Pangea Bio: Computational Metabolomics Platform Enablement

Client and Product Context

Pangea Bio operates at the intersection of computational biology and data platform engineering. Their platform includes a computational metabolomics module for processing and analysing biological data, alongside a Knowledge Graph component for structuring relationships across datasets. The technical stack runs on AWS with GitHub-based source control and CI/CD pipelines. As the client prepared for accelerated development, they needed a delivery partner who could quickly onboard, understand the existing architecture, and begin contributing without disrupting ongoing work.

The Challenge

Technical engagements on complex platforms often lose momentum in the first weeks due to unclear access, scattered documentation, and misaligned expectations. Pangea Bio faced several onboarding challenges:

• Secure handoff of AWS account access and GitHub repository permissions required careful coordination
• Technical documentation for the metabolomics module existed but needed consolidation and a canonical source
• Pipeline architecture and component dependencies needed mapping before meaningful contribution could begin
• Working cadence, communication channels, and escalation paths required formalisation
• Security posture expectations—credential hygiene, least privilege, auditability—needed alignment from day one

What We Did

We approached this as an enablement engagement: establish access, understand the platform, and create the conditions for productive delivery. Our kickoff followed a structured, documentation-first methodology:

• Formalised the engagement with NDA handling, points of contact, and agreed working cadence
• Received and reviewed initial technical documentation for the computational metabolomics module
• Aligned on GitHub as the canonical source for documentation, ensuring updates remain version-controlled and auditable
• Onboarded into the client's AWS account and GitHub repositories with admin access for initial setup
• Documented security expectations: credential hygiene, access scope, and plans for least-privilege refinement
• Created a first-pass discovery plan covering pipelines, platform components, integration points, and immediate next steps

Solution Architecture

The platform architecture spans several interconnected components that we mapped during the discovery phase:

• Computational Metabolomics Module: Core processing pipelines for biological data analysis and transformation
• Knowledge Graph Layer: Structured data relationships enabling cross-dataset queries and insights
• AWS Account: Cloud infrastructure hosting compute workloads, data stores, and pipeline orchestration
• GitHub Repositories: Source control for application code, infrastructure definitions, and technical documentation
• CI/CD Pipelines: Automated build and deployment workflows for platform components
• Data Stores: Persistent storage for raw inputs, processed outputs, and graph data

Technical Highlights

Several engineering decisions defined the quality of this early engagement:

• Secure onboarding: AWS and GitHub access established with clear ownership and audit trail—credentials exchanged securely outside the public case study
• Documentation-first approach: GitHub designated as single source of truth for technical docs, ensuring version control and accessibility
• Discovery methodology: Systematic review of computational pipelines, dependency mapping, and integration point identification
• Security posture: Early alignment on key management expectations, least-privilege planning, and environment separation principles
• Working agreements: Formalised communication cadence, escalation paths, and delivery checkpoints

Security and Reliability Considerations

Enterprise-grade delivery requires operational discipline from day one:

• Access control: Role-based permissions with documented ownership for AWS and GitHub resources
• Credential hygiene: Secure exchange protocols with plans for secrets management tooling
• Audit logging: CloudTrail and GitHub audit logs enabled for access and change tracking
• Environment separation: Clear boundaries between development, staging, and production environments
• Terraform: Infrastructure as Code for managing AWS resources with version-controlled, auditable provisioning
• Repeatable deployments: CI/CD pipelines as the standard path to production, reducing manual intervention
• Operational runbooks: Documentation of common procedures and incident response expectations

What's Next

With the foundation established, the roadmap for the next 2–6 weeks focuses on depth and hardening:

• Pipeline deep-dive: Detailed mapping of computational workflows, data dependencies, and processing stages
• Architecture documentation: Comprehensive platform map covering metabolomics module and knowledge graph integration
• Security hardening: Least-privilege access refinement, secrets management implementation, and monitoring setup
• CI/CD improvements: Pipeline optimisation for faster, more reliable deployments
• Module delivery planning: Milestone definition for upcoming platform features and enhancements

Technologies

Why It Matters

Complex technical platforms require more than code delivery—they need clarity, structure, and operational discipline. By investing in a proper kickoff, Pangea Bio gained a delivery partner who understood their architecture, respected their security requirements, and could contribute productively from week one. Scalexa's experience—including work for brands like Walmart, Coca-Cola, ESPN, UNICEF, and FIFA—ensures enterprise-grade practices applied to research-driven platforms and growing biotech companies alike.

Services Behind This Project

The same senior teams that delivered this work:

• DevOps & CloudCloud architecture, CI/CD and infrastructure
• AI & Machine LearningProduction ML, LLM and computer-vision systems