N8N Whatsapp Chatbot Workflow

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In this video, I break down the full architecture of a production-grade WhatsApp AI automation system built entirely on n8n — no traditional backend, no bloated SaaS tools.

You'll see how a multi-agent routing system works in practice: a Main Controller receives every incoming WhatsApp message via webhook, a Brain workflow classifies intent, and dedicated sub-agents handle Sales, Support, Lead Capture, and Introductions independently.

The AI doesn't just reply — it retrieves answers from a RAG (Retrieval-Augmented Generation) knowledge base built with pgvector and Google Gemini embeddings. Every response is grounded in real business data, not hallucinated. The knowledge base updates dynamically, so the AI always answers from current information.

Leads are captured automatically into Supabase and routed for follow-up.

𝗪𝗵𝗮𝘁'𝘀 𝗰𝗼𝘃𝗲𝗿𝗲𝗱:

→ n8n multi-agent architecture

→ WhatsApp Cloud API webhook setup in n8n

→ RAG pipeline: pgvector + Gemini embeddings + semantic retrieval

→ Lead capture automation into Supabase

→ n8n as a full API backend — no Express, no Flask

Stack: n8n · WhatsApp Cloud API · Google Gemini · pgvector · Supabase · Chatwoot

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#n8n #WhatsAppChatbot #AIAutomation #MultiAgent #RAG #WhatsAppAI #n8nWorkflow #AIAgent #Supabase #NoCode #WorkflowAutomation

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