Implemented AI support triage that tags tickets, summarizes calls, answers common questions, and routes complex issues to the right Zendesk queue.
Challenge
A support team using Zendesk struggled with repetitive tier-one tickets and inconsistent tagging. Agents spent time reading long customer messages, classifying issue type, and routing tickets before any real support work began.
Workflow approach
We built an AI triage layer that classifies incoming tickets, suggests macros, summarizes conversation history, answers simple knowledge-base questions, and routes tickets by issue type and urgency. Low-confidence or sensitive tickets go directly to human agents.
Observed outcome
First response time improved 48%, tier-one queue volume dropped 37%, and every ticket now carries consistent issue tags. Managers gained cleaner support analytics and agents received better context at handoff.
Outcome metrics
- Faster first response
- 48%
- Lower tier-one queue
- 37%
- Issue tags applied
- 100%
- Queues routed
- 6
Systems involved
- Zendesk
- OpenAI
- Pinecone
- n8n
- Slack
- PostgreSQL