Supreme Exterior Cleaning: GHL CRM, Automation & 4-Channel AI Chatbot

The Problem

Supreme Exterior Cleaners was receiving inbound messages across four different channels (WhatsApp, SMS, website chat, Facebook Messenger) with no automated response system. Each channel required manual monitoring and manual replies. Leads that came in outside business hours went unanswered. The owner was personally handling every initial inquiry before any qualification had happened, creating a bottleneck from first message to booked job.

The Solution

4 parallel systems sharing a single AI core. Architecture is identical across all channels: customer message received in GHL on a channel-specific trigger → GHL sends payload to n8n via Custom Webhook → n8n normalises the payload in an Edit Fields node → AI Agent (OpenAI) queries the Supabase vector store knowledge base for relevant business information → generates a contextual response with conversation memory → response returned to GHL → GHL sends reply back on the originating channel. WhatsApp and SMS use GHL-side workflows with n8n handling the AI logic. Website chatbot uses a pure n8n webhook response pattern with no GHL dependency. Messenger routes through GHL with the same n8n AI backend. All 4 channels share the same OpenAI agent and Supabase knowledge base — consistent answers regardless of how a customer contacts the business.

Tech Stack

Screenshots

Results

• All 4 inbound channels (WhatsApp, SMS, Website, Messenger) handled by one AI agent — no channel monitored manually
• Single knowledge base in Supabase vector store serves all channels — answer consistency guaranteed regardless of how the customer reaches out
• Per-customer conversation memory maintained across sessions on every channel
• Website chatbot operates independently of GHL via pure n8n webhook — no dependency on GHL availability
• Owner no longer manually responding to initial inquiries — AI handles qualification before any human involvement