Customer Support Triage Agent
| Status | Duration | Type | Tools |
|---|---|---|---|
| Deployed ✅ | 90 Mins | Sprint | OpenAI, Peekaboo |
Problem
Customer support tickets were piling up, and the team was spending hours manually triaging them. We needed a way to automatically categorize and prioritize incoming tickets without adding more headcount.
The existing workflow required a human to read each ticket, assess urgency, categorize by issue type, and assign to the right team member. This was taking 2-3 hours per day and creating bottlenecks in our response time.
What We Built
An AI agent that reads incoming support tickets, categorizes them by urgency and topic, routes them to the right team member, and suggests automated responses for common issues.
The agent runs on every new ticket submission, analyzes the content using GPT-4, applies a custom classification model we trained on historical tickets, and posts results directly to our ticketing system.
Key Takeaway
The agent reduced response time by 40% and freed up 10 hours per week for the support team. The biggest win was automatic routing—no more manual triage decisions.
Surprisingly, the accuracy was higher than manual triage (92% vs 87%) because the agent could reference historical patterns humans would forget. The team now focuses on actually solving problems instead of categorizing them.
Personal Note
This was our fastest sprint yet. The key was keeping scope tight and shipping a v1 that solved one problem really well instead of trying to automate everything at once.
We initially planned to automate responses too, but decided to focus purely on triage first. That meant we could ship in 90 minutes instead of 2 weeks. The response automation is now Sprint 04.