Improve Ecommerce Support Response Times with Custom AI Agents

Most online stores use helpdesks like Gorgias or Zendesk. Their automation is based on rigid keyword rules. A rule that tags any email containing "return" will miss a customer who writes, "This isn't what I expected, how do I send it back?" This means agents still have to read and manually categorize a huge number of tickets, defeating the purpose of the automation.

Consider this common scenario for an apparel store. A customer emails: "Hi, order 12345 arrived but this shirt is too small. Can I swap for a Large?" A human agent must perform a 5-minute manual checklist: open Shopify, search for order 12345, confirm the product, check if the Large size is in stock, and finally, copy-paste a link to the returns portal. For a team handling 200 tickets a day, this manual data lookup consumes hours of agent time that could be spent solving more complex problems.

The structural problem is that helpdesk platforms are not deeply integrated with your backend ecommerce systems. They can see that a ticket exists, but they do not have native access to your real-time inventory levels, customer lifetime value, or specific return eligibility rules. Their automation can trigger a notification, but it cannot perform the multi-step data gathering and decision-making that an experienced agent does instinctively.

The project would begin with an audit of your last 3 months of support tickets. Syntora identifies the top 5 most frequent and time-consuming ticket types, mapping out every manual step your agents currently take to resolve them. This discovery process produces a clear plan, prioritizing the automations that will have the largest impact on your team's response time.

The technical system would be built on AWS Lambda, using the Claude API for language understanding. When a new ticket arrives in your helpdesk, a webhook triggers a Lambda function. This function sends the ticket text to the Claude API to extract the customer's intent (e.g., 'exchange_request') and key data (order_id: '12345'). The function then uses the Shopify API to fetch order details and check inventory, executing your specific business logic in Python.

The result is delivered back to your helpdesk as an internal note, all within 15 seconds of the ticket's arrival. Your agent opens the ticket and sees a complete summary: customer name, order details, inventory status of the requested item, and a pre-drafted response. The agent's job shifts from data entry to high-level review, allowing them to handle 3-4 times as many conversations per hour.