Outcomes
Operational value
Teams get the context, controls, and execution path needed to move from noisy industrial data to approved operational action.
Operations leaders
See a path from plant data to work, risk reduction, energy improvement, and evidence without replacing every existing system.
Engineers and IT
Get source visibility, schema discipline, governance, replay, and deployment options for cloud, no-cloud, and hybrid sites.
Executive teams
Start with a focused operational workflow and expand the same context across analytics, twins, EAM, Field, and industry packs.
Maintenance and field teams
Turn approved recommendations into work orders, routes, mobile execution, closeout evidence, and history without losing source context.
Connected platform
Extend the same context across the operating layer
DataOps Workbench creates the physics-aware, AI-ready context.
Agentic Analytics uses that context to explain, draft, and validate recommendations.
TwinEdge OS supports cloud-connected, offline, and protocol-rich edge deployments.
AssetOps EAM and Field close the loop from recommendation to evidence-backed work.
Water, wastewater, chemical, water loss, lab, and facility products package industry workflows.
REST and MCP data products make context available to enterprise applications and AI systems.
Evaluate TwinEdge
Plan your first TwinEdge workflow.
Review the operating model with our team, or download TwinEdge to evaluate the platform in your own environment.