Intelligent MBSE™ in Action: AI-Assisted Requirements and Digital Traceability

See how STC’s Intelligent MBSE™ approach combines AI-assisted requirements analysis, systems modeling and product-line engineering to support complex, regulated product development. In this demonstration, STC uses a medical imaging device system model in Cameo Systems Modeler and SysML to show how engineering teams can move from source documents to connected, traceable system models. The demonstration covers: AI-assisted extraction of candidate requirements from standards and regulatory documents Preservation of source-page and section information Engineer review and import of requirements into the system model Traceability across requirements, logical architecture and physical architecture Integration of use cases, interfaces, FMEA and risk data Product-line engineering using 150% and product-specific 100% model configurations Earlier identification of design gaps and downstream impacts A five-person STC team completed the preliminary AI-enabled analysis and proof of value in 200 hours, demonstrating how proven MBSE methods, AI-assisted capabilities and engineering expertise can help make model-based development more practical across regulated industries. Chapters 00:00 Introduction 00:52 Applying MBSE to medical device development 01:10 Model organization and navigation 04:04 Development challenges and objectives 05:26 Metamodel and requirements structure 07:32 AI-assisted requirements extraction 10:45 Functional architecture and use cases 12:15 FMEA, risk and requirements traceability 12:46 Physical architecture and product variations 15:36 Results and key takeaways 16:44 Preliminary proof-of-value results Learn more about Intelligent MBSE™: https://stc.arcfield.com/intelligent-... Explore STC’s digital engineering capabilities: https://stc.arcfield.com/ #IntelligentMBSE #MBSE #DigitalEngineering #SystemsEngineering #ArtificialIntelligence #ProductLineEngineering