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A 32-page governance framework for enterprise AI — model risk management, audit trails, explainability, and the operating model that scales.

Most GenAI deployments fail at grounding. Three production patterns that actually work — drawn from regulated-industry deployments.

A global insurer deployed GenAI summarization on 1.8M annual claims, summarizing the case timeline at handler login. Average handle time down 41%.

A health system built an MLOps platform supporting 14 clinical-decision-support models. Model deployment time: 6 weeks → 3 days; full audit trail for regulatory review.

A practical session on what it takes to get an ML model from notebook to production. Covers monitoring, drift detection, and continuous retraining.

A tier-1 bank built real-time fraud-detection ML models scoring 4M transactions per hour. Fraud losses reduced 41%; false-positive rate dropped from 11:1 to 4:1.

A 3PL logistics company built ML-based demand forecasting across 12,000 SKUs. Stockouts dropped 41%; carrying-cost optimization saved $14M annually.