AI Triager: Faster Root Cause Analysis for Test & IT Ops Teams

Problem Statement

QA, DevOps, and IT Ops teams were spending hours per defect sifting through logs to identify root causes. Manual triaging was

• Time-consuming and inconsistent
• Dependent on expert availability
• Not scalable across large test suites
• Lacking centralized context or reuse of past insights

What We Solved

We built AI Triager, a modular, AI-driven RCA engine that automates the entire failure analysis pipeline:

• Extracts structured meta data from logs using LLMs
• Uses hybrid retrieval (graph + vector search) to match symptoms with known defects
• Streams real-time, explainable RCA summaries, including root cause, fix plan, and references
• Classifies logs in batch (pass/fail/abort) and generates automated reports
• Integrates into CI/CD, bug tracking, and custom dashboards via APIs

Conclusion: What We Achieved

Through AI Triager, we transformed manual, error-prone debugging into a fast, explainable, and scalable process.

By embedding intelligence into each step—from ingestion to resolution—we empowered QA and DevOps teams to:

• Move from reactive to proactive debugging
• Speed up release cycles by resolving issues faster
• Build a continuously learning system that improves with feedback
• Reduce burnout from repetitive triage work