Scaling a US Non-Emergency Medical Transportation Platform Through AWS Migration
CUSTOMER
The customer was a US-based non-emergency medical transportation (NEMT) company operating a technology platform for managing patients, drivers, trips, scheduling, dispatch, and transportation operations.
As the transportation network expanded, the platform needed to support a growing number of drivers, trips, concurrent users, and real-time operational activities.
CHALLENGE
The customer’s existing infrastructure was designed for a smaller operational footprint. As more drivers and transportation requests were added, application and database workloads increased significantly.
Several technical challenges emerged:
* Driver onboarding increased concurrent application traffic and API requests.
* Dispatch activity generated frequent reads and writes against trip and driver data.
* Driver location and availability updates created additional API and database load.
* Peak booking and dispatch periods produced sudden traffic spikes.
* Database queries became more demanding as trip, driver, patient, and operational records grew.
* Application deployments required careful coordination because multiple services depended on the same infrastructure.
* Infrastructure scaling was largely manual, making it difficult to respond quickly to changes in demand.
* Monitoring and infrastructure-level visibility needed improvement to identify performance bottlenecks before they affected operations.
The platform also handled sensitive healthcare-related information, making security, access control, data protection, auditability, and business continuity important requirements.
Cognic’s Solution
Cognic assessed the existing application, infrastructure, database dependencies, traffic patterns, and operational workflows before defining a phased AWS migration strategy.
The target architecture separated application workloads, database services, storage, networking, monitoring, and security controls to provide greater scalability and operational flexibility.
The AWS architecture incorporated services such as Amazon EC2, Elastic Load Balancing, Amazon RDS, Amazon S3, Amazon CloudWatch, IAM, and VPC.
Application workloads were placed behind load balancing so additional compute capacity could be introduced as driver activity, trip volume, and concurrent requests increased.
The database layer was reviewed and optimized around high-volume operational queries, including driver availability, trip assignment, scheduling, and transportation records. Database capacity and query performance were considered as part of the migration rather than treating the cloud move as a simple server relocation.
Cognic followed a phased migration approach:
1. Infrastructure and application assessment
2. Dependency and workload mapping
3. AWS target architecture design
4. Network and security configuration
5. Database migration planning
6. Application workload migration
7. Performance and load validation
8. Production cutover
9. Post-migration monitoring and optimization
The migration approach emphasized incremental validation and controlled production cutover to reduce operational risk. This follows the broader cloud migration practice of assessing dependencies, migrating workloads in phases, validating performance, and optimizing infrastructure after migration.
Business Benefits
The AWS migration provided the customer with a more scalable infrastructure for its growing NEMT operation.
Key benefits included:
* Improved scalability as the number of drivers and trips increased
* Better handling of concurrent API requests and dispatch activity
* Improved application availability through load-balanced infrastructure
* More predictable database performance under increasing transaction volumes
* Better infrastructure and application monitoring through centralized metrics and logs
* Improved security through IAM, VPC controls, and controlled access to application and database resources
* More structured deployment and release processes
* Reduced dependency on manually managed infrastructure
* A stronger foundation for future automation, analytics, and AI capabilities
The new architecture also provided a foundation for continued growth without requiring major infrastructure changes every time the transportation network expanded.
Technology Used
The solution used AWS services including Amazon EC2, Elastic Load Balancing, Amazon RDS, Amazon S3, Amazon CloudWatch, IAM, VPC, and related AWS infrastructure services.
The application stack included web applications, REST APIs, relational database services, driver and trip management workflows, secure document and data storage, and CI/CD-based deployment processes.
The migration covered cloud architecture, infrastructure modernization, database migration, application deployment, security controls, monitoring, performance optimization, and production cutover.