CallSense-AI

Overview

CallSense-AI analyses unstructured customer conversation data to uncover patterns affecting customer experience, escalation behaviour, and revenue loss. It uses NLP and LLM-powered analytics to transform raw call transcripts into structured insight through behavioural analytics, latent intent discovery, clustering, and classification.

Problem Framing

The project is designed around a practical business problem: helping teams understand what is happening inside customer conversations, where friction is emerging, and which operational issues deserve priority.

Key Highlights

• Raw transcript analysis
• Latent intent discovery
• Zero-shot classification
• Embedding-based clustering
• Escalation analysis
• PII redaction before downstream inference
• Business-impact analysis from conversation patterns

Results

• Identified subscription cancellations as 38% of escalations while representing only 25% of total call volume
• Quantified $3,400+ in monthly losses linked to talk-ratio overload in technical support interactions
• Surfaced patterns that traditional contact centre metrics could miss