Built with
// Case Study
A single AI agent and one knowledge base deployed across all four inbound communication channels for a UK exterior-cleaning business, with per-customer conversation memory carried across every session.
The problem
Supreme Exterior received inbound messages across four channels: WhatsApp, SMS, website chat, and Messenger, with no automated response on any of them. Each channel needed manual monitoring, and after-hours leads went unanswered.
The owner personally handled every initial inquiry, which created a bottleneck and meant the response a customer got depended entirely on whether someone was free.
On each channel a customer message triggers a GHL webhook to n8n, which normalises the payload, queries a Supabase vector-store knowledge base, generates a contextual response with conversation memory, and returns it on the originating channel.
WhatsApp and SMS use GHL-side workflows with n8n handling the AI, so the two most common channels stay inside the CRM the business already runs on.
The website chatbot uses a pure n8n webhook pattern independent of GHL, so it keeps operating even when GHL is unavailable.
Messenger routes through GHL with the same n8n backend, so all four channels share one OpenAI agent and one Supabase knowledge base for consistent answers.
All four inbound channels handled by one AI agent with no manual monitoring.
A single knowledge base guaranteeing answer consistency across channels.
Per-customer conversation memory carried across sessions.
The website chatbot operating independently of GHL availability, and the owner no longer manually responding to initial inquiries.
Built with
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