Notes from the last mile.

A large health payer's tangle of warehouses couldn't support modern analytics. Trexin's 12-week assessment (56 leaders, 4 health plans) designed a future-state platform (data lake, sandboxes, analytic workbenches, and an AI/ML library) with an incremental build roadmap.

A malpractice insurer estimated $4M a year from catching high-risk claims at first notice of loss, but review was manual and expert-bound. Trexin's machine-learning model learned the rules from history; within a year the Client was on track to take down $4.3M.

A non-profit wanted to prove the economics of a procedure it advocated. Trexin's Actionable Analytics Jumpstart answered it in under 30 days, and showed the study should measure survival, not just cost, since untreated patients died too quickly for cost offsets to appear.

A state Medicaid plan set a $4MM cost-of-care savings target. Trexin's machine-learning model learned the experts' member-reassignment rules at over 99% accuracy, cutting cycle time an estimated 8–12 weeks and driving more than $1MM of the goal.

A climate-tech startup's flood-risk model took over two hours per property and needed manual tuning. Trexin built an automated, scalable MVP on AWS in under 60 days, cutting analysis from two-plus hours to under five seconds per parcel.

A safety-net hospital wanted to know whether severe-infection patients were being triaged to the right level of care, without a multi-year prospective study. Trexin's data-science analysis of billing and discharge data found mis-triage in 1 in 8 patients, tied to worse outcomes.

Risk-adjustment models exist to remove uncertainty, but current models capture only 10–15% of the variation. The 80%+ they miss is exactly where better care and value hide.