A Companion Specification is a standardized OPC UA Information Model created by an industry working group to define the domain-specific semantics, object types, and behaviors for a particular vertical or device class. It extends the core OPC UA framework by providing a pre-defined, vendor-agnostic Address Space that allows systems from different manufacturers to exchange data with a shared, machine-understandable meaning, enabling true plug-and-produce interoperability.
Glossary
Companion Specification
A standardized OPC UA Information Model developed by industry working groups to define domain-specific semantics for verticals like robotics, machine vision, or machinery.
DOMAIN-SPECIFIC INFORMATION MODEL
What is a Companion Specification?
A formal, standardized OPC UA Information Model developed by industry working groups to define domain-specific semantics for verticals like robotics, machine vision, or machinery.
These specifications are authored by collaborative bodies like the OPC Foundation, VDMA, or Euromap to model the unique attributes of specific assets, such as a robotic arm's joint positions or an injection molding machine's process parameters. By mandating a common vocabulary of Nodes and References, a Companion Specification eliminates the need for custom, project-specific data mapping, drastically reducing integration time and engineering cost for CTOs deploying multi-vendor automation systems.
Domain Semantics
Key Characteristics of Companion Specifications
Companion Specifications transform generic OPC UA data exchange into semantically rich, plug-and-produce interoperability by defining standardized Information Models for specific industries and device types.
01
Vendor-Agnostic Type Definitions
Companion Specifications define ObjectTypes, VariableTypes, and ReferenceTypes that abstract away proprietary interfaces. A robotic arm from Vendor A and Vendor B both expose an identical IRobotControllerType with a standardized JointCount Variable and MoveToPosition Method. This eliminates custom driver development and enables true multi-vendor interoperability on the factory floor.
02
Industry Working Group Governance
These specifications are not created by the OPC Foundation alone. They are authored and maintained by Joint Working Groups comprising domain experts from competing manufacturers, end-users, and system integrators. Examples include:
- VDW (German Machine Tool Builders' Association) for OPC UA for Machinery
- VDMA (Mechanical Engineering Industry Association) for OPC UA for Robotics
- Euromap for plastics and rubber machinery This collaborative model ensures specifications reflect real operational requirements, not theoretical ideals.
03
Semantic Inheritance and Specialization
Companion Specifications build upon the core OPC UA Base Information Model and other base specifications through subtyping. For instance, MachineToolType may subtype a generic ProductionEquipmentType, inheriting base properties like Manufacturer and SerialNumber while adding specialized components like SpindleType and ToolMagazineType. This layered architecture ensures consistency across domains while allowing deep vertical specialization.
04
Instance-Level Identification
Beyond defining types, Companion Specifications mandate the use of standardized identification schemes for instances. A specific machine on the shop floor is identified using a combination of:
- Application URI: Uniquely identifies the OPC UA Server instance
- ProductInstanceUri: A globally unique identifier for the physical asset
- DeviceName: A human-readable, plant-floor designation This multi-faceted identification enables asset tracking and digital twin binding across the entire lifecycle.
05
Mandatory and Optional Facets
To balance interoperability with flexibility, Companion Specifications define ConformanceUnits—grouped sets of Nodes and behaviors that a Server must or may implement. A basic vision sensor might implement only the Core Identification facet, while an advanced system implements Multi-Region Inspection and Calibration Management. Clients can discover supported facets at runtime by browsing the Server's ServerCapabilities Object, enabling graceful degradation.
06
Cloud Library Centralization
All released Companion Specifications and their NodeSet2.xml files are published to the OPC UA Cloud Library (https://uacloudlibrary.opcfoundation.org). This centralized repository allows:
- Developers to browse and download standardized type definitions
- Tools to automatically generate code stubs and address space skeletons
- Validation of Server implementations against the canonical specification This ensures that the entire ecosystem works from a single source of semantic truth.
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Frequently Asked Questions
Clear answers to the most common questions about OPC UA Companion Specifications, their role in industrial interoperability, and how they enable plug-and-produce automation.
An OPC UA Companion Specification is a standardized Information Model developed by an industry working group that defines domain-specific semantics for a particular vertical or device type. While the core OPC UA framework provides the generic communication infrastructure, a Companion Specification populates the Address Space with pre-defined ObjectTypes, VariableTypes, and ReferenceTypes that represent real-world entities like robots, machine tools, or vision systems. For example, the OPC UA for Robotics specification defines a standardized RobotType with variables for joint positions, operational mode, and execution state, allowing any compliant client to monitor and control a robot from any manufacturer without custom drivers. These specifications are stored and versioned in the OPC UA Cloud Library, ensuring semantic consistency across projects and enabling true plug-and-produce interoperability.
About the author
Prasad Kumkar
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
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