Real-World Results, Delivered

Explore how we partner with businesses to transform infrastructure, accelerate delivery, and achieve measurable growth.

E-commerce Scaling

Scaling for Peak Traffic with a Cloud-Native Migration

Challenge: A legacy monolithic application struggled with Black Friday traffic surges, leading to downtime and lost revenue.

Solution: We architected and executed a full migration to a microservices architecture on AWS, leveraging Docker, Kubernetes (EKS), and a robust CI/CD pipeline built with Jenkins and Terraform.

99.99%
Uptime
-60%
Deployment Time
-30%
Infra Costs
AWS Kubernetes Docker Terraform Jenkins
FinTech Security

Achieving DevSecOps and Compliance in a Regulated Industry

Challenge: Slow, manual release processes and difficulty meeting strict financial compliance requirements like PCI DSS.

Solution: We engineered a comprehensive DevSecOps pipeline on Azure, integrating SonarQube, Trivy, and HashiCorp Vault to embed automated security and compliance checks directly into the workflow.

4x
Faster Delivery
95%
Automated Checks
-80%
Audit Prep Time
Azure DevSecOps SonarQube Trivy HashiCorp Vault
MLOps Platform

Building a Scalable MLOps Platform from the Ground Up

Challenge: Data scientists were spending more time on infrastructure than building models. The process for training and deploying models was manual and not reproducible.

Solution: We designed and deployed a unified MLOps platform on Google Cloud using Kubeflow on GKE, integrating MLflow for experiment tracking and Seldon Core for robust model serving.

10x
Faster Deployment
-90%
Infra Management
100%
Reproducible
GCP Kubeflow MLflow Seldon Core GKE
Automated Model Retraining Pipeline

Automating Model Retraining for a Retail Giant

Challenge: A leading e-commerce platform's recommendation models were becoming stale, leading to decreased accuracy. The manual, quarterly retraining process was slow and resource-intensive.

Solution: We implemented an end-to-end MLOps pipeline on AWS that automatically triggers model retraining based on performance drift detected by Prometheus. New models are containerized and deployed to an EKS cluster using a safe, automated canary release strategy.

+15%
Model Accuracy
-80%
Manual Effort
48x
Faster Retraining
MLOps AWS EKS Prometheus CI/CD Docker
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