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Reduction in Operating Costs
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Faster Deployment Cycles
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Availability without Human Intervention
Company Name
Executive Summary
Problem Statement: IBBL manages a high volume of customer service requests, including balance checks, statements, and support queries, across both traditional and digital channels. This demand extends beyond office hours and includes large-scale social media interactions such as Facebook comments, Messenger chats, WhatsApp messages and so forth, creating constant pressure on support operations.
Challenges: IBBL’s customer support operations were constrained by several critical issues:
- High inquiry volume overwhelming support teams
- Limited after-hours availability, leaving customers unattended
- Slow response times, impacting customer satisfaction
- Inefficient social media management for handling comments and messages at scale
- Rising operational expenditure due to reliance on human agents and scaling support infrastructure
Solution: Graaho implemented Koronik AI, an intelligent conversational platform built on AWS and powered by advanced generative AI capabilities. Koronik AI was designed to automate and enhance customer interactions across digital touchpoints while maintaining a natural, human-like conversational experience. Key Capabilities include:
- 24/7 AI-Powered Support & automated Customer Service Tasks
- Human-Like Conversations & multi-Platform Integration
- Social Media Automation at Scale & low-code/no-code (LCNC) Deployment
- Real-time analytics dashboard & proactive issue detection and escalation
- Operational efficiency & cost optimiation
Results:
- Sub-500ms response times for customer interactions
- 99.9% platform uptime SLA achievement & 65% reduction in operational costs
- Support for 1,000+ concurrent AI agents
- 75% faster deployment cycles (automated CI/CD) & 70% improvement in release velocity
Business Challenges
Operational Challenges:
- Limited Scalability: Legacy systems couldn’t handle peak customer interaction volumes
- High Operational Costs: Traditional infrastructure required significant maintenance costs.
- Slow Deployment Cycles: Manual processes delayed feature releases and updates
- Response Time Issues: Customers experienced delays during high-traffic periods
Technical Requirements:
- Enterprise Security: Banking-grade security & data isolation for multi-tenant operations
- Compliance: Adherence to financial regulatory requirements and data sovereignty
- High Availability: Mission-critical uptime requirements for customer-facing services
- Performance: Real-time response capabilities for conversational interactions
- Scalability: Support for thousands of concurrent AI agents across multiple use cases
Strategic Goals:
- Reduce total cost of ownership while improving service quality
- Accelerate innovation through faster deployment cycles
- Enhance customer experience with intelligent
- Maintain competitive advantage through digital leadership
Solution Overview
Core AI & Application Services:
- Amazon Bedrock AgentCore: Powers intelligent conversational agents with foundation models
- Amazon DynamoDB: Provides millisecond-latency data access for session management and context storage
- AWS Lambda: Executes serverless compute for agent orchestration and business logic
- Amazon API Gateway: Manages secure, scalable API endpoints for agent interactions
DevOps & Automation:
- AWS CodePipeline: Orchestrates end-to-end CI/CD workflows
- AWS CodeBuild: Automates build and testing processes
- AWS CodeDeploy: Enables zero-downtime deployments
- Amazon CloudWatch: Provides comprehensive monitoring and observability
- AWS CloudFormation: Infrastructure as Code for consistent environment provisioning
Security & Compliance:
- AWS IAM: Fine-grained access control and identity management
- AWS KMS: Encryption key management for data at rest and in transit
- AWS WAF: Web application firewall for API protection
- Amazon VPC: Network isolation for multi-tenant security
- AWS CloudTrail: Audit logging for compliance requirements
Key Features Implemented
Multi-Tenant Architecture:
- Isolated data planes for each banking /use case
- Shared control plane for efficient resource utilization
- Tenant-specific customization and branding capabilities
Intelligent Agent Capabilities:
- Natural language understanding for customer queries
- Context-aware conversations with session persistence
- Integration with banking systems for real-time information
- Automated escalation to human agents when needed
Automated Operations:
- Fully automated CI/CD pipelines from code commit to production
- Automated testing including unit, integration, and performance tests
- Infrastructure provisioning and configuration management
- Automated scaling based on demand patterns
- Self-healing capabilities with automated recovery
Implementation Process
Phase 1: Discovery & Design (Weeks 1-2)
- Conducted AWS Well-Architected Framework review
- Defined security and compliance requirements
- Designed multi-tenant architecture
- Created proof of concept with Amazon Bedrock
Phase 2: Core Platform Development (Weeks 3-8)
- Implemented serverless backend infrastructure
- Integrated Amazon Bedrock AgentCore
- Developed DynamoDB data models for optimal performance
- Built API layer with authentication and authorization
- Established monitoring and logging framework
