Case study | AWS
Building an AI-Powered Observability & RCA Platform on AWS for Advarisk
- Geography: India
- Industry: Financial Services
- Employees: 300+
- Solution: AWS
- Services: AWS GenAI Implementation
The client
Advarisk is a leading FinTech organization providing digital financial services, payment processing, and transaction management solutions to a large customer base. The company operates business-critical applications that require high availability, real-time monitoring, and rapid incident resolution to ensure seamless customer experiences and regulatory compliance.
Client requirements
Accelerated Incident Resolution
Reduce manual efforts by streamlining incident investigation across logs, metrics, alarms, and traces.
Intelligent Root Cause Analysis
Enable automated RCA to quickly identify and resolve application and infrastructure issues.
No Centralized Incident Knowledge
The absence of a centralized knowledge repository caused similar incidents to be investigated repeatedly, with operational knowledge siloed within individual teams.
Proactive IT Operations
Improve visibility into application dependencies and shift from reactive incident management to predictive operations, reducing MTTR.
Our approach
enreap designed and implemented an AI Observability & Application Root Cause Analysis (RCA) Platform on AWS for Advarisk, built to transform incident management from a manual, reactive process into an intelligent, automated one. The platform continuously collects and correlates metrics, logs, alarms, and traces from application and infrastructure components using Amazon CloudWatch and AWS X-Ray. Event-driven workflows orchestrated through Amazon EventBridge, AWS Lambda, and AWS Step Functions automatically analyze incidents, while Amazon Bedrock generates contextual root cause insights, supported by a centralized knowledge repository in Amazon DynamoDB and predictive analytics powered by Amazon SageMaker.
Our solution
Automated Incident Detection & Telemetry Processing
Amazon CloudWatch and AWS X-Ray collect infrastructure and application telemetry, while Amazon EventBridge triggers automated workflows the moment an anomaly or alarm is detected.
AI-Powered Root Cause Analysis
Dedicated Lambda-based RCA engines, powered by Amazon Bedrock Nova foundation models, generate contextual root cause assessments, impact analysis, and remediation recommendations for both infrastructure and application incidents.
Historical Incident Knowledge & RAG-Based Recommendations
Amazon DynamoDB retains incident patterns and resolutions, while Amazon Bedrock Knowledge Base uses Retrieval-Augmented Generation (RAG) to reuse historical knowledge, improving accuracy and reducing AI inference costs.
Predictive Analytics & Long-Term Archival
Amazon SageMaker analyzes historical operational data to flag potential issues before they cause disruption, while DynamoDB Streams and Amazon S3 archive incident records for compliance, analytics, and continuous learning.
Real-Time Notifications & Dashboards
Amazon SNS delivers RCA summaries and remediation recommendations to operations teams in real time, with Amazon CloudWatch dashboards providing a single pane of glass for monitoring system health and incident trends.
Business benefits
- Reduced Mean Time to Resolution (MTTR) by approximately 60–75%
- Achieved up to 80% reduction in manual troubleshooting effort
- Improved incident response time from 30–60 minutes to under 10 minutes for common issues
- Enabled 40–50% faster resolution of recurring incidents through historical knowledge reuse
- Reduced dependency on subject matter experts by approximately 50%
- Reduced repetitive operational activities by 70% through automation
- Lowered Generative AI processing costs by approximately 25–35% through knowledge reuse
- Established a centralized repository of incident records and RCA findings for continuous learning
Technology stack
