Real Platforms, Real Results

From startups to banks—here's how we've built secure, scalable delivery platforms for teams that ship.

Showing 6 of 6 case studies

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High-Velocity Open Source Organization
SaaS

Open-Source Company CI Overhaul

The Challenge

Running approximately 200 Drone CI jobs per hour for Go microservices across a Hetzner VM fleet. Infrastructure was provisioned manually, CI pipelines lacked security scanning, and container images were unsigned. Scaling was becoming painful, and there was no visibility into supply chain security.

Key Results

  • 40% faster CI pipeline execution through optimization
  • 100% of container images now signed and verified
  • Zero manual infrastructure provisioning (full IaC adoption)
Drone CI Terraform Ansible Hetzner Cloud Go Docker +4 more
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Tier 2 European Banking Institution
Financial Services

European Bank Migration to Azure

The Challenge

Legacy on-premises infrastructure running critical banking services. Jenkins pipelines were fragile and undocumented. Migration to Azure Cloud required meeting strict regulatory compliance (PSD2, GDPR, local data residency). Team lacked cloud-native expertise and needed a secure, compliant landing zone.

Key Results

  • Successfully migrated 15 critical banking services to Azure with zero downtime
  • Achieved PSD2 and GDPR compliance certification
  • Reduced Jenkins maintenance overhead by 70%
Azure Terraform Azure DevOps Azure Key Vault Azure Policy Qualys +2 more
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Major U.S. Banking Institution
Financial Services

U.S. Bank Core Banking Exchange Pipeline

The Challenge

Building a new core banking transaction exchange interface (NDA-protected details). No existing CI/CD pipeline for this greenfield project. Extremely high compliance requirements (PCI DSS, SOC2, FFIEC). Needed end-to-end pipeline with full audit trails, secrets management, and deployment automation for a highly sensitive transactional system.

Key Results

  • Delivered production-ready pipeline meeting all PCI DSS and FFIEC requirements
  • Zero security findings during external audit
  • Deployment time reduced from days (manual) to minutes (automated)
GitHub Actions Terraform HashiCorp Vault Docker Kubernetes Sigstore +2 more
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Dark Fiber Network Monitoring SaaS Startup
SaaS

Fiber Monitoring Startup Kubernetes Platform

The Challenge

Early-stage startup building a monitoring platform for dark fiber networks. Development environment pipelines needed for Go backend services and TypeScript frontend. Required Kubernetes cluster on Vultr for cost efficiency. Team lacked DevOps expertise and needed a production-ready platform quickly to focus on product development.

Key Results

  • Production-ready Kubernetes platform delivered in 6 weeks
  • Dev team able to deploy 10+ times per day with confidence
  • 60% cost savings vs. AWS EKS
Kubernetes Vultr Terraform GitHub Actions Go TypeScript +5 more
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AI/ML SaaS Startup
AI/ML

AI Startup GPU Kubernetes Platform

The Challenge

AI startup needed production Kubernetes infrastructure on Vultr with managed GPU nodes for machine learning workloads. Existing PHP application needed containerization and modern CI/CD. No security scanning or image signing in place. Required fast iteration for AI model training and deployment.

Key Results

  • Production Kubernetes platform with GPU support live in 5 weeks
  • PHP application modernized and containerized
  • CI/CD pipelines reduced deployment time from hours to minutes
Kubernetes Vultr GPU Nodes Terraform GitHub Actions PHP Docker +4 more
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Fiber ISP / Telecom Provider
Telecommunications

Fiber Network AI Ops: Automated Failure Analysis

The Challenge

Fiber network SLA breach investigations taking hours or days to complete. Manual OTDR analysis, root cause identification, and report generation bottlenecked incident response. Field crews dispatched without detailed failure analysis, increasing mean time to repair (MTTR). No automated way to correlate alarms with precise failure locations and actionable repair instructions.

Key Results

  • Incident reports generated in minutes vs. hours (95% time reduction)
  • Automated root cause analysis with 95% confidence scoring
  • Precise failure localization (20.03 km ± 15 meters) enabling faster field response
Claude AI OTDR Systems Python Machine Learning ADVA FSP3000 Network Monitoring +1 more
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