Customer Support AI Agent
Deployed a fully autonomous support agent that handles 80% of tickets end-to-end using knowledge base lookups, order data, and LLM reasoning — escalating only complex cases.
80%
Tickets auto-resolved
4 min
Avg resolution time
$95K
Annual support savings
The Problem
A direct-to-consumer fashion brand with 50,000 monthly orders was receiving 3,000+ support tickets per month. Their team of six support agents was handling an average of 500 tickets each — unsustainable, especially during peak season. Average resolution time sat at 48 hours. Customer satisfaction scores were slipping.
80% of tickets fell into just six categories: order status, returns/exchanges, shipping delays, sizing questions, discount codes, and product care instructions. All of these had deterministic answers — but a human had to type them out every time.
The Automation We Built
We built a multi-step AI agent pipeline that handles the full lifecycle of a support ticket:
- Classification: Incoming Zendesk ticket is classified by category and intent.
- Data fetch: The agent queries Shopify in real-time for the customer's order history, tracking status, and return eligibility.
- Knowledge base lookup: A vector search retrieves the most relevant policy, FAQ, or product information from the brand's knowledge base.
- Response generation: An LLM composes a brand-voice response, personalised with the customer's name and order details.
- Autonomous resolution: If the ticket falls into a low-risk category, the agent sends the reply and closes the ticket. Higher-risk cases (complaints, refund disputes, order errors) are routed to a human with a summary and recommended action.
Results
Within 30 days of deployment, 80% of all incoming tickets were being resolved fully autonomously. Average resolution time collapsed from 48 hours to 4 minutes. The six-person team now focuses entirely on complex cases, pre-sales support, and VIP customer relationships.
The brand projects $95,000 in annual savings from reduced support headcount growth. Customer satisfaction scores (CSAT) improved from 3.8 to 4.6 out of 5.
Tools Used
- OpenAI GPT-4o — response generation and intent classification
- Zendesk — ticket management and automation triggers
- Shopify API — live order and customer data
- Qdrant — vector database for knowledge base search
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