Custom AI Inventory Forecasting for Your Online Store

Most growing stores rely on their ecommerce platform's built-in tools or an inventory management app. Shopify's tracking is just a counter; it tells you what you have, not what you will need. Apps like Stocky or SkuVault improve on this with forecasting, but their logic is based on simple moving averages. These tools cannot account for seasonality, promotional lifts, or trends in product combinations because they treat every SKU independently.

Consider an apparel store preparing for the holiday season. They ran a successful Black Friday promotion last year on a specific coat. A simple moving average model will see that sales spike and recommend a huge reorder for this November. That model misses the context: the spike was driven by a 40% discount and a paid ad campaign that will not be repeated. The store ends up with 500 extra coats in January that must be liquidated at a loss.

The structural problem is that off-the-shelf inventory apps are designed for single-variable analysis. They calculate 'sales velocity' but cannot model the relationship between a marketing campaign and a specific SKU's demand. They cannot see that when your top-selling t-shirt goes out of stock, sales for a similar shirt increase by 15%. This requires a system that can analyze relationships across dozens of features like price, ad spend, and inventory levels of related products for every SKU simultaneously. These apps offer rules, not intelligence.

The result is capital tied up in overstocked items and lost sales from stockouts on bestsellers. A growing store with 500+ SKUs cannot manage this risk in Google Sheets. The time spent manually updating reorder points is time not spent on marketing or product development.

The project would start with a data audit of your sales history. Syntora would connect to your Shopify or BigCommerce API to pull at least 12 months of order data, product information, and inventory levels. The goal is to identify predictive features like seasonality, price changes, and historical promotion impacts. You receive a data quality report and a list of the top 20 predictive features to be used in the model.

The core of the system is a forecasting model using a gradient boosting library like LightGBM for its ability to handle multiple features. This model would be wrapped in a FastAPI service and deployed on AWS Lambda for cost-effective, on-demand processing. A daily scheduled job would pull fresh sales data, retrain the model on a 90-day rolling window, and generate updated 30-day and 60-day demand forecasts for every SKU.

The delivered system is integrated into your workflow. It would write reorder recommendations (e.g., 'Order 75 units of SKU-123 by Oct 15') into a Supabase database, accessible via a simple web dashboard built with Vercel. This dashboard shows the forecast, current stock, and confidence intervals. You get the full Python source code and an API endpoint to integrate forecasts into other tools, with hosting costs under $50/month.