Phase 3: DevOps Automation (Weeks 9-10)
- Configured AWS CodePipeline for automated deployments
- Implemented infrastructure as code with CloudFormation
- Created automated testing suites
- Established deployment strategies (blue-green, canary)
- Configured CloudWatch dashboards and alarms
Phase 4: Testing & Optimization (Weeks 11-12)
- Conducted load testing for 1,000+ concurrent agents
- Optimized DynamoDB capacity and Lambda configurations
- Performed security penetration testing
- Fine-tuned AI agent responses and accuracy
- Validated compliance requirements
Phase 5: Prod Launch & Handover (Weeks 13-14)
- Executed phased production rollout
- Conducted knowledge transfer sessions
- Established support run books and procedures
- Implemented continuous monitoring
- Validated SLA achievement
- Self-healing capabilities with automated recovery
Results & Benefits
Performance Improvements
Response Time Excellence:
- Achieved sub-500ms response times for 95th percentile requests
- 3x faster than previous legacy system
- Consistent performance during peak traffic periods
Reliability & Availability:
- 99.9% uptime SLA consistently met
- Zero critical outages since production launch
- Automated failover and recovery mechanisms
Scalability Achievement:
- Successfully supporting 1,000+ concurrent AI agents
- Automatic scaling handles 10x traffic spikes
- No performance degradation during peak periods
Cost Optimization
65% Reduction in Operating Costs:
- Eliminated infrastructure maintenance overhead
- Pay-per-use pricing model with serverless architecture
- Reduced human resource requirements for operations
- Optimized AWS resource utilization through right-sizing
Cost Predictability:
- Transparent, usage-based billing
- Automated cost monitoring and optimization
- Reserved capacity for predictable workloads
Development Velocity
75% Faster Deployment Cycles:
- Reduced deployment time from hours to minutes
- Automated testing catches issues before production
- Zero-downtime deployments enable continuous delivery
70% Improvement in Release Velocity:
- Multiple deployments per day capability
- Faster time-to-market for new features
- Reduced risk through automated validation
Impact - Live in Production
Reduction in Costs
Uptime SLA consistently met
Faster Deployment Cycles
Improvement in Release Velocity
AWS Services Used
AI & Machine Learning:
- Amazon Bedrock (AgentCore)
Compute:
- AWS Lambda
Database & Storage
- Amazon DynamoDB
- Amazon S3
Networking & Content Delivery:
- Amazon API Gateway
- Amazon VPC
- Amazon CloudFront
Developer Tools:
- AWS CodePipeline
- AWS CodeBuild
- AWS CodeDeploy
- AWS CloudFormation
Security, Identity & Compliance
- AWS IAM
- AWS KMS
- AWS WAF
- AWS CloudTrail
- AWS Secrets Manager
Management & Governance
- Amazon CloudWatch
- AWS Systems Manager
- AWS Config
Business Impact
Enhanced Customer Experience:
- Instant, intelligent responses to customer queries
- 24/7 availability without human intervention
- Personalized interactions based on customer context
- Seamless escalation to human agents when needed
Operational Excellence:
- Minimal operational overhead with automated management
- Proactive issue detection and resolution
- Comprehensive audit trails for compliance
- Simplified capacity planning with predictable scaling
Digital Leadership:
- Positioned IBBL as an innovation leader in Bangladesh banking
- Competitive advantage through advanced AI capabilities
- Foundation for future AI-driven services
- Improved brand perception among digital-savvy customers
Technical Deep Dive
Amazon Bedrock Integration
- Foundation Model Selection: Evaluated & selected optimal models for banking use cases
- Prompt Engineering: Developed banking-specific prompts for accurate responses
- Knowledge Base Integration: Connected agents to IBBL’s knowledge repositories
- Action Groups: Implemented custom actions for banking transactions and queries
- Guardrails: Configured content filtering and safety mechanisms
DynamoDB Design Patterns
Optimized data architecture for performance and cost:
- Single-table design for efficient queries and reduced costs
- Global Secondary Indexes for flexible access patterns
- DynamoDB Streams for real-time event processing
- On-demand capacity for unpredictable workloads
- Point-in-time recovery for data protection
DevOps Automation Architecture
Comprehensive CI/CD pipeline implementation:
Code Commit → CodePipeline → CodeBuild (Test) → Security Scan → CodeDeploy (Staging) → Automated Testing → Approval Gate → CodeDeploy (Production) → Monitoring
Key Automation Features:
- Automated infrastructure provisioning
- Integrated security scanning (SAST/DAST)
- Automated rollback on failure detection
- Canary deployments for risk mitigation
- Automated performance testing
Security Implementation
Multi-layered security approach:
- Encryption: All data encrypted at rest (KMS) and in transit (TLS 1.3)
- Network Security: VPC isolation with private subnets
- API Security: OAuth 2.0 authentication, rate limiting, WAF protection
- Tenant Isolation: Logical separation with IAM policies
- Audit Logging: Comprehensive CloudTrail and application logs
- Compliance: Adherence to banking regulations and data protection laws
“Koronik.ai has transformed our customer engagement capabilities. We're now able to serve our customers faster, more efficiently, and at a fraction of the previous cost. The automated DevOps pipelines have revolutionized how quickly we can innovate and deploy new features."
