Automate WhatsApp Replies: GHL, Redis & Anthropic AI
Automates intelligent WhatsApp/SMS replies in GoHighLevel using Redis buffering and Anthropic AI for accurate, data-driven responses from ClientInfo.
This workflow integrates GoHighLevel (GHL) with Wazzap for WhatsApp, Redis for message buffering, and Anthropic AI to process inbound messages and generate precise replies. It receives messages via webhook from GHL's Customer Replied trigger, filters out audio messages, sanitizes text, groups rapid messages in Redis, queries ClientInfo via AI Agent for service/branch data, sanitizes the output, updates a GHL custom field (IA_answer), and triggers an outbound automation to send the reply—all without extra inbound costs.
Ideal for businesses using GHL and Wazzap, it ensures responses are accurate and context-aware, reducing manual handling and improving customer engagement. Benefits include time savings on repetitive replies, scalability for high-volume chats, and cost-efficiency by leveraging existing GHL automations.
Use cases: Customer support teams automating FAQs, service inquiries, or branch-specific info for WhatsApp/SMS. Setup requires GHL inbound/outbound automations and a custom field; perfect for agencies or SMBs seeking AI-powered chat without complex coding.
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How to import this workflow into n8n
- 1Purchase or download the workflow to get the n8n workflow JSON file.
- 2In your n8n instance, open Workflows and choose "Import from File" (or paste the JSON with Ctrl+V on the canvas).
- 3Open each node marked with a credential warning and connect your own accounts and API keys.
- 4Run the workflow once manually to verify the data flow, then toggle it to Active.
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