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 effort and speed up incident investigation across logs, metrics, alarms, and traces.
Automated Root Cause Analysis
Enable AI-driven RCA to quickly identify the underlying causes of application and infrastructure issues.
Centralized Incident Knowledge
Establish a unified knowledge repository to capture incident history, resolutions, and best practices for faster troubleshooting.
Proactive Operational Visibility
Gain end-to-end visibility into application dependencies and service interactions to detect and resolve issues before business impact.
Scalable IT Operations
Build an operational framework capable of supporting increasing transaction volumes and infrastructure complexity without compromising reliability.
Improved Platform Reliability
Reduce Mean Time to Resolution (MTTR) while ensuring consistent application performance and business continuity.
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 & Workflow Orchestration
Leveraged Amazon EventBridge, AWS Lambda, and AWS Step Functions to automate incident detection, telemetry processing, and RCA workflows.
AI-Powered Root Cause Analysis
Integrated Amazon Bedrock foundation models to generate contextual root cause analysis, impact assessments, and remediation recommendations for infrastructure and application incidents.
Historical Incident Knowledge & RAG-Based Recommendations
Built a knowledge repository using Amazon DynamoDB and Amazon Bedrock Knowledge Base (RAG) to reuse historical incident data and improve RCA accuracy.
Predictive Analytics & Long-Term Archival
Utilized Amazon SageMaker to identify recurring patterns, enable predictive operations, and optimize AI inference by reusing historical incident intelligence.
Secure Monitoring & Continuous Operations
Enabled real-time notifications, centralized dashboards, long-term archival, and secure AWS-native architecture to improve operational efficiency, governance, and platform reliability.
Unified Observability Platform
Centralized infrastructure and application telemetry using Amazon CloudWatch and AWS X-Ray to provide real-time operational visibility.
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
Technology stack
