From alert overload to 93% fewer alerts

The Challenge

The client’s growing digital environment generated millions of logs, metrics, and events each day. Redundant and low-priority alerts made it harder for operations teams to identify issues that required action.

Monitoring data sat across multiple platforms. Engineers had to move between systems to understand an incident and investigate its likely cause. With most incidents detected after user impact, the team spent much of its time reacting to problems rather than preventing them.

As the environment grew, manual triage placed increasing pressure on the team’s capacity.

The Solution

Aditi implemented an AIOps framework across the client’s hybrid environment, bringing observability, diagnostics, and automation into the incident response workflow.

• Connect monitoring data: Integrated logs, metrics, and traces from multiple platforms into a centralized view so engineers could see incidents in context.
• Correlate alerts: Used intelligent agents to connect related symptoms, identify likely causes, and prepare diagnostic summaries with recommended actions.
• Detect anomalies earlier: Established machine-learning baselines to identify changes in system behavior and trigger investigations before user impact.
• Make changes repeatable: Used infrastructure and CI/CD automation to support consistent workflows across environments.
• Work within existing processes: Routed alerts and root-cause summaries into the collaboration and service-management tools the team already used.