Enterprise Incident Intelligence Platform
A data-driven intelligence layer built on top of ITSM platforms to detect patterns, summarize complex outages, and provide actionable trend analysis for Directors and VPs.
The Problem
Leadership lacked visibility into recurring incident patterns, relying on raw ticket data that obscured systemic root causes.
Why Traditional Systems Failed
Standard ITSM reporting tools provide operational metrics (MTTR, ticket counts) but fail to synthesize narrative insights or correlate isolated events into systemic problem statements.
AI-Driven Approach
Built an aggregation pipeline that ingests structured incident data and historical records. Applied a multi-stage analysis engine using localized AI models to cluster similar incidents, extract key entities, and generate executive-level summaries. The architecture decouples data ingestion from analysis, allowing for back-testing of new pattern recognition logic against historical data.
High-Level Architecture
[Architecture Diagram Placeholder]
Flow: Data Ingestion → Vector Embedding → LLM Router → Response Synthesis
*Detailed interaction diagrams available upon request during interview.
Business Value
Shifted leadership focus from reactive firefighting to proactive systemic remediation by surfacing hidden incident clusters.
Key Learnings
- ✓Data quality in manual ticket entry is the biggest bottleneck; pre-processing and normalization are 80% of the work.
- ✓Executives prefer concise, narrative summaries over raw dashboards.
- ✓Feedback loops are essential: allow users to flag "false positives" in pattern detection to retrain the clustering logic.