ChatSeven: Implementation and Result Analysis of an Agentic AI-Based Multi-Agent Platform for Multi-Channel Customer Conversation Management and Campaign Automation

Abstract

Customer conversation platforms increasingly require artificial intelligence, multi-channel messaging, workflow automation, and campaign delivery in a single operational environment. This paper presents the implementation and result analysis of ChatSeven, an agentic AI-based multi-agent platform for customer conversation management and campaign automation. ChatSeven is implemented using a React frontend, an Express/TypeScript backend, Python FastAPI services for Chat AI and vector search, PostgreSQL regional databases, Redis/Bull background queues, and Socket.IO real-time messaging. The system integrates retrieval-augmented generation (RAG), Lang Graph ReAct agents, tool calling through MCP/Zapier-style integrations, visual workflow automation, a unified inbox, and multi-channel campaign delivery.