🚀 SalesOps AI Assistant

An AI-powered post-call automation platform that transcribes, analyzes, and processes sales calls with automated CRM updates and strategic recommendations.

🎯 Features

• 🎙️ Multi-Modal Input: Support for audio files and text transcripts
• 🧠 AI-Powered Analysis: Extract key insights using Google's Gemini models
• 📊 Call Quality Scoring: Automated assessment of sales call performance
• 📧 Auto-Generated Follow-ups: Personalized email drafts ready for review
• 💾 CRM Automation: Direct integration with Google Sheets
• 🎯 Strategic Recommendations: AI-driven next best actions for sales reps
• 📈 Quality Metrics: Track strengths and improvement areas

🏗️ Architecture

┌─────────────────┐
│   Streamlit UI  │
└────────┬────────┘
         │
         ▼
┌─────────────────────────────┐
│  PostCall Orchestrator      │
│  (Sequential Agent)         │
└────────┬────────────────────┘
         │
         ├──► 1. Analyst Agent ────► Extract insights
         │
         ├──► 2. Quality Agent ────► Assess call quality
         │
         ├──► 3. CRM Formatter ────► Save to Google Sheets
         │
         └──► 4. Advisor Agent ────► Generate recommendations

📋 Prerequisites

• Python 3.9 or higher
• Google Cloud Project with Gemini API enabled
• Google Service Account with Google Sheets API access
• Google Sheet for CRM storage

🚀 Quick Start

1. Clone the Repository

git clone git pull origin main --allow-unrelated-histories
cd salesops-ai-assistant

2. Install Dependencies

pip install -r requirements.txt

3. Configure Environment Variables

Create a .env file in the project root:

cp .env.example .env

Edit .env with your credentials:

GOOGLE_API_KEY=your_gemini_api_key_here
CRM_SHEET_NAME=Sales_CRM_Production

4. Set Up Google Service Account

1. Go to Google Cloud Console
2. Create a new project or select existing one
3. Enable Google Sheets API
4. Create a Service Account
5. Download the JSON key file
6. Rename it to service_account.json and place in project root

5. Create Google Sheet

1. Create a new Google Sheet named Sales_CRM_Production (or your custom name)
2. Share the sheet with your service account email (found in service_account.json)
3. Give "Editor" permissions

6. Run the Application

streamlit run app.py

The app will open in your browser at http://localhost:8501

📁 Project Structure

salesops-ai-assistant/
│
├── agents/
│   ├── __init__.py
│   ├── analyst_agent_server.py      # Call analysis & insights extraction
│   ├── quality_agent_server.py      # Call quality assessment
│   ├── advisor_agent_server.py      # Strategic recommendations
│   ├── crm_formatter_agent_server.py # CRM data formatting
│   └── postcall_orchestrator.py     # Main orchestration pipeline
│
├── schema/
│   ├── __init__.py
│   └── models.py                     # Pydantic data models
│
├── tools/
│   ├── __init__.py
│   └── google_sheets_crm.py         # Google Sheets integration
│
├── app.py                            # Streamlit UI application
├── requirements.txt                  # Python dependencies
├── .env.example                      # Environment template
├── .env                             # Your environment variables (git-ignored)
├── service_account.json             # Google credentials (git-ignored)
└── README.md                        # This file

💡 Usage

Text Transcript Analysis

1. Select "Text Transcript" as input type
2. Paste your sales call transcript
3. Click "🚀 Analyze Call"
4. Review results in the organized tabs

Sample Transcript Format

Rep: Hi John, thanks for taking the time today. How are you?

Prospect: Good, thanks for reaching out. I've been looking into solutions for our data pipeline issues.

Rep: Great! Can you tell me more about the challenges you're facing?

Prospect: Well, we're processing about 2TB of data daily, and our current ETL process is taking too long. We need something more efficient.

