Improve Ecommerce Inventory Accuracy with Custom AI

Most small ecommerce businesses rely on Shopify's native inventory tracking. The system works for a single channel but breaks down when you sell on Amazon, Etsy, or through wholesale channels. The stock level in Shopify is often out of sync with what is physically in the warehouse or what is listed on other platforms. This requires manual updates multiple times a day.

Consider a store selling handmade candles with 50 SKUs. They use Shopify as their main site but also sell on Amazon FBA and at local markets using Shopify POS. During a flash sale, they sell 20 units of their most popular candle on Shopify. Simultaneously, Amazon sells 15 units. The warehouse team, using ShipStation, fulfills the Shopify orders first. By the time they see the Amazon orders, the physical stock is gone. They now have to cancel 15 Amazon orders, damaging their seller rating and creating a poor customer experience. ShipStation's inventory sync can lag by up to 15 minutes, which is too slow during a high-volume period.

To solve this, businesses often adopt an inventory management tool like Cin7 or Katana. These tools promise a central source of truth, but their integrations are often shallow. For example, they can sync a final stock number but cannot handle component inventory for assembled products. The candle business also sells kits with multiple components (wax, wicks, jars). If a jar is out of stock, Cin7 will not automatically mark all related candle kits as unavailable. This creates a hidden stockout, where an order is accepted for a product that cannot be fulfilled.

The structural problem is that these off-the-shelf tools are built for the most common use cases. They use polling-based updates, checking every 5-15 minutes, instead of event-driven webhooks because it is cheaper to build and support. They lack the custom logic to handle bundled products, component inventory, or supplier lead times specific to your business. They force your business process to fit their software, rather than building software to match your actual fulfillment workflow. This gap leads directly to overselling and manual reconciliation.

The first step is a full audit of your data flow. Syntora would map every system that touches inventory: your ecommerce platform (Shopify, BigCommerce), your warehouse management system (ShipStation, custom spreadsheet), supplier EDI feeds, and any other sales channels. This discovery phase produces a data flow diagram that shows exactly where stock levels are recorded, where they lag, and where the single source of truth should be. You receive this map before any code is written.

The core of the solution would be a custom forecasting model and a central inventory ledger built on Supabase. For forecasting, we would use a Python model like Prophet trained on at least 12 months of your sales data to predict demand per SKU. This model would run on a schedule using AWS Lambda. The central ledger, a PostgreSQL database in Supabase, would listen for sales events in real-time using webhooks from Shopify and other platforms. A FastAPI service would handle these webhooks, updating the ledger within 200 milliseconds of a sale.

The final system provides two key outputs. First, a dashboard showing current stock levels, forecasted demand for the next 30 days, and reorder point alerts. Second, an API that pushes the correct 'available to sell' quantity back to all your sales channels every 5 minutes. You own the complete system: the Python code for the model, the FastAPI application, and the Supabase database, all in your own accounts. The system eliminates manual inventory checks and prevents overselling during high-volume periods.