Sales Call Analyzer

AI-powered sales call analysis with audio transcription, content scraping, and deep LLM insights.

🎧 Overview

The Sales Call Analyzer provides comprehensive AI-powered call analytics through three main services:

• 🎙 Audio Service – Upload audio files for transcription and analysis
• 👻 Phantom Service – Extract transcripts from URLs using headless browser technology
• 📝 Transcript Service – Analyze pre-provided transcripts directly

🚀 Features

• Multi-Service Analysis: Support for audio files, URL scraping, and direct transcript input
• AI-Powered Transcription: Automatic audio-to-text conversion using Google Gemini API
• Deep LLM Analysis: Comprehensive insights including sentiment, key points, and recommendations
• Modern UI: Beautiful, responsive interface built with Next.js and Tailwind CSS
• Real-time Processing: Live status updates and progress tracking
• Cost Tracking: Monitor API usage and token costs
• Secure Authentication: JWT-based user authentication and authorization

🏗️ Architecture

Backend (Node.js)

• Express.js REST API
• MongoDB with Mongoose ODM
• Google Gemini API for AI processing
• Playwright for web scraping
• JWT authentication
• Winston logging
• Multer file upload handling

Frontend (Next.js)

• React 18 with TypeScript
• Tailwind CSS for styling
• Radix UI components
• React Hook Form for form handling
• Axios for API communication

📁 Project Structure

sales_call_analyzer/
├── Nodejs/                 # Backend API
│   ├── src/
│   │   ├── config/         # Configuration files
│   │   ├── database/       # Database setup
│   │   ├── middleware/     # Express middleware
│   │   ├── models/         # MongoDB models
│   │   ├── routes/         # API routes
│   │   ├── services/       # Business logic
│   │   └── utils/          # Utility functions
│   └── package.json
├── Nextjs/                 # Frontend application
│   ├── src/
│   │   ├── app/           # Next.js app directory
│   │   ├── components/    # React components
│   │   ├── lib/           # Utility libraries
│   │   └── types/         # TypeScript types
│   └── package.json
└── README.md

🛠️ Installation

Prerequisites

• Node.js 18+
• MongoDB
• Google Gemini API key

Backend Setup

1. Navigate to the backend directory:

cd Nodejs

2. Install dependencies:

npm install

3. Create environment file:

cp .env.example .env

4. Configure environment variables in .env:

PORT=5001
MONGODB_URI=mongodb://localhost:27017/sales_call_analyzer
JWT_SECRET=your_jwt_secret_key
GEMINI_API_KEY=your_gemini_api_key

5. Start the backend server:

npm run dev

Frontend Setup

1. Navigate to the frontend directory:

cd Nextjs

2. Install dependencies:

npm install

3. Create environment file:

cp .env.example .env.local

4. Configure environment variables in .env.local:

NEXT_PUBLIC_API_URL=http://localhost:5001/api

5. Start the frontend development server:

npm run dev

🔧 API Services

Audio Service

• Upload audio files (MP3, WAV, M4A, AAC, OGG, FLAC)
• Automatic transcription using Gemini API
• File size and duration validation
• Optional additional context from URLs

Phantom Service

• Extract transcripts from web pages
• Uses Playwright for headless browser automation
• Supports various transcript formats
• Optional additional content scraping

Transcript Service

• Direct transcript analysis
• Support for various transcript formats
• Optional additional context integration
• Real-time validation and processing

📊 Analysis Features

• Call Summary: Comprehensive overview of the conversation
• Sentiment Analysis: Overall sentiment with confidence scores
• Key Insights: Important points and observations
• Action Items: Specific next steps and tasks
• Participant Analysis: Speaker identification and speaking time
• Recommendations: Improvement suggestions
• Risk Factors: Potential issues and concerns
• Opportunities: Growth and development areas

🔐 Security

• JWT-based authentication
• File upload validation
• Rate limiting
• CORS protection
• Input sanitization
• Error handling

📈 Monitoring

• Comprehensive logging with Winston
• API usage tracking
• Cost monitoring
• Performance metrics
• Error tracking

🚀 Deployment

Backend Deployment

1. Set up MongoDB database
2. Configure environment variables
3. Deploy to your preferred platform (Heroku, AWS, etc.)
4. Set up process management (PM2)

Frontend Deployment

1. Build the application: npm run build
2. Deploy to Vercel, Netlify, or your preferred platform
3. Configure environment variables

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

📄 License

This project is licensed under the MIT License.

🆘 Support

For support and questions, please open an issue in the repository.

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Built with ❤️ using Node.js, Next.js, and AI technology.