ODOS uses AI, industry knowledge and connected semantic models to build software around your assets, processes and rules—making custom systems affordable to develop and adapt as your business evolves.
Standard enterprise software covers common business functions. But niche industries and specialized operations often need capabilities it doesn’t provide. Custom systems have traditionally been too expensive to build, maintain and adapt—leaving teams to fill the gaps themselves.
Assets, requirements and processes span ERP, PLM, MES and other tools. Spreadsheets, emails and manual workarounds connect what the software leaves disconnected.
Essential information sits in documents, separate databases and the heads of experienced employees. Teams spend time searching, asking and reconciling conflicting information.
Departments, suppliers and project partners work with different tools and information. Keeping everyone aligned means repeated data entry, status requests and costly misunderstandings.
A changed requirement, component or process can affect many other parts of the business. Without explicit dependencies, teams struggle to identify the consequences and keep plans, systems and documentation consistent.
Laws, standards and company policies must be translated into requirements, checks and evidence. Disconnected information makes this work repetitive and decisions difficult to trace.
When business knowledge is buried in documents and disconnected systems, generative AI must infer meaning, relationships and rules—introducing ambiguity and errors. Production applications need semantically defined data that makes this context explicit, providing a foundation for reliable AI.
ODOS—the Ontology-Driven Operating System—helps you create and maintain digital twins of your business, for example a production line or a building, including its assets and operating processes. These twins provide a shared semantic foundation for applications, workflows and AI agents.
ODOS turns the knowledge already present in your people, documents and systems into a shared semantic foundation. The result is a living digital model that can generate and continuously adapt the software your operations need.
Combine ontologies, domain knowledge and normative frameworks into an industry-specific semantic foundation.
AI interviews, documents and connected systems define your assets, processes, responsibilities and company rules.
Represent specific assets and processes with their structures, states, dependencies and requirements.
Generate data structures, APIs, workflows and applications—or connect the capabilities you already use.
Employees and AI agents check requirements, analyze dependencies, evaluate scenarios and coordinate actions.
Updates from systems, documents and follow-up interviews keep models, operations and software current.
Engineering an automated production line brings together mechanical, hydraulic and electrical design, robotics, controls, material flow and safety requirements. Equipment suppliers, engineering teams and customers must coordinate closely—yet specifications, component data and decisions often remain scattered across tools and documents.
ODOS connects requirements with the line’s assets and processes: production cells, robots, tooling, feeders, control systems and their dependencies. Expert interviews capture practical knowledge, while engineering documents and existing systems supply technical data.
Participating teams get a shared view of specifications, interfaces, responsibilities and progress. Requirements link to components and acceptance criteria, making handovers and commissioning easier to check against the agreed design.
When a robot, tool or production requirement changes, ODOS identifies affected interfaces, process steps and suppliers.
The engineering model can continue into commissioning and operation, supporting troubleshooting, maintenance and future reconfigurations.
Example: Connect feeding, robotic handling and welding in one model for an automated door assembly line. If a replacement robot has a different reach or cycle time, trace which workstations, tooling, controls and process sequences need review.
The value: Less manual coordination, earlier visibility of conflicts and a consistent information foundation from initial design through commissioning and operation.
Construction projects bring together clients, architects, engineers, contractors and authorities. Requirements, designs, schedules and evidence remain spread across BIM models, documents and separate systems. Keeping everyone aligned creates substantial coordination effort—and changes can trigger costly rework.
ODOS connects building elements and technical systems with requirements, construction processes, responsibilities and dependencies. It combines BIM data, project documents and expertise captured through AI interviews in a shared semantic model.
Building regulations, client requirements and project rules can be linked to relevant spaces, components and systems. Teams can coordinate interfaces, decisions and progress while connecting tasks, deliverables and acceptance criteria across disciplines and organizations.
When a layout, material or technical system changes, ODOS identifies affected disciplines, requirements and procurement packages. Validated project information can be reused for submissions, reports, compliance evidence and handovers.
Example: Connect the room program and applicable requirements with classrooms, escape routes, ventilation and electrical systems when building a school in Saxony. If classroom capacity changes, identify which designs, compliance checks and tender packages need review.
The value: Less administrative effort, better collaboration and traceable information and change management from planning and approval through construction and operation.
Critical infrastructure depends on interconnected assets, operating procedures, regulations and teams. Knowledge is often distributed across technical documents, maintenance systems, supplier information and experienced employees, making it difficult to assess dependencies and respond consistently to change.
ODOS creates a shared semantic model for assets, processes, responsibilities and requirements. Digital twins give operators and AI agents the context to analyze situations, check compliance, coordinate work and evaluate the consequences of planned changes.
The value: A reliable information foundation for resilient operations, clearer decision support, traceable compliance and better preservation of operational expertise.
Explore the resources behind the operating system: how industry knowledge becomes an enterprise model, a digital twin and an operating foundation.
01 White paper ENGLISH / COMING SOON
02 White paper DEUTSCH / Lesen / Herunterladen ↗03 Demo video ENGLISH / COMING SOON
04 Demo video DEUTSCH / COMING SOON
05 API documentation COMING SOON
Select a focused operational challenge, capture the knowledge behind it, validate the model with experts and implement an initial application. ODOS gives you a practical path from first value to a broader operating system.
Choose a process where fragmented information, coordination or change creates measurable friction.
Combine interviews, documents and existing systems, then review the model with the people who know the work.
Turn the validated model into a useful workflow, system of record or AI-supported operational tool.
Connect adjacent processes and systems while keeping the enterprise model and digital twins aligned.
Tell us where fragmented information is slowing your organisation down.