Our Work
Innovate. Elevate. Lead.
Discover inspiring success stories where our expertise has driven transformative solutions, empowering startups, and global enterprises.
Our Latest Works
- All Projects
- Cloud Migration
- DevOps Automation
- Kubernetes
- AI Infrastructure
- Education
- Fintech
- Media & Entertainment
Enterprise Cloud Migration for Financial Services
Migrated a legacy on-premise banking system to AWS with zero downtime, implementing auto-scaling and multi-region failover.
End-to-End DevOps Transformation
Implemented complete DevOps automation for a SaaS platform, reducing deployment times from 4 hours to 7 minutes.
Large-Scale Kubernetes Implementation
Designed and deployed a production-grade Kubernetes cluster handling 50M+ daily transactions with auto-scaling.
ML Training Platform for Computer Vision
Built a scalable ML training platform reducing model iteration time from weeks to hours with distributed training.
University ERP System
Comprehensive ERP solution for a university network handling 50,000+ students with modules for admissions, academics, and finance.
Asset-Backed Lending Platform
Warehouse management system with integrated financial tracking for collateralized lending operations, processing $2B+ in assets annually.
Enterprise Content Hub
Custom CMS for a streaming platform handling 500K+ daily content deliveries with AI-powered metadata tagging.
Enterprise Cloud Migration for Financial Services
Zero-downtime migration of a legacy banking system to AWS with full CI/CD automation and infrastructure-as-code
Project Overview
We partnered with a leading financial services provider to migrate their mission-critical banking platform from an on-premise data center to AWS. The system processed over 2 million transactions daily with strict compliance requirements including PCI DSS and SOC 2.
The legacy system was built on monolithic architecture with Oracle databases running on physical servers. Maintenance windows were required weekly, impacting customer experience during peak business hours.
Challenges
Legacy Architecture
Monolithic application with tight coupling between components made scaling individual services impossible.
Downtime Constraints
Zero downtime requirement with no tolerance for transaction loss during migration.
Compliance Requirements
Financial regulations required maintaining strict audit trails and data encryption standards.
Our Solution
We implemented a phased migration approach using the Strangler Fig pattern to gradually replace functionality while maintaining system availability:
Infrastructure as Code
Terraform modules for provisioning AWS resources with compliance guardrails built-in.
Containerization
Dockerized application components with Kubernetes for orchestration and auto-scaling.
Data Migration
Implemented AWS Database Migration Service with change data capture for near real-time sync.
The new architecture featured microservices with API gateways, distributed caching, and multi-region deployment for disaster recovery. We established CI/CD pipelines with automated testing and canary deployments to reduce risk.
Results
The client achieved full cloud adoption within 9 months, eliminating their data center footprint. The new system automatically scales to handle 300% increases in transaction volume during peak periods without manual intervention.
End-to-End DevOps Transformation
Complete automation of software delivery pipeline reducing deployment times from 4 hours to 7 minutes
Project Overview
We worked with a SaaS provider in the healthcare space to transform their manual, error-prone deployment process into a fully automated CI/CD pipeline. The platform served over 500 hospitals with strict uptime requirements.
Prior to our engagement, deployments required coordination between 5 teams and often resulted in production incidents requiring rollbacks. The average deployment took 4 hours with 30% resulting in some form of service degradation.
Challenges
Manual Processes
Deployments required 23 manual steps documented in Word documents leading to human errors.
Lack of Visibility
No centralized monitoring made troubleshooting production issues time-consuming.
Environment Drift
Development, staging, and production environments had significant configuration differences.
Our Solution
We implemented a comprehensive DevOps transformation with the following key components:
Pipeline Automation
GitLab CI/CD pipelines with automated testing, security scanning, and deployment orchestration.
Infrastructure as Code
Terraform and Ansible for consistent environment provisioning across all stages.
Observability
Prometheus and Grafana dashboards with SLO-based alerting for production systems.
We established a GitOps workflow where all changes to infrastructure and application configuration were managed through Git merge requests. Feature flags allowed for safer production deployments with the ability to quickly disable problematic features.
Results
The transformation enabled the client to move from monthly "big bang" releases to multiple daily deployments with confidence. Mean time to recovery (MTTR) for incidents improved from 4 hours to 15 minutes through better observability and automated rollback capabilities.
Large-Scale Kubernetes Implementation
Production-grade Kubernetes cluster handling 50M+ daily transactions with auto-scaling
Project Overview
We designed and implemented a Kubernetes-based platform for a high-traffic e-commerce company experiencing growing pains with their VM-based infrastructure. The platform needed to handle Black Friday traffic spikes of 10x normal volume while maintaining sub-second response times.
The existing infrastructure couldn't scale quickly enough to meet demand, resulting in lost revenue during peak periods. Manual scaling processes took hours to complete and often lagged behind actual traffic patterns.
Challenges
Traffic Spikes
Unpredictable traffic patterns with sudden 10x increases during promotions.
Resource Waste
Over-provisioned VMs sitting idle 80% of the time to handle peak loads.
Complex Deployments
Multi-component services with complex dependencies between microservices.
Our Solution
We implemented a Kubernetes platform on AWS EKS with the following key features:
Horizontal Pod Autoscaler
Automatically scales pods based on CPU/memory usage and custom metrics.
Cluster Autoscaler
Dynamically adds/removes worker nodes based on pod scheduling needs.
Service Mesh
Istio for advanced traffic management, canary deployments, and observability.
