Proven Outcomes.
We don't just "lift and shift". We architect, automate, and optimize. Explore our deep-dive engineering partnerships below to see exactly how we solved complex infrastructure challenges.
Quentia
The Problem
Quentia required a foundational cloud architecture from the ground up that could support rapid scaling, secure data handling, and automated deployments without requiring a large in-house operations team.
Engineering Challenges
- Designing a multi-environment AWS architecture from scratch.
- Implementing strict security boundaries and IAM policies.
- Automating the CI/CD pipeline to allow developers to ship code multiple times a day safely.
The Architecture & Implementation
We implemented an AWS infrastructure using Terraform to ensure all environments were version-controlled. The compute layer utilizes Docker containers managed by ECS, allowing for rapid auto-scaling. The deployment pipeline was fully automated via GitHub Actions, integrating testing and staging validations before production releases.
Engineering Outcomes
- ✓Zero-downtime automated deployments.
- ✓100% Infrastructure as Code (IaC) coverage.
- ✓Secure, isolated VPC architecture.
Business Value
Enabled the product team to focus entirely on feature development rather than infrastructure management, drastically reducing time-to-market.
Lessons Learned
"Investing heavily in Terraform from Day 1 prevents immense technical debt when scaling from staging to production environments."
eSarathi
The Problem
The platform needed a robust, highly-available foundation with deep observability to identify and resolve performance bottlenecks before they impacted end-users.
Engineering Challenges
- Managing complex microservice interactions.
- Setting up centralized logging and metrics collection.
- Executing zero-downtime database upgrades during peak hours.
The Architecture & Implementation
We deployed a production-grade Kubernetes cluster (EKS) configured with Prometheus and Grafana for centralized metrics. We implemented automated deployment workflows and set up structured logging pipelines to capture and index application logs in real-time, providing deep insights into cluster health.
Engineering Outcomes
- ✓Sub-second alert triggering for cluster anomalies.
- ✓Eliminated manual deployment errors via GitOps.
- ✓Successful zero-downtime database migrations.
Business Value
Transformed reactive operations into proactive observability, reducing Mean Time to Resolution (MTTR) by 75%.
Lessons Learned
"Deep observability isn't just about logs; it's about correlating metrics with deployments to instantly identify regressions."
Vello (Shopify App)
The Problem
Taking a complex Shopify application from local development to a production environment capable of handling massive, unpredictable traffic spikes during merchant flash sales.
Engineering Challenges
- Handling Shopify Webhook spikes without dropping payloads.
- Architecting for multi-tenant data isolation.
- Setting up a reliable, automated release process.
The Architecture & Implementation
We decoupled the webhook ingestion using a queue-based architecture to handle traffic bursts. The cloud infrastructure was designed specifically for the Shopify ecosystem's latency requirements. We implemented strict CI/CD pipelines to ensure code changes were automatically tested against Shopify API mocks before deployment.
Engineering Outcomes
- ✓Seamless production launch with 100% uptime.
- ✓Webhook processing capacity increased by 10x.
- ✓Automated release rollbacks in case of failure.
Business Value
Provided the technical foundation required to scale the app to thousands of merchants without operational bottlenecks.
Lessons Learned
"Queue-based architectures are mandatory when dealing with external webhook platforms to prevent systemic failures."
FILSER AI Search & Filter (Shopify)
The Problem
The application required highly optimized infrastructure to serve complex search queries at sub-second latencies across global merchant storefronts.
Engineering Challenges
- Tuning Elasticsearch for high-throughput reads.
- Building resilient deployment pipelines to avoid search downtime.
- Establishing operational support protocols.
The Architecture & Implementation
We re-architected the search cluster to utilize optimized AWS instances and configured intelligent load balancing. Deployment pipelines were enhanced to ensure that new code releases did not interrupt active search sessions. We also established comprehensive operational playbooks for the engineering team.
Engineering Outcomes
- ✓Maintained <50ms search latencies during peak load.
- ✓Migrated production workloads seamlessly without merchant impact.
- ✓Established robust on-call and operational support protocols.
Business Value
Protected merchant revenue by ensuring the search functionality—a critical conversion driver—remained blazing fast and highly available.
Lessons Learned
"Search infrastructure requires constant tuning; separating the ingest pipeline from the query pipeline is critical for stability."