Data Review & Curation: Audit incoming E/E design data, including electrical schematics, wiring diagrams, harness designs, component libraries, netlists, and electrical network models from tools such as Capital, ePlan, Zuken, and related platforms, to ensure it accurately represents real-world engineering intent. Data Augmentation and Synthetic Data Generation: Use E/E design tool APIs and scripting languages such as Python to generate variations of electrical schematics, wiring harnesses, network topologies, component configurations, and electrical design scenarios to augment training sets.