Automated Incident Detection & Workflow Orchestration
Leveraged Amazon EventBridge, AWS Lambda, and AWS Step Functions to automate incident detection, telemetry processing, and RCA workflows.
- Geography:
- India
- Industry:
- Financial Services
- Solution:
- AWS
- Services:
- AWS GenAI Implementation
- Employee size:
- 300+
The client
Discover how enreap built an AI-powered observability & RCA platform on AWS for Advarisk to improve monitoring & incident resolution.
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 & solution
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.
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.
Value delivered
- 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
Building an AI-Powered Observability & RCA Platform on AWS for Advarisk
Turn reactive monitoring into proactive, AI-driven operations with an AWS observability specialist partner.
AWS
Building an AI-Powered Observability & RCA Platform on AWS for Advarisk