Automate Ecommerce Returns Processing

Many ecommerce businesses start with Shopify's built-in returns or a dedicated app like Loop Returns. These tools provide a customer-facing portal and let you define basic rules based on dropdown selections. They work well when a customer neatly selects 'Wrong Size' or 'Item Damaged'. But they fail the moment a customer's real-world problem does not fit into a predefined box.

Consider an apparel brand where a customer emails support: "I received my order #12345, and while the jacket fits, the color is much darker than it appeared on my phone. I'd like to send it back." A standard returns app cannot process this. It's not a 'defect' or 'wrong item'. This forces a support agent to manually read the ticket, create a return label in Shopify, and email it to the customer. For a store processing 150 returns a day, this manual work easily consumes a full-time employee's entire day.

Helpdesk platforms like Gorgias or Zendesk can route these tickets with macros, but they cannot make the decision. The agent still has to read, interpret, and act. The structural problem is that these tools are all built on structured data. They require a customer to categorize their own problem. A truly automated system must understand unstructured, natural language text and translate it into a structured business action, a capability these platforms lack by design.

The engagement would begin with a data audit. Syntora would analyze 3 to 6 months of your historical return emails and helpdesk tickets to identify the most common return reasons and your team's historical actions. This analysis informs the classification model and business logic. You receive a report detailing the proposed automation categories and the predicted accuracy for each before any code is written.

The technical system would be built around a core classification pipeline. An AWS Lambda function ingests incoming return requests from your helpdesk API. The text is passed to the Claude API, which classifies the reason (e.g., 'size exchange', 'color mismatch', 'quality issue') and extracts the order number. This process is highly effective; we have built similar document processing pipelines for financial services using this exact pattern. The classification logic is wrapped in a FastAPI service for clean integration.

The delivered system connects directly to your existing tools. A request classified as 'color mismatch' would automatically trigger the Shopify API to generate a return label and email the customer, then tag the Gorgias ticket as 'return-pending'. Requests with low confidence scores or unusual language are flagged for human review. Your team stops processing routine returns and focuses only on the exceptions. The system would be hosted in your AWS account, with monthly costs typically under $50.