Implement a Custom AI Chatbot for Your Ecommerce Store

Most ecommerce stores use help desk software like Gorgias or Zendesk. Their automation features are based on simple keywords and macros. They can auto-reply to a ticket containing 'Where is my order?' with a link to a tracking page, but they cannot perform multi-step logic or make decisions. They are designed to help human agents work faster, not to resolve issues autonomously.

For example, a customer wants to return a shirt they bought 40 days ago, outside the standard 30-day window. They claim the color faded after one wash, which is a quality issue covered by a separate 90-day policy. A Gorgias macro sees 'return' and '40 days' and sends a templated rejection. The bot cannot analyze the context of 'color faded' to apply the correct policy. A human agent must intervene, find the order in Shopify, read the policy, and manually process the return, taking 10 minutes for a single ticket.

The structural problem is that these platforms are stateless rule engines, not stateful applications. They cannot maintain context across multiple steps. To process the 'faded shirt' return, a system must: 1) get order details from Shopify, 2) check the date against the standard policy, 3) understand the 'quality issue' exception, 4) check the date against the second policy, and 5) generate a return label via a Shippo API call. No help desk automation can execute this chain of dependent logic.

This forces support teams to spend the majority of their time on repetitive inquiries that have clear, rule-based answers. The only way to handle growth is to hire more agents, which increases operational costs and directly reduces profit margins. Customer satisfaction also suffers from inconsistent answers and slow resolution times on seemingly simple requests.

The project would begin with a discovery phase to audit your last 3 months of support tickets and document your complete set of business rules for returns, exchanges, and warranties. Syntora maps every customer intent, from simple order status checks to complex multi-item return scenarios. This analysis forms the blueprint for the AI's logic.

The technical core would be a FastAPI service hosted on AWS Lambda. When a new ticket arrives in your help desk, a webhook sends the content to the service. The Claude API parses the customer's intent and extracts key information like an order number. The FastAPI application then orchestrates the workflow: it fetches order data from the Shopify API, applies your specific business logic, and executes the correct action, like calling the Shippo API to create a return label. All actions and decisions are logged to a Supabase database for tracking.

The delivered system integrates directly into your existing help desk. The AI would function as your first-response agent, autonomously resolving the majority of tickets in under 30 seconds. For the small fraction of issues requiring human judgment, the AI would summarize the case, list the steps already taken, and assign it to a human agent with full context. You receive the complete Python source code and a runbook for maintenance.