Demand and capacity forecasting, failure and outage prediction, risk probability modeling, and customer behavior and churn prediction. Models that help organizations anticipate what's coming rather than explaining what already happened.

When the question isn't what will happen but what should we do, prescriptive models provide the answer. Resource and capacity optimization, cost and performance trade-off analysis, decision support under regulatory constraints, and scenario-based recommendations that give teams something to act on rather than just consider.

Some of the most valuable things a model can do is notice what doesn't fit. Fraud and misuse detection, operational anomaly identification, compliance and policy violation detection, and early signals of systemic risk that human reviewers would miss in high-volume environments.

Credit and eligibility scoring, risk and priority classification, quality and performance scoring, and segmentation that applies consistent business rules at a scale that manual processes can't match.

Decision framing and context definition, data exploration and feature engineering, model design, training, validation, explainability checks, and bias testing. Every stage approached with the rigor that regulated and high-stakes environments require.

Getting a model into production is the beginning, not the end. Real-time performance monitoring, drift detection and alerting, automated retraining when conditions change, and version control that keeps the model's history traceable and auditable. Models that stay accurate and relevant over time rather than decaying quietly.