Sr. Technology Executive, IBBL
Best Practices Implemented
AWS Well-Architected Framework
Operational Excellence:
- Infrastructure as Code for all resources
- Automated deployment pipelines
- Comprehensive monitoring and alerting
- Runbook automation for common scenarios
Security:
- Defense in depth with multiple security layers
- Principle of least privilege for all access
- Automated security scanning in CI/CD
- Regular security assessments and updates
Reliability:
- Multi-AZ deployment for high availability
- Automated backup and recovery procedures
- Chaos engineering for resilience testing
- Circuit breakers and retry logic
Performance Efficiency:
- Serverless architecture for optimal resource utilization
- DynamoDB optimization for low-latency access
- CloudFront caching for static content
- Lambda function optimization and right-sizing
Cost Optimization:
- Pay-per-use serverless model
- Reserved capacity for predictable workloads
- Automated resource cleanup
- Regular cost optimization reviews
Sustainability:
- Serverless reduces carbon footprint
- Efficient resource utilization
- Minimal idle resources
Lessons Learned
Technical Insights:
- Amazon Bedrock Optimization: Careful prompt engineering and model selection significantly impact response quality and cost
- DynamoDB Design: Single-table design with well-planned access patterns delivers optimal performance
- Lambda Cold Starts: Provisioned concurrency for critical paths ensures consistent performance
- Multi-Tenancy: Proper isolation design from the start prevents costly refactoring
Process Improvements:
- Automated Testing: Comprehensive test automation is essential for rapid deployment cycles
- Monitoring First: Implementing observability early accelerates troubleshooting
- Incremental Rollout: Phased deployments reduce risk and build confidence
- Documentation: Automated documentation generation keeps pace with rapid changes
Partnership Success Factors:
- Clear Communication: Regular stakeholder updates ensured alignment
- Agile Methodology: Iterative development allowed for course corrections
- Knowledge Transfer: Comprehensive training enabled customer self-sufficiency
- AWS Support: Leveraging AWS technical resources accelerated implementation
Conclusion
The partnership between Graaho Technologies LLC and Islami Bank Bangladesh PLC demonstrates the transformative power of AWS’s AI and serverless technologies in the financial services sector. By leveraging Amazon Bedrock, DynamoDB, and comprehensive DevOps automation, Graaho delivered a production-grade conversational AI platform that exceeds performance expectations while dramatically reducing costs.
The solution’s success — characterized by sub-500ms response times, 99.9% uptime, 65% cost reduction, and 75% faster deployments — showcases how modern cloud-native architectures can deliver both technical excellence and business value. The fully automated DevOps pipelines enable continuous innovation, allowing IBBL to maintain its competitive edge in Bangladesh’s rapidly evolving digital banking landscape.
This case study exemplifies AWS’s commitment to empowering partners and customers to build intelligent, scalable, and cost-effective solutions that drive digital transformation and enhance customer experiences.
Future Roadmap
Short-term (3-6 months)
- Expand to additional banking use cases (loan processing, account opening)
- Implement voice-based interactions using Amazon Transcribe and Polly
- Add multilingual support for regional languages
- Enhance analytics with Amazon QuickSight dashboards
Medium-term (6-12 months)
- Integrate with additional banking systems for broader automation
- Implement predictive analytics for proactive customer service
- Develop mobile SDK for embedded agent experiences
- Expand to other financial institutions in the region
Long-term (12+ months)
- Explore Amazon Bedrock’s advanced features as they become available
- Implement federated learning for privacy-preserving model improvements
- Develop industry-specific agent templates for rapid deployment
- Create marketplace offering for other financial institutions
