AI Call Analysis System
A production-ready system that analyzes sales and support calls using AI. Converts recordings into transcripts, extracts KPIs like sentiment, engagement, objections, confidence, and outcomes, generating structured insights for teams at scale.
The Problem
Sales and support teams generate thousands of hours of call recordings, but extracting actionable insights from this data was manual, time-consuming, and inconsistent. Managers needed a way to understand call quality, agent performance, and customer sentiment at scale without listening to every call.
The Solution
I built an end-to-end AI-powered call analysis system that automatically processes call recordings through speech-to-text transcription, then uses LLMs to extract structured insights. The system identifies key moments, tracks objections, measures sentiment throughout the call, and scores overall engagement and outcomes.
Technical Architecture
The pipeline uses advanced speech-to-text models for accurate transcription, followed by custom LLM prompts for insight extraction. Data flows through automated workflows that handle batching, error recovery, and result aggregation. Results are stored in a structured database and surfaced through analytics dashboards for managers and leadership.
Key Features
• Automatic call transcription with speaker diarization • Sentiment analysis tracking throughout calls • Objection detection and categorization • Engagement and confidence scoring • Outcome prediction and classification • Multi-user team dashboards • Batch processing for high call volumes • Custom KPI extraction based on business needs
Impact
The system now processes hundreds of calls daily, providing real-time insights that help teams identify coaching opportunities, track performance trends, and understand customer pain points. What used to take hours of manual review now happens automatically within minutes of call completion.