A leading healthcare revenue cycle management (RCM) SaaS platform provider faced a fixed deadline to migrate its on-premise, monolithic claims platform. Rapid growth and multiple acquisitions had compounded legacy debt, making the .NET 4.5 application difficult to scale and maintain. The company needed a scalable cloud-native architecture through AI legacy modernization that maintained strict HIPAA compliance while accelerating developer productivity to improve time to market.
Download the full case study to see how the team delivered 9 weeks of engineering value in just 3 days.
The solution
Working with Grid Dynamics, the company executed a phased AI legacy modernization on Microsoft Azure using the Strangler Fig pattern. This approach allowed legacy components and new microservices to coexist, ensuring uninterrupted claims processing.
The modernization initiative included:
- Upgrading applications from .NET Framework 4.5 to .NET 8 for better performance and cross-platform compatibility.
- Replacing legacy Windows services with Linux containers deployed on Azure Kubernetes Service (AKS).
- Migrating more than 20 databases to Azure SQL PaaS and transitioning legacy ETL pipelines to Azure Databricks.
- Introducing an AI-enabled software development lifecycle (SDLC) centered around Cursor.
To drive adoption, engineering teams completed targeted training on applying AI for code generation, refactoring, and automated test creation while maintaining enterprise security standards.
The results
Integrating AI directly into the codebase drastically accelerated the legacy modernization timeline and improved code quality.
- Delivered nine weeks of engineering value in just three days of AI-assisted development.
- Rewrote 23,000 lines of legacy code rapidly and accurately.
- Increased unit test coverage from 0% to 58%, reducing operational risk.
- Met the fixed data center exit deadline with zero downtime.
- Reduced total cost of ownership by consolidating infrastructure inherited from acquisitions.
Download the full case study to view the complete Azure cloud architecture and learn how to implement an AI-driven SDLC across your engineering teams.
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