Get SKU-Level Demand Forecasts Built for Your Data

Most online shops start with Shopify's built-in analytics or a basic inventory app. These tools show past performance but cannot reliably predict future sales. A Shopify app like Stocky relies on simple moving averages, which fails for seasonal items and causes stores to overstock on winter-themed products in March.

A common next step is exporting data to Google Sheets and using the FORECAST formula. This is a basic linear regression that cannot handle multiple variables, like an upcoming email promotion or a price change. It fails to capture seasonality and becomes a full day of manual, error-prone work for an operations manager to update for a few dozen SKUs.

These off-the-shelf approaches fundamentally cannot work because they treat every product identically and ignore external factors. They can't learn that a specific marketing campaign drives a 3-day sales spike for one category but has no effect on another. A 20% forecasting error on a single high-volume SKU can tie up thousands in cash and lead to costly stockouts.

Syntora's approach to building a custom AI sales forecasting model would begin with a data discovery and integration phase. We would audit your existing data sources, such as Shopify store APIs, Google Analytics, and Klaviyo, to understand data availability and quality. Our engineering team would then develop Python scripts, leveraging libraries like Polars, to unify these diverse sources into a clean, comprehensive time-series dataset. This process involves engineering a robust feature set per SKU, per day, incorporating variables like price history and marketing activity. We have built similar document processing pipelines using Claude API for financial documents, and the same robust data engineering patterns apply here.

Once the data is prepared, we would design and train a LightGBM gradient boosting model. This model architecture is chosen for its ability to capture complex, non-linear patterns in sales data, such as how price changes or promotional events impact demand differently depending on the day of the week. The model would be developed to predict future sales quantities for active SKUs, typically looking out 30, 60, and 90 days.

The trained model would then be packaged into a container and deployed as a serverless function, for example, on AWS Lambda, accessible via a FastAPI endpoint. A scheduled task, such as a cron job, would orchestrate daily updates: pulling the latest sales data, re-generating forecasts, and writing the updated predictions to a designated output, such as a Google Sheet or a Supabase database via its REST API. This ensures your operations team receives fresh forecasts regularly without manual intervention.

As part of the engagement, Syntora would deliver the fully configured data pipelines and the deployed forecasting system. We would also provide the client with a basic Streamlit dashboard for monitoring key model performance indicators and a mechanism for alerts if forecast accuracy deviates significantly, indicating a need for model adjustments. The client would need to provide access to their data sources and collaborate on defining relevant business rules and integration points. A typical build timeline for a system of this complexity, assuming clean data, is approximately 8-12 weeks.