As AI models grow in complexity and regulatory scrutiny intensifies under frameworks including  California’s AB-2013 and the EU AI Act, software teams face a challenge beyond delivering great code: They need to produce comprehensive, auditable model documentation before the models are released. Model cards describe how a model works, its intended use and license, training data, performance, and limitations. They promote transparency and accountability so downstream users—customers, regulators, and affected communities—can make informed decisions when selecting and deploying AI. That audience extends beyond developers: Policymakers, procurement teams, and risk assessors rely on model cards to evaluate fitness for use and compare models across vendors. In practice, creating model cards manually is tedious and slow. Documentation lags behind development, and metadata is often outdated by ship date.…