Proof, Perspective, and Where Occams Is Focused

Challenge

Legacy on-prem systems, manual model scoring, fragmented MLOps workflows, and rising pressure to prepare for Gen-AI made the operating model hard to scale.

Solution

Occams helped structure a cloud migration, automated model operations (MLOps), enabled secure data exchange, and defined a cloud-native architecture that could support future AI use cases.

Outcome

The program created a scalable data foundation, more stable ML pipelines, stronger cross-team alignment, and a clearer roadmap for AI-supported innovation.

Challenge

A decades-old claims platform had to be modernized while supporting high transaction volume, regulated data handling, and uninterrupted service across the business.

Solution

Occams supported a phased cloud modernization approach, tokenized sensitive claimant data, and established streaming, warehousing, and governance foundations for future analytics and AI.

Outcome

The platform moved off its legacy core on schedule, claims handling improved materially, and the organization gained a more governable foundation for ongoing transformation.

Challenge

Finance and workforce forecasting relied on spreadsheets and external advisors, limiting speed, consistency, and executive visibility into changing conditions.

Solution

Occams led discovery with AI architects and domain experts, unified finance, CRM, HR, and staffing signals, and designed AI-enabled workflows for both cash and talent forecasting.

Outcome

Forecasting cycles improved by 40–50%, reliance on outside advisors fell, leaders gained better visibility into staffing and cash positions, and the organization created a stronger base for ongoing AI adoption.