Complaint Handler
Detect frustration, escalate intelligently
Analyze incoming messages for tone, urgency, and emotional intensity, then route complaints to senior agents with a summary and recommended resolution. Prevents customers from churning due to slow or tone-deaf responses to critical issues.
GPT-6 Luna
for 400K tokens a month · 70% input / 30% output
Why this model
For this workload — 400K tokens a month, 70% of them input — it comes to about $0.09 a month, the lowest of the models suggested here.
Alternative models
Implementation tips
- 1
Go beyond binary sentiment: ask for an intensity score from 1–10 and a 'churn risk' label — this lets you triage the most critical complaints first rather than treating all negatives equally.
- 2
Train on your own resolved complaint history: include 5–10 examples of escalated vs. non-escalated complaints in the system prompt to calibrate the model to your specific thresholds.
- 3
Generate a one-sentence 'agent briefing' alongside each escalated ticket so the human agent has immediate context without reading the full message thread.