Aigenomix Moves to Google Cloud
Success Stories
Industry: Medical Practice
Primary project location: United States
About the Client
Aigenomix is a biotechnology research company focused on building cutting-edge bioinformatics solutions. Its flagship platform, the Palmona Pathogenomics Platform (P3), serves public health labs, academic medical centers, and life sciences researchers by enabling large-scale genomic analysis, management, and data sharing for pathogen research.
Partner role in the project:
As the strategic Google Cloud implementation partner, Evonence led the complete migration and modernization journey from AWS to Google Cloud. Our role included infrastructure automation, containerization, data migration, confidential computing enablement, CI/CD redesign, and knowledge transfer — ensuring a secure, scalable, and compliant platform foundation.
The Challenge:
Aigenomix’s bioinformatics platform (P3) was deeply integrated with AWS-native services, making the migration to Google Cloud highly complex.
Key challenges included:
Tightly coupled AWS dependencies across multiple applications (pg-web4e, P3, svc-manager, workflow services).
Service replacement for RabbitMQ, transitioning from managed AWS to self-managed deployment in Google Kubernetes Engine (GKE).
Rebuilding CI/CD pipelines from Bitbucket to align with GCP’s security and identity management.
Integrating confidential computing models with Google Confidential Space for secure genome prediction.
Implementing Workload Identity Federation (WIF) for secure, keyless authentication across environments.
The solution:
Evonence designed and implemented a fully automated, cloud-native architecture using Terraform for reproducibility across Dev, UAT, and Prod.
Key components included:
Infrastructure modernization – Deployed VPCs, GKE clusters (Standard & Autopilot), Cloud SQL (Postgres), Cloud Memorystore (Redis), and Artifact Registry.
Application refactoring – Rebuilt core services to run natively on GCP, ensuring clean API integration and scalability.
Confidential Computing enablement – Deployed genome prediction models securely in Google Confidential Space and Confidential VMs, with workload attestation for data integrity.
CI/CD transformation – Re-engineered Bitbucket pipelines using Workload Identity Federation (WIF) for secure, keyless deployments.
Data & database migration – Moved all workloads, datasets, and schemas from AWS RDS and S3 to Cloud SQL and Google Cloud Storage with zero data loss.
The Result:
Seamless Migration – Complete shift from AWS to GCP with no downtime or service disruption.
Confidential Predictions Live – Genome models now run securely in Confidential Space with verifiable trust.
Cloud-Native Efficiency – All microservices run on GKE with autoscaling, integrated caching (Redis), and high-availability databases.
Smarter Deployments – CI/CD automation with Terraform, Helm, and WIF ensures consistent, secure rollouts.
Operational Excellence – Faster, reliable platform with end-to-end validation, documentation, and scalability for future research expansion.
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