Our work in Data & AI Foundations

Tightening PFAS regulation and supplier exits threatened a MedTech maker's supply chain. Trexin ran a structured outreach across 3,000+ suppliers (3,085 contacted, 1,971 reaching complete status) to inventory 'forever chemicals' for compliance and continuity.

A global medical-device maker's supplier data was siloed and inconsistent, breaking reporting. Trexin built an 'MDM-lite' solution on the tools they already owned (faster ingestion, automated auditing, change-history tracking) with no new license fees.

An NCQA mandate requires 80% directly-sourced REL data; the payer was collecting under 5%, with no governance to secure it. Trexin restructured their data-governance framework and built a 12-tactic roadmap to close health-equity gaps.

Trexin served as data delivery lead building a large health payer's external data management system: governed, traceable data exchange that reduces the risk of sensitive-data disclosure, delivered on time and on budget.

Trexin stood up a production analytics environment in Microsoft Azure for a mid-sized manufacturer: self-service BI in about 90 days, aggregating 50+ source tables on a 5-minute refresh.

Most organizations accept that their data is valuable. Where they get stuck is turning it into something they can act on, and whether to treat data as a service they stand up in the cloud. A practitioner's view on when DaaS pays off, and what to weigh first.

A MedTech group's analytics ran on siloed data and Excel, producing late, two-number reports. Trexin re-architected it into a reusable 'analytics factory': MVP in 5 months, with data-prep effort down 60%.

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 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 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.