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Case Studies

Work That Speaks

AI-powered delivery. Architect-verified quality. Here's a look at what our teams have built, fixed, scaled, and secured for our clients.

Early-stage FinTech startup, pre-Series A

FinTech Startup — From Idea to Launch in 5 Weeks

The Challenge

The founders had a working prototype but needed a production-ready, compliance-friendly platform before their funding deadline. No in-house engineering team. Tight regulatory requirements (KYC/AML). Five weeks to launch.

What We Did

A lead architect designed the system architecture and assembled an AI engineering team — frontend, backend, and DevOps agents working under continuous architect review. Built the platform on a modern stack with event-driven architecture. Implemented KYC/AML integrations. Set up CI/CD, infrastructure as code, monitoring, and alerting from day one. Ran weekly demos with stakeholders.

Results

  • Launched on time, two days ahead of the funding deadline
  • Onboarded 10,000+ users in the first quarter
  • Passed SOC 2 Type I audit on the first attempt
  • Zero critical security findings in third-party pen test

Stack

Node.js React PostgreSQL AWS (ECS, RDS, CloudFront) Terraform GitHub Actions
Growing e-commerce company, $30M annual revenue

E-Commerce Platform — Infrastructure Cost Reduction

The Challenge

Cloud bill had tripled in 18 months. Resources were over-provisioned, auto-scaling was misconfigured, and nobody knew which services cost what. Leadership needed clear answers and fast savings.

What We Did

Our AI agents ran a comprehensive cloud cost audit across 3 AWS accounts, supervised by a lead architect. Tagged every resource by team and product. Identified $180K/year in waste (orphaned resources, oversized instances, unoptimized storage). The architect designed the optimization strategy; AI agents implemented right-sizing, reserved capacity planning, spot instance strategy, and cost alerting. Built a cost dashboard for leadership.

Results

  • 60% reduction in monthly cloud spend ($15K/mo savings)
  • Full resource tagging and cost attribution across all teams
  • Automated cost anomaly alerts preventing future waste
  • ROI: Engagement paid for itself in the first month

Stack

AWS Cost Explorer CloudWatch Terraform Python Grafana
Regional manufacturing company, 500+ employees, 20-year-old systems

Manufacturing Company — Legacy Modernization

The Challenge

Core business systems were running on a legacy monolith (classic ASP, on-prem SQL Server). The original developer had retired. The system was brittle, poorly documented, and blocking the company's ability to integrate with modern supply chain partners.

What We Did

Our architect spent a week reverse-engineering and documenting the existing system with AI-assisted code analysis — the kind of judgment work that requires human expertise directing AI agents. Then AI agents, under architect direction, executed a strangler fig migration strategy — replacing components incrementally without disrupting operations. Built a modern API layer in front of the legacy database. Migrated the three most critical modules to a new microservices architecture.

Results

  • Three critical modules modernized in 4 weeks
  • New API layer enabled two new supply chain partner integrations
  • Reduced manual data entry by 12 hours per week
  • System documented for the first time in 15 years — team can now maintain it independently

Stack

.NET 8 Azure (App Service, SQL Database, Service Bus) React Swagger/OpenAPI

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