Our Work: Engineered for Impact.

Real-time Analytics Platform for FinTech

Client: Leading FinTech Innovator

Challenge: The client needed to ingest and process massive streams of financial transaction data in real-time to detect fraud and provide instant risk assessments, replacing a batch-processing system with significant latency.

Solution: Designed and deployed a fully serverless, event-driven architecture on AWS. Utilized Kinesis for data ingestion, Lambda for stream processing, DynamoDB for fast lookups, and Redshift for complex analytical queries. Implemented automated CI/CD with Terraform.

Key Technologies:

• AWS Kinesis, Lambda, DynamoDB, Redshift, S3
• Python, Node.js for Lambda functions
• Terraform for IaC

Results: Achieved sub-second data processing latency, reducing fraud detection time by 90%. Scaled to handle over 5 million events per minute with 99.999% uptime and a 30% reduction in operational costs.

Global E-commerce API Optimization

Client: Multi-national Retail Giant

Challenge: An existing monolithic e-commerce API experienced performance bottlenecks during peak sales events, leading to slow response times and occasional outages, impacting user experience and revenue.

Solution: Executed a strategic migration from on-premise virtual machines to AWS ECS Fargate, re-architecting the API into a set of highly optimized microservices. Implemented robust caching with ElastiCache and optimized database interactions with Aurora Serverless.

Key Technologies:

• AWS ECS, Fargate, API Gateway, ElastiCache
• Aurora Serverless (PostgreSQL)
• Docker, CI/CD with GitHub Actions

Results: Achieved an average 70% reduction in API latency and increased request throughput by 5x. The new architecture provided unparalleled scalability, handling extreme traffic spikes without performance degradation.

Secure IoT Data Ingestion & Processing

Client: Industrial IoT Startup

Challenge: The client needed a secure, highly scalable platform to ingest vast amounts of data from thousands of IoT devices globally, process it, and make it available for analytics and device management.

Solution: Developed an AWS IoT Core-centric solution, utilizing MQTT for device communication, Kinesis for data streaming, and Lambda for processing and routing data to S3 and DynamoDB. Implemented strong authentication and authorization policies.

Key Technologies:

• AWS IoT Core, Kinesis, Lambda, S3, DynamoDB
• Python for data processing
• IAM for fine-grained access control

Results: Successfully onboarded over 50,000 devices with zero data loss. The platform now securely processes terabytes of data daily, enabling real-time monitoring and predictive maintenance for industrial assets.

Automated Disaster Recovery for SaaS

Client: Enterprise SaaS Provider

Challenge: The client required a robust and fully automated disaster recovery (DR) strategy for their critical SaaS application, aiming for a Recovery Point Objective (RPO) of minutes and a Recovery Time Objective (RTO) of less than an hour.

Solution: Implemented a multi-region active-passive DR solution using AWS services. Automated database replication with RDS cross-region snapshots, cross-region S3 replication, and a fully scripted failover/failback mechanism using Lambda and CloudFormation.

Key Technologies:

• AWS RDS, S3, Lambda, CloudFormation, Route 53
• Python for automation scripts
• Robust monitoring and alerting

Results: Achieved RPO of 5 minutes and RTO of 30 minutes in live DR drills. The automated system significantly reduced the risk of downtime and improved business continuity without manual intervention.