AI-powered Zendesk support responses with RAG, OpenAI, and Supabase knowledge base
⚡ How it works
This workflow automates first responses to new Zendesk tickets with the help of AI and your internal knowledge base.
Webhook trigger fires whenever a new ticket is created in Zendesk.
Ticket details (subject, description, requester info) are extracted.
Knowledge base retrieval – the workflow searches a Supabase vector store (with OpenAI embeddings) for the most relevant KB articles.
AI assistant (RAG agent) drafts a professional reply using the retrieved KB and conversation memory stored in Postgres.
Decision logic:
If no relevant KB info is found (or if it’s a sensitive query like KYC, refunds, or account deletion), the workflow sends a fallback response and tags the ticket for human review.
Otherwise, it posts the AI-generated reply and tags the ticket with ai_reply.
Logging & context memory ensure future ticket updates are aware of past interactions.
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🔧 Set up steps
This workflow takes about 15–30 minutes to set up.
Connect credentials for Zendesk, OpenAI, Supabase, and Postgres.
Prepare your knowledge base: store support content in Supabase (documents table) and embed it using the provided Embeddings node.
Set up Postgres memory table (zendesk_ticket_histories) to store conversation history.
Update your Zendesk domain in the HTTP Request nodes (<YOUR_ZENDESK_DOMAIN>).
Deploy the webhook URL in Zendesk triggers so new tickets flow into n8n.
Test by creating a sample ticket and verifying:
AI replies appear in Zendesk
Correct tags (ai_reply or human_requested) are applied
Logs are written to Postgres
