Inspect the sources
Connect plant and business systems, then understand the tags and records they expose.
TwinEdge DataOps Workbench
Connect once. Use the context everywhere.
AI-native industrial DataOps.
TwinEdge DataOps is AI-native industrial data operations. Connect the systems you already have. Give agents, twins, and the CMMS the same trusted picture.
AI-native industrial DataOps AI-NATIVE DATAOPS
Bring OPC UA, MQTT, historians, files, and enterprise systems into one workbench.
START WITHTags · tables · documents
TWINEDGE CONNECTSFrom source to shared context
MOVE FORWARD WITHTwins · agents · APIs
THE WORKFLOW
From industrial source to governed data product
Build the asset context
Map instances, relationships, standards, and namespace paths into one model.
Publish reusable data
Serve the context through pipelines, REST, and MCP, with source lineage attached.
Less repeated mapping
A reusable asset model replaces separate mappings for every application.
Traceable answers
Follow a result back through the model to its source.
Faster next use case
Reuse connectors, profiles, and industry packs as the operation grows.
FROM THE PRODUCT
See the work in context.
Select a screen to explore the workflow. Open it for a closer look.
01 / INSIDE TWINEDGE
Follow source to context
Connection health, asset binding, namespace readiness, and recommendations sit in one workbench.
BUILT FOR THE OPERATION
Industrial DataOps capabilities
Connections and conditioning
Source catalog, tag browser, topic inspection, schema review, unit normalization, quality checks, freshness, transformations, and readiness scoring.
Models, pipelines, and UNS
Reusable asset models, instances, transformations, pipelines, namespace design, canonical graph bindings, profile projections, and operational standards.
REST and MCP publishing
AI-readable data products with catalog entries, schemas, tenant scope, monitor, lineage, approval review, audit trails, and default read-only MCP access.
GO DEEPER
Details for your operating context.
Industrial DataOps that does not stop at contextualized data.
Standalone DataOps hubs can make plant data cleaner and easier to consume. TwinEdge uses that same governed context as the foundation for physics twins, agents, work execution, field evidence, and operational learning loops.
- Physics-aware operational context. Tags, topics, records, and files can bind to asset models, operating envelopes, process twins, failure modes, and physics model inputs.
- Governed AI and MCP by default. Published REST and MCP products carry schemas, scope, catalog metadata, approval state, audit history, and replayable source context.
- Action loop beyond the data layer. Validated context can flow into Agentic Analytics, AssetOps EAM, Field, GIS-aware response, BI, reports, and evidence-backed work closeout.
Supported source families, namespace patterns, and standard-profile projections
TwinEdge uses a native canonical graph, supports MQTT/Sparkplug and Unified Namespace patterns, then projects into standard profiles where relevant so teams can keep local operating context and still support exchange patterns.
- CSV
- JSON
- Parquet
- OPC UA
- MQTT
- Sparkplug B
- Modbus TCP/RTU
- SQL
- REST
- Webhooks
- Event Hubs
- S3
- Azure Blob
- Google Cloud Storage
- GeoJSON
- Shapefile ZIP
- KML
- GeoPackage
- GPX
- WaterOS
- WastewaterOS
- OGC WaterML
- SensorThings
- SOSA/SSN
- EPA WQX
- SWAN
- i3X
- ISA-95
- AAS
- Haystack preview
- Brick planned
- ASHRAE 223 planned
CONNECTED BY DESIGN
Fits the systems around it.
Keep source context available as the workflow moves between teams and applications.
One connected record.
TwinEdge DataOps Workbench
- 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 TwinEdge Field close the loop from recommendation to work, guided mobile diagnostics, human-reviewed evidence, and closeout.
- Water OS, Wastewater OS, TwinEdge Chemical, Water Loss OS, Water Quality, Capital Planning, ESG, and Facility OS package industry workflows.
- REST and MCP data products make context available to enterprise applications and AI systems.
BEFORE YOU START
A few useful answers.
What is industrial DataOps?
Industrial DataOps is the governed process of connecting OT and IT sources, validating and contextualizing their data, mapping it to reusable asset models, and publishing trusted data products for analytics, twins, applications, and AI agents.
Does DataOps Workbench support a unified namespace?
Yes. Teams can organize MQTT and Sparkplug topics, asset models, canonical graph relationships, standards, and profile projections into a governed namespace rather than exposing raw tag names to every consumer.
Which industrial data sources can TwinEdge connect?
Supported source families include OPC UA, MQTT and Sparkplug, historians, databases, files, REST APIs, cloud storage, GIS, and enterprise systems. Deployment-specific connector support should be confirmed during evaluation.
How does MCP fit into industrial DataOps?
TwinEdge can publish selected operational context through tenant-scoped, audited MCP tools so approved AI clients work from modeled assets and governed data products instead of direct source credentials.
Does TwinEdge replace an industrial historian?
Not necessarily. TwinEdge can work with existing historians and add contextualization, lineage, governed publishing, digital twin inputs, and operational workflows above them.
THE NEXT CONNECTION
Explore the product familyBuild the rest of your loop.
START WITH YOUR OPERATION
Put the next decision in motion.
Discuss your sources, assets, and the outcomes your team wants to improve.