Real infrastructure.
Measurable results.
Every engagement starts with a problem and ends with production-grade infrastructure your team can own and operate.
ShipSarthi
From 45-minute deploys to 3-minute pipelines
Challenge
ShipSarthi was scaling fast across India but their deployment pipeline hadn't kept up. Manual deployments took 45 minutes, rollbacks were a nightmare, and downtime during releases was eating into SLA commitments. The team was spending more time firefighting infrastructure than building product.
Solution
Rebuilt their entire deployment pipeline on GCP Cloud Run with Terraform-managed infrastructure. Implemented blue-green deployments with automated health checks, centralized logging with structured alerts, and a cost optimization strategy that consolidated underutilized resources across regions.
ZammerNow
100% Infrastructure as Code for rapid fashion e-commerce
Challenge
ZammerNow needed to launch a high-performance quick-commerce fashion platform with sub-2-second page loads. Their existing infrastructure was manually provisioned, impossible to replicate across environments, and couldn't handle the traffic spikes typical of flash sales and promotional events.
Solution
Designed a Kubernetes-first architecture on AWS with Helm-managed microservices, GitHub Actions CI/CD with preview environments for every PR, and autoscaling policies tuned for flash-sale traffic patterns. Achieved 100% IaC coverage from day one — every resource tracked, every change auditable.
AI Pipeline
Sub-200ms inference at scale with 60% GPU savings
Challenge
An enterprise client needed a production RAG pipeline handling 10,000+ daily queries with strict latency requirements. Their proof-of-concept worked in notebooks but fell apart at scale — inference latency was unpredictable, GPU costs were spiraling, and there was no observability into pipeline performance.
Solution
Built a Kubernetes-native inference pipeline with NVIDIA GPU scheduling, intelligent batching, and tiered caching. Implemented model versioning with canary rollouts, real-time latency monitoring, and auto-scaling policies that right-size GPU allocation based on actual query patterns rather than peak provisioning.
Let's build something
that scales.
Your infrastructure challenges are unique. Let's talk about what the right solution looks like for your team.
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