Real infrastructure.
Measurable results.

Every engagement starts with a problem and ends with production-grade infrastructure your team can own and operate.

PAN-India Logistics — 500+ Clients

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.

45min → 3min
Deploy time
35%
Cost reduction
99.95%
Uptime achieved
GCP
Cloud Run
Terraform
Quick-Commerce Fashion

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.

<2s
Page load time
100%
IaC coverage
12x
Daily deploys
Kubernetes
AWS
Helm
GitHub Actions
Enterprise RAG — 10K+ Daily Queries

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.

<200ms
P95 latency
10K+
Daily queries
60%
GPU cost savings
Kubernetes
NVIDIA
GCP

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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