An Application for Model-Based Guided Engineering

Model-based systems engineering (MBSE) deliverables are encumbered by time-intensive tasks such as manual model creation, documentation, and traceability analysis to ensure elements of the system model support tracking of requirements, design decisions, and system behaviors. To reach digital transformation milestones in condensed schedules, model-based guided engineering (MBGE) leverages generative artificial intelligence (AI) to accelerate MBSE development of complex systems using generative systems engineering (GSE) data. Booz Allen developed an AI software tool, the MBSE AI Platform for Productivity (MAPPy) which interfaces with Cameo MagicDraw, to harness MBGE and address existing bottlenecks in MBSE practices. The tool provides an interface for systems engineers to optimize workloads with advanced capabilities to support data literacy, MBSE frameworks, and vast program knowledge bases. This paper describes how MAPPy streamlines MBGE to develop GSE data for comprehensive MBSE technical baselines by integrating generative AI, retrieval-augmented generation (RAG), and agential workflows to remove redundant, timeconsuming tasks and accelerate overall model development.