Triage support tickets in seconds.

How the classifier works

This demo uses rule-based NLP (keyword matching, pattern detection, and heuristic scoring) to simulate how a production triage system classifies tickets. In a real deployment, we'd layer in ML models for higher accuracy, but the logic here demonstrates the core workflow.

Urgency classification

• Critical: System outages, data loss, security breaches, blocked operations with time pressure
• High: Account access issues, payment failures, functionality broken for active users
• Medium: Feature confusion, billing questions, non-blocking bugs, how-to requests
• Low: Feature requests, general feedback, cosmetic issues, informational inquiries

Topic detection

• Scans for keyword clusters: billing/payment, account/login, technical/bug, feature request, security, onboarding, performance, integration
• Falls back to "General Support" when no strong topic signal is found

Sentiment analysis

• Detects emotional signals: frustration markers (exclamation density, caps, threatening language), positive language (thanks, love, great), and neutral tone
• Outputs: Positive, Neutral, Frustrated, or Negative

In production

A real implementation would add: ML-based classification models fine-tuned on your ticket history, auto-routing to the right team/queue, SLA assignment, canned response suggestions, escalation triggers, and integration with your helpdesk (Zendesk, Intercom, Freshdesk, etc.).