Sales Call Analyzer
This tutorial demonstrates how to build an AI-powered sales call analyzer using LangChain and CrewAI, robust frameworks for developing complex language model applications. The goal of this project is to transcribe audio from sales calls, analyze the transcription using natural language processing (NLP) techniques, and generate a detailed report on the call, including sentiment analysis, key phrases, pain points, and recommendations for improvement.
In sales environments, analyzing call transcriptions can provide valuable insights into customer behavior, agent performance, and opportunities for improvement. By automating the process of transcription and analysis, businesses can save time, enhance their training, and improve their customer interactions. This project combines OpenAI's Whisper for audio transcription and CrewAI's task automation to build an efficient, scalable solution for call analysis.
What you'll learn
- 1Audio Transcription: Use OpenAI Whisper to transcribe audio calls into text.
- 2Call Analysis: Define tasks for analyzing the transcription using sentiment analysis, key phrase extraction, customer pain points, agent effectiveness, and m…
- 3Task Automation: Use CrewAI's agents and tasks framework to structure and automate the analysis process.
- 4Report Generation: Generate a detailed, structured report containing actionable insights for improving sales calls.
About this tutorial
This hands-on Jupyter notebook is part of GenAI Agents, a free open-source repository by Nir Diamant covering ai agents techniques with runnable code examples and detailed explanations.
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