Stop Guessing. Start Predicting Perishable Goods Demand.

Most small businesses start with spreadsheets. The owner builds a sheet to track daily sales and uses a simple moving average to guess the next day's production. This breaks down when seasonality hits, a local event drives foot traffic, or a key employee goes on vacation. The logic lives in one person's head and is prone to human error.

Off-the-shelf inventory tools in POS systems like Square or Toast offer basic forecasting, but they are reactive. They use historical data to project forward, often with simple exponential smoothing. This cannot account for external factors. For a cafe selling pastries, a 3-day heatwave can crush sales, but the POS model based on last week's mild weather will recommend the same production run. The result is 70 unsold croissants in the trash.

These backwards-looking methods fail because they cannot model the complex relationships between sales, weather, holidays, and promotions. They treat every variable in isolation. To accurately predict demand for perishable items, you need a model that weighs dozens of these features simultaneously and understands their combined impact.

Syntora would approach demand forecasting by first conducting a discovery phase to understand your current data landscape and business processes. This would involve identifying key data sources like your Point-of-Sale (POS) system APIs (e.g., Toast, Square, Lightspeed) and assessing their data quality. We would then work with your team to collect a minimum of 12-18 months of historical transaction-level data.

The data engineering phase would involve extracting this historical data, enriching it with relevant external sources such as weather forecasts from the OpenWeatherMap API or local event calendars, and then meticulously cleaning and processing it using tools like Pandas. This would prepare a comprehensive feature set for model training.

For the core prediction, Syntora would train a gradient boosting model, typically using LightGBM. This machine learning approach is chosen for its ability to capture complex, non-linear interactions within your sales data, such as how specific days of the week or weather patterns influence demand. The model would be trained on historical data and rigorously validated against recent periods to ensure robust predictive performance.

The deployed system would expose daily forecasts via a FastAPI service, hosted on a serverless architecture like AWS Lambda. A CloudWatch event would typically trigger the forecasting process each night, generating production numbers for your critical SKUs. This serverless design aims for both efficiency and cost-effectiveness in operations.

Post-deployment, every forecast and corresponding actual sales data would be logged into a Supabase database. This would feed a custom dashboard, potentially built with Vercel, to monitor model accuracy over time. We would also implement an alerting system, such as a Slack notification, to signal when model performance dips below a predefined threshold, indicating a need for retraining or recalibration.

A typical engagement for this complexity, assuming clean data access, would range from 8 to 12 weeks for initial build and deployment. Your team would need to provide API access to sales data and subject matter expertise on operational nuances. Deliverables would include the deployed forecasting system, source code, documentation, and a monitoring dashboard.