An AI agent that automatically triages incoming customer support tickets for an e-commerce store. It analyzes the ticket content to classify the issue (e.g., 'refund request', 'shipping status', 'damaged item') and routes it to the correct department or agent in Slack.
Online stores receive a steady mix of where-is-my-order emails, refund requests, damaged item photos, fit questions and address changes, all landing in the same helpdesk queue. Someone has to open each ticket, work out what it is about, look up the order in Shopify and decide who should handle it. During sales events or courier delays the queue balloons, urgent issues such as a wrong address before dispatch get buried behind simple tracking requests, and customers wait longer than they should.
Rule-based helpdesk tags help a little, but they rely on keywords that customers do not reliably use. A message saying the parcel arrived in pieces might never mention the word damaged. Staff end up re-tagging tickets by hand, and the warehouse or logistics team often hears about a problem only when a frustrated customer escalates. The support lead has little visibility of which issue types are rising until someone manually counts them at the end of the month.
A new ticket in Gorgias or Zendesk triggers an n8n workflow that sends the message text, subject and any order number to a language model for classification.
The model returns a category such as refund request, shipping status, damaged item or address change, plus an urgency level and a confidence score.
For order-related categories the workflow queries the Shopify API for fulfilment status, tracking link and line items, and attaches that context to the ticket.
Low-risk categories get a drafted first reply using the live order data; refunds, damage claims and low-confidence classifications go to a human without an automatic response.
The workflow posts a summary into the matching Slack channel, such as logistics or returns, and tags the ticket so the helpdesk routes it to the correct queue.
A customer emails support about a missing package. The AI agent reads the email, classifies it as 'Order Inquiry - Shipping', pulls the order status from Shopify, and provides an initial response to the customer while also notifying the logistics team in a specific Slack channel.
n8n, NLP for classification (OpenAI), Gorgias/Zendesk API, Shopify API, Slack API
Customer names, addresses and order histories are personal information under the Privacy Act 1988, so only send the minimum needed to the AI provider and check where that provider stores data. Replies about refunds must not suggest customers have fewer rights than the Australian Consumer Law gives them; the consumer rights it sets out mean a faulty or damaged item can entitle the customer to a repair, replacement or refund regardless of your store policy. Avoid automated replies that say no refund is available. If an unauthorised person gains access to the ticket data, the Notifiable Data Breaches scheme may apply.
This solution requires careful evaluation of cost-benefit ratio. Consider implementation only after thorough analysis and risk assessment.
Only where you allow it. Most stores let it send a first reply for tracking and order status questions using live Shopify data, while refunds, damage claims and complaints are routed to a person with a suggested draft. You can start with drafts only and switch on automatic replies for specific categories once you trust the output.
Any helpdesk with an API and webhooks can be connected. Gorgias has a close Shopify integration, which makes order lookups straightforward, and Zendesk works through its ticket API. The n8n workflow sits in the middle, so switching helpdesks later means changing the connection steps rather than rebuilding the classification logic.
Two to four weeks is typical. The first week covers agreeing categories and pulling sample tickets, the next builds the helpdesk, Shopify and Slack connections, and the remaining time is testing on real tickets in draft mode. Stores with messy tagging histories may need extra time to settle on clean categories.
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