Outcomes, not logos.

A global beauty business could forecast brick-and-mortar demand reasonably well, but D2C e-commerce demand was unstable. A planning model improved forecast accuracy, reduced bias, and helped teams reduce inventory imbalance, marketing waste, and late discounting.

A quality-control model was too slow for the line. Reframing vibration data as a signal-processing problem enabled edge inference without slowing production.

A multi-billion-dollar food exporter was losing margin because dealers could claim back poor-quality fruit. Batch-level shelf-life prediction helped the business decide which fruit to send first, where to send it, and how to reduce avoidable claims.

A telecom operation was over-provisioning field labor because faults were treated as random events. Decay modeling shifted maintenance from reactive dispatch to risk-based intervention.

A large professional-services organization was matching people to projects through manual HR coordination. A recommendation workflow helped HR and project managers find suitable resources faster, reduce allocation delays, and improve project fit.

A large US telecom provider could predict churn risk, but prediction alone did not create retention. The breakthrough came from connecting network-service issues to targeted intervention, using an agentic workflow to triage unhappy customers at scale.

A major automotive distributor was advised to classify which leads would buy. The better answer was to model the buying journey step by step, giving sales teams better prompts, finance context, and next-best-action guidance at each stage.