Holistic protection and seamless traceability for cybersecurity, functional safety, and the Cyber Resilience Act
Overview
Methodological excellence and tailored tools for model-based system and software engineering.
Enterprise software from a single source: AI integration, legacy migration and full-stack development — cost-efficiently and sovereignly hosted.
Overview
Model-based approaches for systems and software engineering, combined with custom tool development and seamless toolchain integration — pragmatic methods and tailored tool landscapes from a single source.
With itemis CREATE as the workbench for state machines and itemis ANALYZE as a hub for the integration of heterogeneous tool landscapes.
Methods and tool expertise is what itemis brings to the table. We help engineering teams optimise their entire development process — from requirements engineering through system analysis and design to software engineering, implementation and test. We close gaps with individually developed tools, ensure seamless integration of heterogeneous tool landscapes and create continuity.
The four topic areas reflect exactly that: Model-Based Systems Engineering, model-driven software development, custom tool development and toolchain integration.
MBSE only delivers value when applied pragmatically: as a means to reduce complexity and integration risks in systems development, not as a compliance exercise.
Here you will find out when MBSE concretely saves time and money, when it does not fit, and what the path from methodology selection through tool choice to implementation looks like.
Model-driven development unfolds its value when the model takes on the role of single source of truth: the foundation for reproducible code generation and early error detection, not a documentation format.
Here you will learn how formal models make complexity manageable, how to validate and simulate early and generate code deterministically and whether MDSD is still relevant in the age of AI.
Every domain has its own language. Where standard tools fall short, we develop custom tools that speak exactly that language.
Here you will learn when a domain-specific tool pays off and when standard tools suffice, how AI changes the economics of tool development and how itemis shapes the path from domain analysis to the delivered tool.
Engineering toolchains grow over years and are rarely coherent by design: requirements management, system modelling, test management, implementation. Four tasks, four tool breaks, no end-to-end continuity.
Here you will learn why toolchain integration is critical, what integration levels exist and how itemis approaches tool selection.
Schedule a call with Dr. Alexander Nyßen and Andreas Mülder.
the engineering workbench for state machines
Requirements Traceability for Engineering Toolchains
We staff projects with people who have already worked together — and with engineers who are motivated because we take them seriously as professionals. More than 80% of our engineers have been with itemis for over three years.
Audits, release weeks, crisis sprints — we stay calm, roll up our sleeves and put in the extra hours. Dependability that is not written into the contract.
No diplomatic rounds, no manoeuvring. We say openly when something is not workable. Problems are solved with a call, not a memo. Sparring partner, not supplier.
We don't chase you with sales conversations. Instead, we invest in the relationship — sometimes more than the current budget allows. Those who know us know: when it needs to work, they call itemis.
System Composer or a SysML tool for your architecture modeling? A comparison across requirements handling, simulation, onboarding, stakeholder views, AI integration, and cost — and why the answer depends on your project.
Read Article
Benjamin Alders
7 min read
How the Portalon plugin connects AI coding agents like Claude to the live model of a JetBrains MPS project via MCP — structurally safe edits, validation, and language-engineering skills, on your current MPS version.
Read Article
Dr. Klaus Birken
10 min read
How to use UML profiles in Enterprise Architect and how to process profiled models with the Eclipse-based EA-Bridge for code generation using Xtend.
Read Article
Dr. Patrick Könemann
6 min read
How to extend Enterprise Architect with add-ins: model assistants, integrity checks, and installer-based rollout — and which tasks are better handled by external tools like the EA-Bridge.
Read Article
Dr. Patrick Könemann
7 min read
How Eclipse with Xtend and the Java-based EA-Bridge provides a proven alternative to EA's built-in code generation — and how the AUTOSAR Consortium uses this approach headlessly at scale.
Read Article
Dr. Patrick Könemann
6 min read
How the Eclipse-based EA-Bridge loads Enterprise Architect models as Eclipse UML models, reports syntactical errors, and enables custom validation rules with quick fixes.
Read Article
Dr. Patrick Könemann
5 min readDräger relies on itemis CREATE to generate firmware and UI code for its medical ventilators and incubators.
View ProjectMAGNOTHERM cut firmware source code creation and maintenance effort using the C++ code generator in itemis CREATE.
View ProjectMAN Energy Solutions uses itemis CREATE to generate platform-independent C code for engine firmware.
View ProjectKOSTAL now integrates bidirectional UML modeling seamlessly into its existing Enterprise Architect workflow, thanks to itemis.
View ProjectMDSD and AI are not competitors. AI generates statistical code without conformance guarantees — the same prompt yields different results, without determinism or traceability. MDSD delivers exactly what AI structurally cannot: reproducible generation, auditability and qualifiable tools. In safety-critical development under ISO 26262, DO-178C or IEC 61508, determinism is not an option — it is a prerequisite.
AI is an effective accelerator today for building models: translating requirements texts into initial model drafts, checking models for consistency, suggesting refactorings. The model remains the deterministic source of truth — AI accelerates the path to get there.
The four topic areas are interconnected levels of the same problem. MBSE addresses the system level: requirements, architecture and system behaviour in a consistent model. MDSD translates this knowledge into executable, generated code — deterministically and platform-independently. Custom Tools encode domain knowledge directly into the tool: as validated models, enforced rules, consistent generation. Toolchain Integration ensures that all these tools communicate end-to-end and that traceability is maintained across tool and discipline boundaries.
Combining all four levels yields engineering knowledge that is structurally anchored in the system — not in the heads of a few key individuals.
