Triochat
AI-Powered WhatsApp Business Communication Platform
Overview
Triochat is a comprehensive WhatsApp business communication platform that enables businesses to manage customer conversations, automate responses, and leverage AI for enhanced customer engagement. The platform serves as a centralized hub for multi-agent collaboration and real-time messaging.
The Challenge
Businesses struggle to manage high volumes of WhatsApp conversations efficiently. Manual handling leads to delayed responses, missed opportunities, and poor customer experience. Additionally, there's no unified platform for team collaboration on customer queries.
Key Features
Core functionality delivered
Unified multi-agent inbox with real-time messaging
AI-powered response suggestions and auto-replies
WhatsApp API integration with webhook handling
Team collaboration tools with assignment and escalation
Customer segmentation and tagging system
Automated message templates and workflows
Analytics dashboard with performance metrics
Stripe payment integration for premium features
Architecture
Triochat follows a microservices-inspired architecture with clear separation between frontend, API gateway, and service layers. The system leverages real-time capabilities for instant messaging and AI integration for intelligent features.
Next.js 14 with App Router, React Server Components for optimal performance
Zustand for client state, React Query for server state caching
Socket.io for bi-directional communication
Node.js/Express microservices for business logic
Supabase (PostgreSQL) with Row Level Security policies
OpenAI API with custom prompt engineering layer
Stripe webhook handling for subscription management
Posthog for product analytics and error tracking
Challenges & Solutions
Technical challenges faced and how we overcame them
Project Gallery
Explore the interface and features through our project gallery
Main dashboard showing conversation overview
Templates and categories for easy management
Login and registration flow
Landing page with features and pricing
Key Learnings
Real-time systems require careful consideration of edge cases like connection drops and message ordering
AI integration needs robust fallback mechanisms when AI services are unavailable
User experience significantly improves with optimistic UI updates
Comprehensive logging and monitoring are essential for production systems
Database indexing strategy dramatically affects query performance at scale
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