CX Agent
AI Triager: Faster Root Cause Analysis for Test & IT Ops Teams
SERVICE
Solution Based Regression
Domain
Artificial Intelligence (AI)
Industry
Networking
Location
USA
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
Let's Craft Brilliance
Just exploring? Let's think out loud together. We would love to hear from you. Come, let's get started!