Your support team spends 40% of time on questions already answered in docs, while genuine bugs sit in queue.
Support triage is where most SaaS founders lose CSAT points without realizing it. A ticket lands in your inbox. Your team doesn't know if it's a billing question (answer in 2 minutes) or a data corruption bug (escalate to eng immediately). So they read it. Then they search docs. Then they draft a reply. Then they second-guess the reply. Forty-five minutes later, the customer is annoyed, your team is context-switched, and your engineer is still waiting. The fix: build a triage gate that runs before human eyes touch the ticket. Here's the workflow: ticket arrives → AI classifier reads the subject and body → assigns severity (critical, high, medium, low) and category (billing, feature request, bug, account access, other) → if it's a FAQ match (billing, password reset, feature explanation), draft a reply from your help docs → if it's critical or novel, flag for human review → if it's low-severity feature feedback, batch it for product → human support lead approves or rewrites the draft before sending. The result: 60% of tickets get a documented answer in under 2 minutes. Your team focuses on the 40% that actually need judgment. Engineering stops getting pinged about password resets. CSAT climbs because customers get faster first responses, even if those responses are templated. The key constraint: the AI classifier must be trained on your actual ticket history, not generic support categories. A "bug report" in your domain might be a known limitation. A "feature request" might be a misunderstanding of existing functionality. Spend a week tagging 200 of your recent tickets by hand. Feed those examples to your classifier. Then let it loose. Failure mode: the classifier is too aggressive. It marks a real bug as FAQ and sends a canned reply. The customer escalates. You've now burned trust and added a second ticket. Prevent this by setting the approval gate high for anything marked "critical" or "bug." Let the human support lead read those before they ship.
Trigger: New ticket arrives in your support inbox (email, Zendesk, Intercom, or custom webhook).
Refunds, legal, security → escalate
1. Export your last 200–500 support tickets (subject, body, category, resolution time). 2. Tag 200 of them by hand: severity (critical/high/medium/low) and category (billing, bug, feature request, account access, documentation, other). 3. Use an LLM API (OpenAI, Anthropic, or open-source via Hugging Face) to fine-tune or prompt-engineer a classifier on your labeled set. Test it on 50 holdout tickets; aim for 85%+ accuracy on severity and 90%+ on category. 4. Build a webhook or Zapier integration that intercepts incoming tickets, runs the classifier, and either (a) drafts a reply from your help docs using vector search (Pinecone, Weaviate) or (b) flags the ticket for human review with a priority label. 5. Set up a human approval step: support lead reviews all "critical" and "bug" tickets before they're sent. 6. Monitor false positives weekly. Retag misclassified tickets and retrain monthly.