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Composable Enterprise

CASE STUDY

Medical Firm Enabled 24x7 Observability and 20X Faster Time to Repair with AI-Led SRE

Industry

Healthcare & Life Sciences

Our Contributions

SRE Transformation, AIOps Enablement, Observability Modernization

Location

Global

Technologies

Datadog, PagerDuty, AI-Driven Analytics

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Coforge partnered with a global medical firm to enhance its operational maturity by adopting AI-led Site Reliability Engineering (SRE) practices. The client faced high alert noise, inefficient incident management, and limited observability, impacting system reliability and operational efficiency.

By implementing an AI-driven SRE framework, Coforge transformed operations from reactive support to proactive, intelligent reliability engineering. The solution improved incident response, reduced alert fatigue, and enabled predictive, data-driven operations, ensuring high availability and performance across critical systems.


Transformation Timeline

Phase 1
SRE maturity assessment and observability gap analysis
Phase 2
SRE core and run team setup with 24x7 monitoring
Phase 3
AIOps enablement and runbook standardization
Phase 4
Advanced analytics, automation, and optimization

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The Challenge

The client’s operations were heavily impacted by noisy alerts and inefficient triaging processes, with SMEs spending 60–70% of their time on incident investigation and resolution. Alerts were not aligned with service dependencies, resulting in duplicate notifications and increased operational overhead.

Additionally, inconsistencies between monitoring tools such as PagerDuty and Datadog further complicate alert prioritization. The organization’s SRE maturity was at a basic level, with limited observability and a lack of standardized processes.

Given that a significant portion of revenue was driven by operations in the U.S., ensuring high availability and rapid incident resolution was critical. The client required a robust, scalable solution to improve reliability, reduce alert noise, and enhance operational efficiency.

 

Our Approach

SRE Operating Model Implementation

Established dedicated SRE Core (build) and SRE Run teams to enable continuous monitoring, incident management, and escalation.

24x7 Observability & Monitoring

Implemented centralized “eye-on-glass” monitoring across geographies, ensuring real-time visibility and faster incident detection.

AIOps-Driven Alert Optimization

Leveraged AI-driven analytics to reduce alert noise, improve prioritization, and eliminate duplicate alerts through dependency mapping.

Runbook Standardization & Process Maturity

Conducted tabletop exercises to identify gaps and enhance runbooks, enabling consistent and efficient incident resolution.

SRE Best Practices Adoption

Introduced SRE principles, including SLIs/SLOs, blameless postmortems, and root cause analysis to improve reliability and operational discipline.

Partner / Technology Ecosystem

  • Datadog (Observability)
  • PagerDuty (Incident Management)
  • AIOps & Analytics Platforms

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Impact to Date

20×

Improvement in Mean Time to Repair (MTTR)

15×

Reduction in Alert Noise

99.999%

System Availability Achieved

Improved

Predictive Monitoring & Incident Prevention

Business Impact

 

  • Reduced operational overhead by minimizing alert fatigue and manual triaging
  • Improved system reliability with proactive monitoring and predictive analytics
  • Enhanced incident response through standardized runbooks and SRE practices
  • Enabled autonomous operations with AI-driven triage and self-healing capabilities
  • Strengthened operational maturity with scalable, data-driven SRE framework

Coforge enabled the client to evolve from reactive incident management to an AI-led, proactive SRE model, significantly improving system reliability, reducing alert fatigue, and accelerating incident resolution. This transformation established a scalable, intelligent operations framework that ensures high availability, operational efficiency, and sustained business performance.