Results in Production
Case Studies
Three representative engagements — anonymised at client request — showing what engineering depth looks like when measured against real production metrics.
Case Study 01 — FinTech Platform
Monolith to microservices: payment processing at 40,000 TPS.
Context: A regional payment processing platform serving 4.2 million active users was operating on a monolithic Java backend. Deployment frequency had dropped to bi-weekly due to coordination overhead between teams. A peak traffic event had exposed scaling limits at 8,000 TPS — a threshold the business was reliably exceeding during promotional campaigns.
What we did: We performed a bounded context analysis across the existing codebase and identified five high-priority decomposition targets: payment orchestration, fraud scoring, notification dispatch, settlement reconciliation, and user identity. We extracted these over a 14-week incremental migration using the strangler-fig pattern, maintaining 100% availability of the existing system throughout. Infrastructure was migrated to Kubernetes on AWS, with Kafka introduced as the event backbone between services.
40,000 TPS
Peak Throughput
210ms
P99 Latency
99.97%
Uptime (12mo)
Multiple/day
Deploy Frequency
Case Study 02 — E-commerce Marketplace
Cloud-native infrastructure for a 3.8M SKU marketplace scaling through peak season.
Context: A fast-growing multi-vendor marketplace with 3.8 million active SKUs was running on manually provisioned EC2 infrastructure. Peak season traffic (10–12× baseline) caused repeated degradation events. Engineering team velocity was being constrained by the effort required to manage infrastructure manually.
What we did: We redesigned the infrastructure on EKS with horizontal pod autoscaling calibrated to real traffic patterns. Infrastructure as code was implemented across all environments using Terraform. CI/CD pipelines were rebuilt with automated testing gates and canary deployments. An observability stack was introduced with Prometheus, Grafana, and Jaeger — providing per-service latency and error rate visibility that had not existed before.
11.4× baseline
Peak Traffic Handled
18 min
Environment Provision
34%
Cloud Cost Reduction
< 3 min
MTTD Incidents
Case Study 03 — Enterprise SaaS + AI
Production-grade RAG system for a B2B SaaS platform with 280,000 enterprise users.
Context: A B2B SaaS company serving 280,000 enterprise users across 14 countries had built a proof-of-concept AI assistant using a direct OpenAI API integration. In production, it produced inconsistent results, exceeded latency budgets under concurrent load, and had no observability — making it impossible to identify what was failing or why.
What we did: We redesigned the AI layer as a structured system component. The RAG pipeline was rebuilt with a dedicated document ingestion service, chunking optimized for domain content, and a hybrid retrieval system combining dense and sparse search. A provider-agnostic LLM gateway was introduced to support multi-model routing based on query type and latency requirements. Response validation, prompt versioning, and per-query observability were added throughout.
89%
Relevance Score
1.4s
P95 Response Time
58%
Token Cost Reduction
1.2M
Daily Queries
Your system. The same engineering standard.
If you're dealing with scaling constraints, infrastructure fragility, or AI reliability at scale — let's talk about what the solution architecture looks like for your specific context.