Case Study

From Manual Bottlenecks to Scalable Precision in Health Tech

Customer Context

Industry: Health Tech / Biotechnology Research

Company Size: Small, Early Stage

Core Stack: Node.js, Python, Spark, Kubernetes, AWS, RDS, React

Challenge

We started by analyzing their existing setups to identify inefficiencies. We found that not all cloud environments were right for their specific workloads. So, we decided to consolidate their operations into two main setups: Azure for their cloud needs and an on-premise solution for tasks that required more direct control.

Choosing the Right Tools:

We implemented ITIL processes, which are basically best practices for IT service management, to help them manage their infra better. This gave us a structured approach to make their operations smooth and predictable.

Monitoring Setup:

We also introduced them to Grafana, a tool for monitoring their systems in real-time. This way, they could immediately see if something was off and fix it before it became a bigger problem.

A small but ambitious biotech startup was at a critical point. They had strong domain expertise and early demand—but no MVP. Their data-heavy workflows, still running on local machines, couldn’t keep up with the scale of healthcare datasets. Processing billions of rows took hours (sometimes days), and manual checks introduced avoidable delays and errors. On top of that, compliance with ISO standards loomed as a non-negotiable requirement.

What they needed wasn’t just infrastructure—they needed a launchpad.

Customer Challenge

Their goals were clear:

Bring file processing times under 15 minutes

Control costs and avoid unnecessary overhead

Lay the foundation for scale and compliance

How We Helped

We approached the engagement as long-term collaborators—not just implementers. The initial 6-month project quickly transitioned into an ongoing retainer where we continue to manage and evolve the infrastructure.

Our Role

Designed and built cloud-native infrastructure on AWS
Set up automated CI/CD pipelines with GitLab CI
Created scalable, resilient data pipelines using Spark and Prefect
Integrated monitoring and observability via Prometheus and Grafana
Supported the team through ISO-aligned certification processes
Delivered a reliable MVP platform with autoscaling and full self-service

Tech at a Glance

AWS EKS, Terraform — to codify and scale infrastructure
GitLab CI — to support frequent, low-friction shipping
Prefect, Spark — to enable fast and parallel big data processing
Prometheus, Grafana — for real-time observability

Outcomes That Matter

After implementation, the platform achieved a measurable leap in reliability, performance, and security.

2 billion+ rows processed in under 15 minutes
99% fewer manual operations
Autoscaling, observable infrastructure ready for growth
Enabled daily shipping through CI/CD
Better UX through backend parallelization

“We went from idea to infrastructure that works for our users—and for us. The platform scales, the pipelines run, and we’re finally focused on building, not firefighting.”

— CTO, Health Tech Startup

What Made It Work

After implementation, the platform achieved a measurable leap in reliability, performance, and security.

We didn’t just drop tools into place—we guided the architecture from the ground up. From early decisions around infrastructure design to ensuring out-of-the-box compliance readiness, we focused on delivering something that worked from day one, but wouldn’t get in the way later.

Zero-to-One Guidance — Full-stack setup, from design to deployment
Compliance Built In — No last-minute scrambling for audits
Infrastructure as Code — Repeatable, scalable, auditable from the start
Self-Service DNA — Teams can move fast without needing ops bottlenecks

Final Word

After implementation, the platform achieved a measurable leap in reliability, performance, and security.

This project wasn’t just about speed or scale—it was about helping a customer focus on what they do best. By translating big data challenges into infrastructure that performs quietly in the background, we helped create space for real progress.

We’re proud to keep supporting that progress—one scalable step at a time.

Conclusion

This project shows how a well-designed cloud architecture can transform operational stability and speed.

The new environment can recover itself through Infrastructure as Code, scale automatically under load, and detect issues before they impact users. Security layers with Cloudflare WAF and DDoS protection keep the edge clean, while improved monitoring and alerting mean problems are fixed in minutes, not hours.

Performance is now predictable. Traffic spikes no longer cause downtime. Data flows seamlessly through Redis, Kafka, and ClickHouse — powering real-time operations with speed and stability. Today, the platform runs on a self-healing, compliant, and future-proof foundation that gives both developers and operations teams what they need most: confidence.

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