Ticket Triage Automator

Python

Flask

Redis

PostgreSQL

Docker

Saved ~4 hours per week in manual triage effort

The Problem

Manual ticket triage consumed hours every week, leading to slow response times and inconsistent categorization across the support team.

The Solution

Built a rules-based automation engine that ingests incoming support tickets, classifies them by severity and department using keyword matching and pattern analysis, and routes them to the correct queue. The system integrates with existing ticketing APIs and provides a dashboard for monitoring triage accuracy and throughput. Designed to be easily extensible with new classification rules without redeployment.

Key Features

Automated ticket classification by severity (P1-P4) using configurable rule sets
Integration with REST-based ticketing systems via webhook listeners
Real-time monitoring dashboard showing triage throughput and accuracy metrics
Configurable routing rules with support for team-based and skill-based assignment
Audit logging for every triage decision with full rule-match traceability

View on GitHubDemo available on request