Summary
Business Challenge
A US retailer was managing a growing customer support queue through Zendesk. Its 10-person support team handled questions about orders, deliveries, returns, refunds, account access, and product issues.
Ticket volume was increasing, but much of the team’s time was not spent resolving customer problems. Agents had to read unstructured messages, identify the issue, assign categories, update tags, search for relevant information, and draft responses from scratch.
The retailer wanted to increase support capacity without immediately hiring more agents or replacing its existing Zendesk environment.
The Solution
We built and deployed an AI automation layer connected directly to Zendesk.
The system processes every new support request before it reaches an agent. It reads the ticket, identifies the customer’s intent, extracts relevant information, assigns the appropriate category and urgency level, and detects missing details.
The AI agent then uses the structured ticket data, approved support templates, the retailer’s knowledge base, and examples from resolved cases to prepare a complete response draft.
Instead of starting with a raw customer message, the support agent receives a categorized ticket with a resolution-ready reply inside Zendesk.
How the Zendesk automation works:
1
A ticket enters Zendesk
A customer submits a request through email, chat, or a website form. The message may contain incomplete details, inconsistent formatting, or several questions in one request.
2
The workflow starts
A Zendesk webhook sends the new ticket into the automated workflow as soon as it is created.
3
The AI agent normalizes the request
The system converts the unstructured message into a consistent record containing:
- Customer intent
- Support category
- Urgency level
- Order or account details
4
A response draft is generated
The AI agent selects the appropriate support workflow and creates a personalized response using approved company language and available customer context.
5
A support agent reviews the response
The assigned agent reviews the draft inside Zendesk. They can send it, edit it, request more information, or escalate the case.
6
The outcome is logged
After the agent takes action, it updates the ticket status, applies the relevant tags, and records the AI recommendation and final agent decision.
Project Results
After the automation was deployed in production, the retailer reduced the amount of manual work required before an agent could respond to a customer.
