Stop Paying Per-Seat Fees for Off-the-Shelf AI

Many businesses start with a SaaS tool like Inventory Planner. It connects to Shopify and provides basic reorder points. But these tools use simple models like exponential smoothing. They cannot incorporate external data, like a planned marketing campaign or a competitor's stockout, which dramatically affects demand. The result is a forecast that reacts to the past but cannot predict the future.

Consider a 20-person e-commerce business selling seasonal goods. They tried Netsuite's demand planning module but found the per-seat costs were designed for 500-person companies. More importantly, the system is a black box. When it suggested ordering 5,000 units of a winter coat in August, there was no way to inspect the model's logic or understand its assumptions. The team could not trust a recommendation they could not explain.

The fallback is always a massive Google Sheet. The ops lead spends two days every month exporting Shopify sales, Google Analytics traffic, and Klaviyo email data. The spreadsheet has dozens of VLOOKUPs and pivot tables that break if a column name changes. A single copy-paste error can lead to a $50,000 ordering mistake. This manual process does not scale past 100 SKUs and is completely dependent on one person.

Syntora's approach to building a custom demand forecasting system would begin with a thorough discovery phase. This would involve auditing your existing data landscape, including e-commerce platforms like Shopify, analytics tools such as Google Analytics, and marketing automation systems like Klaviyo. We would identify relevant data points for sales, page views, and campaign performance that can be extracted via their respective APIs.

For data ingestion, we would engineer robust Python scripts deployed on a serverless platform like AWS Lambda. These scripts would pull historical data (e.g., the last 24 months of daily sales) and regularly refresh it, loading it into a scalable database such as Supabase Postgres. Data cleaning, transformation, and feature engineering would be managed using dbt (data build tool). This process would involve joining disparate sources and constructing a comprehensive feature set for each SKU, potentially including over 70 features derived from sales, product attributes, and marketing activities.

The core of the system would be the predictive models. We would likely develop a multi-model ensemble approach, potentially utilizing a time-series model like Prophet for baseline forecasting and a gradient boosting model such as LightGBM to capture the impact of various covariates. The LightGBM model would be trained to learn intricate patterns, for example, how specific promotional emails or seasonal events influence product sales. Model validation would involve backtesting on historical data, establishing target metrics like Mean Absolute Percentage Error (MAPE) to ensure predictive accuracy meets business requirements.

Once validated, the trained model would be serialized and packaged into a Docker container. This container would be deployed as a highly performant API service using FastAPI on a serverless infrastructure like AWS Lambda, allowing for efficient scaling and execution. A scheduled job would trigger this service daily or as needed, enabling it to pull the latest data, generate updated forecasts (e.g., a 90-day outlook for all active SKUs), and write the predictions back into the Supabase database.

A key deliverable would be a user-friendly dashboard, potentially built with Streamlit, providing visibility into the forecast, actual performance, model accuracy metrics, and insights into the most impactful features driving predictions. This transparency is crucial for business users to trust and utilize the system. We would also implement monitoring and alerting for model performance, such as PagerDuty integration for significant deviations in accuracy. Throughout the engagement, Syntora would provide clear documentation and knowledge transfer to your team, ensuring long-term maintainability and understanding of the deployed system. The client would be responsible for providing API access credentials, business context, and feedback on model performance.