Enterprise Case Studies
Deep dives into how we design, architect, and optimize mission-critical infrastructure for some of the most demanding workloads in the world.
Scalable Multi-Cloud Web Application Architecture & CI/CD Platform
Designed and implemented a production-grade multi-cloud architecture for a high-traffic cinema web application using Vercel, Cloudflare, Oracle Cloud Infrastructure (OCI), Jenkins, and object storage.
Target Architecture: Global Media Streaming (OCI & Cloudflare)
The Challenge
The client needed a secure, highly reliable infrastructure capable of handling large-scale traffic spikes during blockbuster movie releases while ensuring isolated environments (QA/Prod) and secure VPN-based administration.
Architecture Before
Traditional single-cloud hosting with manual deployments, exposed origin servers, and limited CDN acceleration.
Architecture After
Multi-cloud architecture leveraging Vercel for Next.js frontend, Cloudflare for DNS/WAF/CDN, and OCI for secure backend compute. Automated CI/CD via Jenkins and GitHub.
Measurable Results
- Improved deployment reliability via automated dual-pipeline CI/CD (Vercel & Jenkins).
- Enhanced application performance and origin protection through Cloudflare Edge caching.
- Secured administrative access via an isolated VPN Network Bubble into the OCI VCN.
Lessons Learned
"A hybrid multi-cloud approach—using Vercel for edge frontend delivery and OCI for heavy backend compute—provides the optimal balance of performance and cost for high-traffic applications."
Technical Stack
Timeline
4 Months
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Book Architecture ReviewMulti-Application Media Platform Infrastructure on AWS
Designed and managed scalable AWS infrastructure for a large media network (Outlook India) hosting multiple digital platforms (outlookindia.com, outlookbusiness.com, etc).
Target Architecture: Multi-Application Media Platform (AWS)
The Challenge
The client needed a highly available architecture capable of supporting multiple separate applications, severe traffic spikes during breaking news, and continuous content delivery while maintaining operational visibility.
Architecture Before
Isolated, non-scalable legacy servers struggling to handle traffic surges and lacking centralized observability.
Architecture After
Consolidated AWS infrastructure with Cloudflare CDN, AWS Application Load Balancers, dedicated EC2 Application Servers, and a highly available Amazon RDS MySQL cluster.
Measurable Results
- Enabled seamless handling of millions of monthly users with high availability and fault tolerance.
- Significantly improved application performance and global page speed via Cloudflare CDN.
- Established real-time monitoring and alerting through Amazon CloudWatch and SNS.
Lessons Learned
"Separating multiple high-traffic media properties behind a unified AWS ALB and Cloudflare CDN simplifies SSL termination, WAF protection, and traffic distribution while allowing individual backend scaling."
Technical Stack
Timeline
5 Months
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Book Architecture ReviewProduction Infrastructure Architecture on Oracle Cloud Infrastructure
Designed and managed a production-grade cloud infrastructure on Oracle Cloud Infrastructure (OCI) for a high-availability application with strict security and data sovereignty requirements.
Target Architecture: Secure Production Infrastructure (OCI Dubai Region)
The Challenge
The client required a highly secure, scalable cloud deployment that isolated the application and database tiers from the public internet while ensuring seamless developer access and automated disaster recovery.
Architecture Before
Unsecured public-facing compute instances with manual backup processes and no centralized traffic inspection.
Architecture After
A hardened OCI Virtual Cloud Network (VCN) featuring public load balancing, isolated private application subnets, OpenVPN for secure access, and automated block volume/object storage backups.
Measurable Results
- Eliminated direct internet exposure to application servers and databases via OCI Private Subnets and NAT Gateways.
- Secured administrative access by implementing an OpenVPN tunnel restricted to developer IPs.
- Automated disaster recovery via cross-region replication of OCI Object Storage and Block Volume backups.
Lessons Learned
"Implementing a strict Public/Private subnet topology on OCI, combined with Cloudflare at the edge, provides enterprise-grade security without sacrificing application performance."
Technical Stack
Timeline
3 Months
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Book Architecture ReviewUltra-Low Latency Secure Infrastructure
Engineered a zero-trust, ultra-low latency trading environment on AWS, adhering to strict financial compliance frameworks (PCI-DSS, SOC2).
Target Architecture: High-Frequency Trading Platform (AWS)
The Challenge
The client needed to execute trades faster than competitors while maintaining absolute security and passing rigorous external compliance audits.
Architecture Before
Fragmented AWS environment with public subnets and unencrypted data stores.
Architecture After
Private EKS clusters communicating over VPC Endpoints, with AWS Direct Connect to trading exchanges and Aurora Serverless for encrypted persistence.
Measurable Results
- Achieved sub-millisecond network latency between trading pods and exchanges.
- Passed SOC2 Type II compliance audit with zero infrastructure exceptions.
- Automated 100% of infrastructure provisioning via Terraform.
Lessons Learned
"Implementing network security at the subnet and pod level (NetworkPolicies) is critical for compliance, but requires careful benchmarking to avoid latency overhead."
Technical Stack
Timeline
6 Months
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Book Architecture ReviewScalable LLM Inference & GPU Orchestration
Built a dynamically scaling inference pipeline for fine-tuned Large Language Models, optimizing expensive GPU usage and reducing queue times.
The Challenge
Data scientists were manually provisioning EC2 GPU instances, leading to idle waste (costing $40k/month) and unacceptable queue times for API users.
Architecture Before
Manual EC2 instances running Flask servers with local model weights.
Architecture After
Containerized vLLM engine deployed on EKS with KEDA auto-scaling based on SQS queue length, loading weights dynamically from S3.
Measurable Results
- Reduced monthly GPU spend by 60% through aggressive scale-to-zero capabilities.
- Increased inference throughput by 3x using vLLM continuous batching.
- Enabled data scientists to deploy new models in 5 minutes (down from 2 days).
Lessons Learned
"Separating model weight storage (S3) from compute allows for rapid scaling of stateless inference nodes."
Technical Stack
Timeline
2 Months
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