Impact at a Glance
Faster feature time-to-market (monthly releases vs quarterly)
Reduction in operational workload
Lower maintenance costs (due to modular architecture and automation)
Fully scalable to handle 5x future account growth without major re-architecture
About the client
The client is a leading provider of card management platforms, currently supporting over 10 million active accounts, with plans to scale to 50 million accounts. Their existing platform processes near real-time transactions across hybrid infrastructure (private on-premise + public cloud).
Business Challenge
Monolithic Architecture limited scalability and agility
- Feature releases were slow (quarterly cycles).
- Infrastructure maintenance costs and operational overhead were high.
- Resilience issues: Single points of failure impacted service availability.
- Competitive pressure to deliver real-time, personalized services.
Cognida.ai Solution
Architecture Modernization
- Transitioned from monolithic to domain-driven, event-driven microservices architecture.
- Introduced Command Query Responsibility Segregation (CQRS) for improved scalability and performance.
- Implemented asynchronous event messaging using Kafka for inter-service communication.
- Refactored 3 critical business modules (Card Issuance, Customer Rewards, Application Processing) into independent microservices.
Platform Engineering
- Designed a cloud-native platform with full portability across AWS, Azure, and private data centers.
- Deployed services in Kubernetes clusters with:
- Auto-scaling
- Load balancing
- Service mesh integration (Istio)
- Implemented centralized observability
- Built secure CI/CD pipelines
- 100% automated infrastructure provisioning for both cloud and on-prem environments.
- Zero-downtime blue-green deployments and canary releases enabled.
Technical Proof
- Microservices Latency: <100ms for internal service-to-service communication
- Resilience: Services isolated with automatic failover; maintained 99.99% uptime during live migration
- Deployment Frequency: Increased from quarterly to monthly (3x improvement)
- Infrastructure Provisioning Time: Reduced by 85% (from ~5 days manual setup to under 8 hours automated)
- Monitoring Coverage: 100% services monitored with real-time alerts and log aggregation
- Database Strategy: Adopted a polyglot persistence model — PostgreSQL for transactional services, MongoDB for customer metadata
