AIOps Case Study

Transforming IT Operations with Artificial Intelligence

About the Project

With customer expectations for digital services at an all-time high, ensuring seamless availability and performance is critical for any large-scale online platform.

One of our clients — a global e-commerce leader — operates over 50 microservices hosted across AWS and Azure, serving millions of daily active users and managing thousands of transactions every second.

However, their rapid growth created a surge in system data and operational complexity that traditional monitoring and manual troubleshooting could no longer handle efficiently. Faced with rising incident volumes and extended downtimes during traffic peaks, the company turned to Spundan to help modernize its operations through AIOps — Artificial Intelligence for IT Operations.

Challenges Faced

Prior to implementing AIOps, the client faced multiple operational bottlenecks:

High Incident Volumes

Manual incident detection and resolution could not keep up with the volume and velocity of log data across distributed systems.

Slow Resolution Times

Root cause analysis (RCA) required time-consuming manual investigations, leading to higher Mean Time to Resolution (MTTR).

Limited Visibility

Siloed monitoring tools provided fragmented insights into system health.

Reactive Troubleshooting

IT teams were stuck in a constant loop of firefighting instead of preventing issues proactively.

Spundan's Solution

Spundan's DevOps and AIOps specialists designed and executed a phased rollout tailored to the client's complex hybrid cloud architecture.

Key solution elements included:

Centralized Observability

We integrated diverse data streams — logs, metrics, traces — into a unified observability layer to provide a complete view across AWS, Azure, and Kubernetes clusters.

Machine Learning-Driven Monitoring

Advanced ML models were deployed to detect anomalies in real time, reduce false positives, and correlate related incidents automatically.

Automated Incident Management

We configured smart workflows to generate tickets, trigger alerts, and launch predefined remediation actions for common, recurring issues — drastically reducing manual intervention.

Root Cause Automation

Intelligent correlation engines and dependency mapping helped teams identify the root cause of incidents in minutes, not hours.

Continuous Learning

Feedback loops enabled the AI engine to learn from each resolved incident, improving detection accuracy and response efficiency over time.

Change Enablement

Spundan provided training sessions and best practice workshops to ensure development and operations teams fully adopted the new AI-driven processes.

Implementation Timeline

The project was delivered in four phases over six months:

1

Assessment & Planning

Detailed review of existing tools, data pipelines, and incident management workflows.

2

Pilot Deployment

AIOps platform implemented on critical services to validate impact and tune ML models.

3

Full Rollout

Expanded to all microservices and hybrid cloud infrastructure.

4

Continuous Improvement

Ongoing model tuning, automation enhancements, and team enablement.

Key Outcomes

“With Spundan's AIOps expertise, we've transformed the way our IT teams operate. We've cut incident resolution times in half and now resolve issues before they reach our customers.”
— Head of Cloud Operations, Global E-Commerce Client

  60% reduction in Mean Time to Resolution (MTTR)

  40% fewer major incidents impacting users

  Unified, real-time observability across all cloud and on-premise services

  Increased operational efficiency freeing engineering teams to focus on delivering new features and improvements

Lessons Learned

Data Quality is Critical

Successful AIOps depends on clean, well-integrated data pipelines.

Team Buy-In Drives Results

Change management and practical training ensured teams trusted and embraced AI-assisted operations.

Continuous Tuning Adds Value

Regular monitoring and model updates were essential for sustained performance improvements.

Conclusion

Through its AIOps transformation, this leading e-commerce platform moved from reactive troubleshooting to proactive, intelligent operations — delivering uninterrupted, high-quality services to millions of users worldwide.



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