Case study • Healthcare • Cloud & DevOps Modernization

40% Faster Deployments for Genetic Testing Platform with Cloud & DevOps Transformation

We partnered with a globally accredited genetic testing laboratory to modernize their infrastructure and DevOps capabilities. By implementing a scalable AWS-based architecture, streamlining CI/CD pipelines, and enabling secure data management, we improved deployment velocity, scalability, and cost efficiency—while ensuring precision and reliability in diagnostic workflows.

Success Highlights

  • 40% faster deployment cycles with CI/CD automation
  • 30% scalability improvement using containerized workloads
  • 25% reduction in infrastructure costs with cloud optimization

Key Details

  • Industry: Healthcare / Genetic Diagnostics
  • Geography: United States
  • Platform: Amazon Web Services (AWS), Amazon EKS, Amazon RDS, AWS CloudFront

Business Challenge

The client faced critical gaps in DevOps maturity, cloud adoption, and migration readiness—impacting scalability, efficiency, and time-to-market.

  • Limited DevOps Expertise: Lack of skilled personnel slowed infrastructure automation and deployment processes.
  • Migration & Documentation Gaps: Incomplete system documentation created risks around cloud migration, data integrity, and system reliability.
  • Low AWS Tooling Maturity: Limited experience with AWS DevOps services led to inefficiencies in CI/CD implementation.
  • Complex Migration Planning: Required a phased, low-risk migration approach to avoid downtime in critical diagnostic systems.

Our Solution Approach

We implemented a cloud-native DevOps transformation strategy focused on scalability, automation, and secure data management.

1 · Discover

Assess DevOps Gaps & Migration Risks

We analyzed existing infrastructure, workflows, and documentation gaps to define a structured migration roadmap.

2 · Consolidate

Design Scalable Cloud Architecture

We built a containerized architecture using Amazon EKS and centralized data systems with RDS and Secrets Manager.

3 · Automate

Implement CI/CD & Infrastructure Workflows

We enabled automated build, test, and deployment pipelines using AWS-native DevOps services for faster releases.

4 · Accelerate

Optimize Delivery & Content Distribution

We improved application performance and global accessibility using CDN-based content delivery and optimized cloud resources.

Technical Highlights

  • Containerized workloads on Amazon EKS for scalable and resilient application deployment
  • Managed relational databases using Amazon RDS with secure credential storage via AWS Secrets Manager
  • CI/CD automation using AWS CodeCommit, CodeBuild, and CodePipeline for continuous delivery
  • Content delivery optimization using AWS CloudFront CDN for low-latency global access
  • Infrastructure provisioning and configuration standardization for repeatable deployments
// Python
// Psuedocode: CI/CD Deployment Trigger Logic
def deploy_application(commit):
if run_tests(commit) == "pass":
build_container(commit)
deploy_to_eks(cluster)
else:
notify_team("Build Failed")

Business Outcomes

Enabled a scalable, secure, and automation-driven cloud platform that improved deployment speed, cost efficiency, and operational reliability.

40%
Faster Deployment Cycles:

Automated CI/CD pipelines reduced release time and improved delivery consistency.

30%
Increase in Platform Scalability:

Containerized workloads allowed dynamic scaling to handle fluctuating diagnostic workloads.

25%
Reduction in Infrastructure Costs:

Optimized cloud usage and pay-as-you-go pricing improved cost efficiency.

  • Improved system reliability and reduced downtime during migration
  • Enhanced data security with managed secrets and controlled accessFaster time-to-market for new diagnostic featuresStandardized deployment processes reducing operational risk
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