Enhance Ecommerce Operations with Custom AI

Many online stores rely on built-in automation like Shopify Flow. These tools are useful for simple, rule-based tasks like tagging a customer after a purchase. They fail when a task requires context or understanding. A Shopify Flow cannot read a customer review, understand the customer is complaining about shipping time, and automatically tag the order for follow-up by the logistics team. That critical connection requires manual work.

In customer support, platforms like Gorgias or Zendesk use macros for common questions. This works for "where is my order?" but breaks down with nuanced requests. Consider a customer message: "My last order of the Ethiopian coffee was too acidic. I prefer something full-bodied with chocolate notes. What should I try next?" A macro cannot answer this. A support agent must read the ticket, understand the flavor profile, search the Shopify product catalog, and manually write a personalized recommendation. This takes 5-10 minutes of a skilled person's time.

Off-the-shelf recommendation apps from the Shopify App Store offer another partial solution. They typically use simple logic like "customers who bought X also bought Y." They cannot incorporate your specific business rules, such as "don't recommend low-margin items" or "push this new product we're overstocked on." The core issue is that your tools are siloed. Your helpdesk, your ecommerce platform, and your review software do not talk to each other in a meaningful way. The store owner or a senior employee becomes the slow, expensive human API connecting them.

The first step would be a data audit. Syntora would connect to your ecommerce platform (Shopify, BigCommerce) and helpdesk (Gorgias, Zendesk) APIs to map your current data flow. The audit identifies the most valuable points for automation, assesses data quality, and establishes a baseline for performance. You would receive a brief technical plan outlining a specific workflow, like support ticket analysis, with a clear timeline before any build begins.

The technical approach for support automation would involve a FastAPI service that listens for new ticket webhooks from your helpdesk. When a ticket arrives, the service sends the text to the Claude API for intent extraction and entity recognition. The Claude API returns structured data identifying the customer's need (e.g., intent: 'product_recommendation', entity: 'flavor_profile: full-bodied'). This data then powers a query against a Supabase database containing your product catalog, returning the best match. The system is built with Python for its robust data handling libraries.

The delivered system integrates directly into your existing tools. For a support agent, the AI's recommendation would appear as a private note inside the Gorgias ticket, ready to be copied or edited. The entire process takes less than 5 seconds. The code runs on AWS Lambda, keeping hosting costs low, typically under $20/month. You receive the full source code, a deployment runbook, and control over the entire system.