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Automated AI Ops Alert Filtering System for IT Operations

Automated AI Ops Alert Filtering System for IT Operations

A global enterprise IT service provider based in Toronto, Canada, managed over 20,000 infrastructure alerts daily across multiple cloud and on-prem environments. The operations team struggled with excessive false positives and redundant incident notifications, which distracted engineers from genuine issues. To optimize response efficiency, the company partnered with our engineering team to deploy an AI Ops-driven alert suppression system that filtered redundant events and grouped related incidents intelligently.

Image representing case study

72%

Reduction in redundant alerts

50%

Decrease in false-positive incident tickets

38%

Improvement in MTTA

About  

Problem Statement

  • Redundant alerts overloaded the IT incident queue, slowing down triage.
  • 65% of generated alerts were repetitive with identical root causes.
  • False positives led to alert fatigue and slower escalation response.
  • Manual correlation across multiple monitoring tools caused delays.
  • Lack of unified visibility hindered cross-environment incident management.

Industry: IT Operations & Cloud Services

Services: AI Ops, Automation, Monitoring Optimization

Region: Canada

Our Tech Stack

Tech stack we used

Solution Approach

  • Collected alert data from Splunk, Nagios, and Datadog, normalizing formats using Logstash.
  • Built a correlation model in PyTorch that clustered alerts by source, pattern, and severity similarity.
  • Implemented Kafka-based streaming to feed the model with live alerts for on-the-fly classification.
  • Integrated AWS Lambda functions that suppressed redundant incidents and grouped correlated alerts.
  • Connected Elasticsearch to tag and archive filtered alerts for later analysis.
  • Configured PagerDuty API for intelligent escalation only when critical thresholds were breached.
  • Visualized performance and suppression ratios in Kibana for continuous improvement cycles.

Benefits

  • Reduced total alert volume by 72%, improving operational focus.
  • Enhanced accuracy in incident escalation and prioritization.
  • Cut false-positive notifications across multiple monitoring tools.
  • Lowered mean time to acknowledgment (MTTA) by 38%.
  • Boosted IT team productivity with unified and contextual incident insights.

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