Customer Service Sentiment Intelligence
AI models analyze customer messages, tickets, and calls to detect sentiment, emotion, and urgency across every service interaction. These insights help teams prioritize at‑risk customers, tailor responses in real time, and surface systemic issues driving dissatisfaction. The result is higher CSAT, faster resolution, and reduced churn through data-driven customer care.
The Problem
“Sentiment + urgency scoring for every support interaction—at message and account level”
Organizations face these key challenges:
Escalations and churn signals are discovered too late (after multiple negative interactions)
Inconsistent triage: urgent angry customers sit in the same queue as low-impact requests
Managers lack reliable trend reporting on what is driving dissatisfaction (by product, region, agent, topic)
QA reviews are manual and sparse, missing tone issues and policy compliance at scale