
Accelerating engineering process rollouts with AI
AI is increasingly pivotal in R&D acceleration, helping engineering teams to more rapidly adopt new processes. AI chatbots remove the need to navigate through processes. AI-enabled modelling assistants accelerate process definition and increase consistency.
Trust the process
Instead of manually navigating complex documentation or searching for specific details, engineers can simply chat with a purpose-built bot. These chatbots use process knowledge graphs, so they can provide personalized and context-aware answers to user queries right when they need them.
Users can gain trust in the accuracy of the given answers through evidence notes and links to the original sources of the defined processes, an important capability also enabled by the process knowledge graph.
As the chatbots learn more about an engineer’s specific work context, they can start tailoring their responses to align with relevant process variants. For example, they’ll know whether a target product is safety-relevant or not and adjust their guidance accordingly. They can even adapt to workflows unique to specific programs, projects or teams.
The process knowledge graph
A model-based approach to managing engineering processes — such as our proven WHO-WHAT-WHEN-HOW-WHY metamodel — enables the transformation of context-rich process models into a structured hierarchical graph.
This process knowledge graph plays an important role as the bridge between engineering data, product life cycles and the business context. Furthermore, it provides all product development participants with fine-grained, cross-disciplinary orientation and transparency, driving coordination and collaboration.
Most importantly, it is a centerpiece for any AI-driven acceleration of the product development life cycle. It is the ideal format to fuel AI information systems based on graph-based retrieval-augmented generation (Graph RAG), which enhances information accessibility and usability.

Process knowledge graph-based systems have significant advantages over document-based AI approaches or simple process diagrams because they provide finer-grained information structure and substantial context. Combining both is key to lowering the rate of hallucinations and improving attention to detail compared to other AI approaches.
Supercharged process definition
Stages uses AI to propose process descriptions, detailed steps, checklists and more. Its context-aware modelling assistant can generate activities for new processes or streamline existing process descriptions. All content generated by Stages AI takes modeling guidelines and best practices into account.
UL Solutions Software Intensive Systems values your trust and stands committed to Responsible AI. This means that Stages AI upholds the principles of transparency, privacy, safety and security. People continue to be the center of effective process management, and Stages AI empowers people to define and use processes — much more efficiently than ever before.
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