ChatSeven: An Agentic AI-Based Multi-Agent Platform for Multi-Channel Customer Conversation Management and Campaign Automation

Abstract

Abstract—Customer engagement platforms increasingly re-quire artificial intelligence (AI), multi-channel messaging, work-flow automation, and outbound campaign delivery within a single operational system. Traditional conversational agents—including rule-based chatbots, retrieval-augmented generation (RAG) only bots, and standalone large language model (LLM) interfaces—typically address only a subset of these requirements. This paper presents ChatSeven, an agentic AI-based multi-agent platform for multi-channel customer conversation management and campaign automation, implemented as the ChatSeven production system. ChatSeven integrates LangGraph ReAct agents, PGVector-based RAG, a unified inbox across Web Chat, WhatsApp, Email, SMS, and Instagram, visual workflow Flows, and queue-based campaign broadcasting under a master–regional multi-tenant database architecture. The platform is realized through a React frontend, Express/TypeScript backend, FastAPI Chat and Vector microservices, PostgreSQL, Redis/Bull job queues, and Socket.IO real-time messaging. Experimental evaluation on AI answer quality, RAG retrieval, tool reliability, flow completion, response latency, and campaign delivery demonstrates that ChatSeven closes a critical integration gap between agentic LLM research and enterprise conversation operations. Demo evaluation reports approximately 88% AI answer accuracy, 0.86 Precision@5 for RAG retrieval, 2.4 s average response latency, 90–94% tool success, 84% flow completion, and 96% campaign delivery. Index Terms—Agentic AI; Multi-agent systems; Conversa-tional AI; Retrieval-Augmented Generation; Omnichannel cus-tomer engagement; Campaign automation; LangGraph; Multi-tenant SaaS.