Rep: I understand. Our platform can handle that volume with 10x faster processing. Would you like to see a demo?

Prospect: Yes, that would be helpful. Can we schedule something for next week?

Rep: Absolutely! How about Tuesday at 2 PM?

Prospect: Perfect, let's do it.

📊 Output Sections

1. Overview Tab

• Prospect name and company
• Sentiment score (1-10)
• Executive summary
• CRM update status

2. Insights Tab

• Identified pain points
• Agreed next steps
• Key discussion topics

3. Follow-up Email Tab

• AI-generated personalized email
• Copy and download options
• Ready for customization

4. Recommendations Tab

• 3 prioritized next best actions
• Strategic sales advice
• Deal progression tactics

5. Quality Report Tab

• Call quality score (1-5)
• Meeting request status
• Strengths identified
• Improvement areas

🔧 Configuration

Customizing Agents

Each agent can be customized by editing their respective files in the agents/ directory:

• AnalystAgent: Modify extraction logic and insights focus
• QualityAgent: Adjust scoring criteria
• AdvisorAgent: Tailor recommendations style
• CRMFormatterAgent: Change CRM field mapping

Adding Custom Fields to CRM

Edit tools/google_sheets_crm.py to add custom columns:

row = [
    datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
    data.get("prospect_name", ""),
    data.get("company_name", ""),
    # Add your custom fields here
    data.get("custom_field", ""),
]

headers = [
    "Timestamp", "Prospect Name", "Company",
    # Add corresponding headers
    "Custom Field"
]

🧪 Testing

Test with Sample Data

# Create a test transcript file
echo "Rep: Hello, this is a test call.
Prospect: Hi, I'm interested in your solution.
Rep: Great! Let me tell you about our features..." > test_transcript.txt

Then paste the content in the app and run analysis.

🐛 Troubleshooting

Common Issues

1. API Key Error

Error: GOOGLE_API_KEY not configured

Solution: Ensure .env file has valid GOOGLE_API_KEY

2. Service Account Error

Error: service_account.json not found

Solution: Download service account JSON and place in project root

3. Sheet Not Found

Error: Spreadsheet 'Sales_CRM_Production' not found

Solution:

• Create the Google Sheet
• Share with service account email
• Verify CRM_SHEET_NAME in .env

4. Permission Denied

Error: The caller does not have permission

Solution: Share Google Sheet with service account email as Editor

🔐 Security Best Practices

1. Never commit sensitive files:

   • .env
   • service_account.json

2. Add to .gitignore:

   .env
   service_account.json
   *.pyc
   __pycache__/
   

3. Use environment variables for all credentials

4. Regularly rotate API keys and service account keys

🚀 Deployment

Deploy to Streamlit Cloud

1. Push code to GitHub (excluding .env and service_account.json)
2. Go to Streamlit Cloud
3. Deploy from your repository
4. Add secrets in Streamlit Cloud dashboard:
   • GOOGLE_API_KEY
   • Paste service_account.json content as SERVICE_ACCOUNT_JSON

Deploy to Other Platforms

• Heroku: Use buildpacks for Python and configure env vars
• AWS: Deploy on EC2 or ECS with environment configuration
• Google Cloud Run: Containerize and deploy with secrets

📈 Roadmap

• Direct integration with Zoom, Google Meet, Loom
• Real-time audio transcription
• Multi-language support
• Advanced analytics dashboard
• Integration with popular CRMs (Salesforce, HubSpot)
• Custom agent training on company data
• Slack/Teams notifications
• Mobile app

🤝 Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Submit a pull request

📄 License

This project is licensed under the MIT License - see LICENSE file for details.

💬 Support

For issues, questions, or suggestions:

• Open an issue on GitHub
• Contact: [your-email@example.com]

🙏 Acknowledgments

• Built with Google AI Development Kit (ADK)
• Powered by Google Gemini
• UI with Streamlit

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Built with ❤️ for Sales Teams