Build a Multi-Channel AI Sales Agent (WhatsApp, Text & Email)

Quick Overview

The video demonstrates how to build a multi-channel AI sales agent using an n8n workflow that unifies inputs from WhatsApp, SMS, and Email into a consistent format, processes the lead information via an AI Agent utilizing PostgreSQL for chat memory, and updates an AirTable CRM with consistent data fields regardless of the incoming communication channel.

Key Points: The workflow uses a central 'Session ID' module to unify incoming messages from four channels: Email Trigger, Incoming SMS, WhatsApp, and n8n Chat. The 'Load Lead' module searches an AirTable Leads base using a complex formula that checks Lead ID, phone numbers (SMS/WhatsApp), and email to find an existing lead or determine if a new one should be created. The AI Agent is configured with both an OpenAI Chat Model (GPT-4.1 Mini) for conversation and a PostgreSQL Chat Memory node to maintain context using the Lead ID as the key. The AI Agent's system prompt is customized to instruct it to always ask for Name, Email, and Company if that information is missing before answering product questions. The 'Channel Outputs' section uses a Switch node to correctly route responses back to the originating channel (WhatsApp, SMS, Email, or n8n Chat) based on the platform variable identified earlier. The AirTable base is set up with specific columns (Email, Lead ID, First Name, Last Name, Company, Deal Size, SMS #, WhatsApp #, n8n Session ID) to capture normalized data from all channels. The demo confirms that when an SMS arrives, the system successfully searches for the lead using the phone number and, after the AI responds, updates the record in AirTable with the correct information.

Context: The video is a technical tutorial demonstrating how to construct a resilient, multi-channel AI sales agent workflow using the n8n automation platform. The core challenge addressed is normalizing data from disparate communication sources (WhatsApp, SMS, Email, and n8n Chat) so that a subsequent AI agent can consistently interact with the lead, retrieve existing CRM data from AirTable, and store conversation history via a PostgreSQL database.