AI-Driven Incident Analysis System

At McGraw Hill I architected and shipped an AI-driven incident-analysis system that mines historical incident data across 20+ microservices to model failure patterns, auto-generate diagnostic reports, and surface actionable insights — cutting incident triage, tracking, and resolution time by 50%+ and helping prevent recurring outages.

Problem

Incident knowledge was scattered across services, dashboards, and history. Engineers spent significant time re-discovering what had failed before, why, and what resolved it — slowing triage and letting the same classes of failure recur.

Approach

• Mine historical incident data across the service estate to build a corpus of past failures, signals, and resolutions.
• Model failure patterns so recurring signatures and likely root causes can be recognized quickly.
• Generate diagnostic reports that summarize an incident, surface similar prior incidents, and propose actionable next steps for the on-call engineer.
• Surface insights to inform architectural and process decisions, not just individual incidents.

Impact

• 50%+ reduction in incident triage, tracking, and resolution time.
• Coverage across 20+ distributed microservices.
• Fewer recurring outages by turning past incidents into reusable, searchable knowledge.

Details are kept general to respect internal and proprietary specifics.