Case Studies

A frozen .NET monolith was blocking every feature, every release, every schema change. NonStop decomposed it into microservices, migrated live with zero downtime, and added observability, canary deployments, and Elasticsearch. The platform went from a liability to a compounding asset.

A genetic testing lab was processing hundreds of requisition forms daily, handwritten, faxed, scanned, across 20+ providers, entirely by hand. Errors surfaced days later as rejected claims and patient mismatches. Scaling volume meant scaling headcount, and accuracy still got worse

Every report layout, every field, every structural change was hardcoded. Clinical staff wrote Jira tickets instead of owning workflows, every new client onboarding and customization was a fresh engineering project, and template changes required engineering efforts.
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A genetic testing lab doubled from 50 to 100 people in months. With increased test volumes, prior auth submissions became inconsistent, denial rates climbed, and reps spent more time copying data between systems than resolving cases.
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Labs lose PA revenue not from coverage gaps but from inconsistent submissions. NonStop built an embedded AI agent that reads the clinical order, pulls the right payor policy, maps the correct codes, and assembles a complete submission package before it goes out, with a counselor reviewing every draft before submit

Labs hand-assemble medical necessity forms for every order, wrong templates, missing fields, days lost to signature chasing. NonStop's agent auto-selects the right payor template, populates it from existing lab data, validates every field, and routes for digital signature automatically.
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Manual triage was causing inconsistencies, delays, and downstream clinical risk. NonStop built a self-hosted AI agent that reads every inbound request, classifies intent and urgency, and drafts a Jira ticket routed to the right team, human reviews before anything is created, PHI never leaves the environment.

Rare disease test orders were arriving with incomplete phenotype data because referring clinicians had no structured tool to capture it at the point of care. The lab was absorbing the cost: payor denials, resubmission cycles, and technicians spending hours on intake translation.

A scaling genomics lab had no visibility into what its bioinformatics pipelines were consuming or costing. Without pipeline monitoring tool, failures went undetected for hours, and cloud spend arrived as a single monthly aggregate with no breakdown by pipeline or sample.