We implemented GitOps workflows using ArgoCD for declarative configuration management. All infrastructure was provisioned using Terraform with policy guardrails for security and compliance.
Results
The platform successfully handled Black Friday traffic with zero downtime and maintained sub-200ms response times throughout peak periods. The client reduced their cloud infrastructure costs by 60% while improving reliability and performance.
ML Training Platform for Computer Vision
Scalable training platform reducing model iteration time from weeks to hours
Project Overview
We built a machine learning training platform for an autonomous vehicle company working on advanced computer vision models. Their existing setup required 3-4 weeks to train a single model iteration, slowing down research progress.
The platform needed to support distributed training across hundreds of GPUs while efficiently managing expensive compute resources. Researchers needed self-service access to launch experiments without deep infrastructure knowledge.
Challenges
Long Training Times
3-4 weeks per model iteration slowed research progress.
GPU Utilization
Expensive GPU resources were idle 60% of the time.
Experiment Tracking
No centralized system for tracking experiments and results.
Our Solution
We implemented a Kubernetes-based platform with the following key components:
Distributed Training
Horovod and TensorFlow distributed training across multiple GPU nodes.
GPU Sharing
Time-slicing and MIG for efficient GPU resource utilization.
Experiment Management
Kubeflow Pipelines with MLflow for tracking experiments.
The platform automatically scaled GPU resources based on demand and preempted low-priority jobs when high-priority work arrived. Researchers could launch experiments through a web interface or API with predefined resource profiles.
Results
The platform reduced average training times from 3-4 weeks to 8-24 hours, enabling faster iteration cycles. GPU utilization increased from 40% to 80% through better scheduling and sharing. Researchers could run 10x more experiments with the same budget.
University ERP System
Comprehensive ERP solution for a university network handling 50,000+ students
Project Overview
We developed a cloud-based ERP system for a university network with 15 campuses across the country. The system replaced 17 legacy systems that had grown organically over 20 years, creating data silos and operational inefficiencies.
The new platform needed to integrate all aspects of university operations including student information, course management, financial aid, HR, and finance while providing real-time analytics across all functions.
Challenges
System Fragmentation
17 separate systems with duplicate and conflicting data.
Manual Processes
Paper-based workflows for admissions, registration, and grading.
Reporting Limitations
No unified reporting across academic and administrative functions.
Our Solution
We implemented a modern ERP system with the following key components:
Unified Platform
Single system for all university operations with role-based access.
Student Lifecycle
End-to-end digital workflows from application to graduation.
Real-time Analytics
Dashboards for enrollment trends, financial aid, and academic performance.
The microservices architecture allowed different university departments to customize their modules while maintaining data integrity. We implemented event sourcing to maintain a complete audit trail of all system changes for compliance.
Results
The ERP system reduced administrative workload by 70% through automation and eliminated data reconciliation tasks. Student satisfaction improved with self-service portals for registration, grades, and financial aid. The university saw a 300% increase in online applications in the first year.
Asset-Backed Lending Platform
Warehouse management system with integrated financial tracking for $2B+ in assets
Project Overview
We developed a comprehensive platform for a fintech company specializing in collateralized lending against physical assets. The system needed to track inventory across multiple warehouses while maintaining real-time financial records for auditing and compliance.
The legacy system relied on manual inventory checks and paper-based reconciliation, creating delays in loan processing and increasing fraud risk. Audits required weeks of manual work to verify asset values.
Challenges
Manual Verification
Physical inventory checks delayed loan approvals by weeks.
Fraud Risk
No real-time visibility into inventory movements.
Audit Complexity
Reconciling financial records with physical assets was time-consuming.
Our Solution
We implemented an integrated platform with these key features:
IoT Tracking
RFID and sensor networks for real-time inventory visibility.
Blockchain Ledger
Immutable record of all inventory movements and valuations.
Automated Valuation
Integration with commodity markets for real-time asset pricing.
The system automatically alerted for suspicious inventory movements and generated audit trails for compliance reporting. Loan officers could approve financing within hours instead of weeks with confidence in collateral values.
Results
The platform reduced loan approval times from 3 weeks to 2 days while decreasing fraud incidents by 60%. Automated audits saved thousands of hours annually in manual reconciliation work. The company expanded their lending portfolio by 400% with the same operational staff.
Enterprise Content Hub
Custom CMS for streaming platform handling 500K+ daily content deliveries
Project Overview
We developed a content management and delivery platform for a major streaming service with millions of subscribers. The system needed to handle ingestion, processing, and distribution of video content to multiple platforms and devices.
The client was using 8 different tools for various parts of their workflow, creating bottlenecks in content publishing. Metadata management was inconsistent, making content discovery difficult for users.
Challenges
Disjointed Tools
Multiple systems created workflow bottlenecks.
Manual Metadata
Human tagging resulted in inconsistent content discovery.
Format Complexity
Content needed adaptation for 20+ device types.
Our Solution
We built a unified content hub with these key capabilities:
Automated Processing
AI-powered transcoding and format adaptation.
Smart Metadata
Computer vision and NLP for automated tagging.
Multi-channel Delivery
Single publish point for all platforms and devices.
The platform used microservices architecture to independently scale different processing workloads. Content creators could upload once and have assets automatically prepared for all delivery targets with appropriate metadata.
Results
The platform reduced time-to-market for new content from days to hours and increased publishing capacity by 4x. Viewer engagement improved by 30% through better content discovery powered by AI-generated metadata. Infrastructure costs were cut in half through more efficient processing.
