How Indegene Optimizes User Experience with HAQM CloudWatch

In today’s digital healthcare landscape, optimal application performance and user experience are crucial for business success. Indegene, a digital-first life sciences commercialization company, combines deep medical expertise with domain-contextualized technology to help clients accelerate innovation, modernize operations, and improve customer experience. With the world’s top 20 pharma companies among its clientele, Indegene brings an AI-first approach to solving complex business problems for the global life sciences industry. Their NEXT platform includes a sophisticated suite of B2C applications for commercial content authoring. This case study explores how Indegene transformed their monitoring approach using HAQM CloudWatch, driving proactive performance optimization in the process.

The Challenge: Beyond traditional monitoring

Before implementing a comprehensive observability solution, Indegene faced several critical challenges:

• Inconsistent Production Support: The team relied on customer complaints to identify issues, leading to extended system downtime and client inconvenience.
• Complex Integration Dependencies: Their content authoring applications required seamless integration with client Digital Asset Management (DAM) systems like Veeva, adding to monitoring complexities.
• Limited Performance Visibility: Without real-time monitoring, performance bottlenecks often went undetected until they impacted user experience.
• High Mean Time to Resolve (MTTR): Issue resolution typically took several days, affecting system availability and customer satisfaction.

Indegene’s Program Lead shared, “Before implementing observability, our production support was mostly reactive. This led to unexpected system unavailability and an inconsistent customer experience. By addressing this challenge, we’re now delivering superior performance and an enhanced experience to our clients.”

The Solution: Implementing HAQM CloudWatch

Indegene adopted a methodical approach to implementing HAQM CloudWatch by focusing on two critical areas:

1. Third-party integration monitoring using HAQM CloudWatch Synthetics

Indegene turned to CloudWatch Synthetics canaries – a feature that creates configurable scripts (called canaries) to simulate user interactions including page navigation, form submissions, login workflows, and API endpoint testing through automated HTTP requests. They implemented these canaries as their first step, creating automated monitoring of APIs availability.

Indegene set up two primary monitoring systems using CloudWatch Synthetics:

• Veeva API monitoring: A canary was configured to simulate basic API calls to the Veeva API at regular intervals. This acts as an early warning system, providing quick health checks without impacting the production environment.
  
  
• NEXT Web application heartbeat monitoring: Another canary was implemented to regularly send requests to the NEXT web application, verifying its availability and functionality. This heartbeat monitor ensures the application is running and responsive.
  
  The team configured CloudWatch alarms to monitor metrics emitted by the canaries. When these metrics indicate failures or downtime, the alarms automatically trigger email notifications to relevant stakeholders. This allows for faster issue resolution and helps maintain the stability of the system, minimizing any disruptions in service.
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Figure 1: Indegene’s CloudWatch Synthetics implementation architecture

Figure 2: CloudWatch Synthetics canaries created by Indegene

Figure 3: CloudWatch Synthetics canary metrics

2. End user and application behavior analysis using HAQM CloudWatch Real User Monitoring (RUM)

The implementation of CloudWatch Real User Monitoring (RUM) provided near-real time insights into:

• User Behavior Patterns: Detailed analytics on browser usage, geographical distribution, and peak load times.
• Application Performance: Critical Core Web Vitals metrics including First Contentful Paint (FCP) – the time when users first see any content rendered on the page – and Largest Contentful Paint (LCP) – the time when the largest visible element finishes loading, providing comprehensive insights into perceived page loading performance.

The following are some of the telemetry data points used by Indegene to analyze their end user performance using CloudWatch RUM.

Figure 4: Performance related telemetry on CloudWatch RUM console


Figure 5: Browsers & Devices related telemetry on CloudWatch RUM console


Figure 6: Client-side errors captured by on CloudWatch RUM console

Insights derived from using telemetry collected through CloudWatch RUM:

• 5-7% of users accessed via the legacy browsers
  The insight helped in focusing effort on top 3 browsers that constituted 95% of users. Browser-specific development and testing would take up significant time and impact time-to-market. Hence, the remaining 5% of users who were accessing using legacy browsers were encouraged to move to Chrome or Firefox browsers.
  
  
• Spike in page load time (greater than 8 seconds) during high traffic events
  This correlation was an important insight that helped in identifying bottlenecks in application and deployment architecture. Lazy loading and horizontal scaling were implemented among other optimizations to reduce latency
  The above solutions were strategically rolled out across their application ecosystem, starting with two primary applications in the content authoring suite.

Impact and Results

After implementing HAQM CloudWatch, Indegene achieved these measurable improvements:

Incident Response – Reduced Mean Time to Resolution (MTTR) by 35-40%, decreasing from 2~3 days to approximately 8~10 hours. The solution enabled proactive issue detection, addressing problems before they impact end-users.

Performance Optimization – Identified critical performance bottlenecks in pages with load times exceeding 8 seconds. Implementation of targeted optimizations achieved a 50% reduction in overall application load time.

Infrastructure Expansion – Leveraged CloudWatch RUM geographical telemetry data to guide infrastructure expansion in Southeast Asia. This strategic change achieved a 28% reduction in latency for users in the region.

Product Development – Enhanced feature prioritization through user behavior analytics and real user interaction patterns. This data-driven approach enables more strategic product decisions aligned with user needs.

Future Roadmap

The following describes Indegene’s planned observability enhancements:

Feature Adoption Monitoring – Implement comprehensive usage analytics through CloudWatch RUM custom events to track new features and dedicated analytics dashboards for feature-specific insights.

Application Performance Monitoring – Enhance problem detection and resolution capabilities. This includes enabling CloudWatch Application Signals and implementing systems to further reduce Mean Time to Resolution (MTTR).

Platform Expansion – Extend observability solutions across the enterprise. This includes deployment across all four content authoring applications, integration with Super App and NCCI platforms, and automated ticket creation through ServiceNow integration.

Conclusion

In this post, we demonstrated how Indegene leveraged AWS cloud-native monitoring tools to transform their healthcare operations through comprehensive observability. By implementing CloudWatch Synthetics and Real User Monitoring they moved from reactive to proactive application monitoring – resulting in improved application reliability, enhanced customer satisfaction, and measurable business value through operational efficiency. This approach shows how AWS observability solutions can help your organization maintain reliable operations and deliver better experiences, whether for healthcare platforms or other mission-critical systems.

To learn more about AWS Observability services, please check the below resources:

• Hands-on experience with AWS Observability Workshop
• AWS Observability Best Practices Guide
• AWS Skill Builder course on CloudWatch Application Signals

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