Project Overview

Architected and developed an end-to-end enterprise WhatsApp automation platform from concept to production deployment for M7 Corporation. This comprehensive solution enables businesses to manage customer communications at scale through WhatsApp Business API integration, featuring real-time chat dashboards, multi-agent support, and advanced automation capabilities.

The platform serves as a central hub for customer engagement, allowing teams to handle thousands of conversations simultaneously while maintaining personalized service quality. Built with modern web technologies, it ensures low latency, high reliability, and seamless user experience.

Key Features

• Real-time Chat Dashboard: Live message streaming using Django Channels and WebSockets for instant communication without page refreshes
• Multi-Agent Support: Team collaboration features allowing multiple agents to manage conversations with role-based access controls
• Automated Message Templates: Pre-approved WhatsApp templates for quick responses to common queries, reducing response time by 70%
• Broadcast Messaging: Bulk message capabilities for marketing campaigns and customer notifications with delivery tracking
• Chat Assignment & Routing: Intelligent conversation routing based on agent availability, expertise, and workload
• Contact Management: Comprehensive CRM features for storing customer information, interaction history, and custom attributes
• Analytics & Reporting: Detailed insights on response times, conversation volumes, agent performance, and customer satisfaction metrics
• Media Handling: Support for sending and receiving images, videos, documents, and location data through WhatsApp

Technical Architecture

Backend: Django framework with Django Channels for WebSocket support, enabling bidirectional real-time communication. Redis serves as the channel layer for message brokering and caching frequently accessed data.

Database: PostgreSQL for persistent data storage with optimized queries and indexing strategies ensuring sub-second response times even with millions of messages.

Message Queue: Celery with Redis for handling asynchronous tasks like broadcast message sending, report generation, and webhook processing.

WhatsApp Integration: Official WhatsApp Business API integration using webhooks for receiving messages and REST API for sending messages with automatic retry mechanisms for failed deliveries.

Deployment: Hosted on AWS EC2 with Nginx as reverse proxy and Gunicorn as WSGI server. Daphne handles ASGI requests for WebSocket connections. Auto-scaling configured to handle traffic spikes.

Project Impact

• Successfully handling 10,000+ daily conversations across multiple business accounts
• Reduced average response time from 15 minutes to under 2 minutes
• Improved customer satisfaction score by 45% through faster, more consistent responses
• Automated 60% of routine customer inquiries through smart templates and chatbot integration
• Generated $50K+ in attributed sales through targeted WhatsApp campaigns in first quarter
• Achieved 99.8% platform uptime with robust error handling and monitoring

Challenges Overcome

• Implemented efficient WebSocket connection management to support 500+ concurrent agents
• Designed database schema to handle high-velocity message ingestion while maintaining query performance
• Built resilient webhook processing system to handle WhatsApp API rate limits and temporary failures
• Created intuitive UI/UX for agents to manage multiple conversations without overwhelming them