Forecast demand, understand what's driving it, and identify what needs your attention before it becomes a business problem.
Upload your sales data. Generate explainable forecasts across products and stores.
Demand changes with seasonality, promotions, pricing, products, and customer behavior. Traditional reports tell you what happened. They don't tell you what is likely to happen next—or why.
Demand changes faster than static reports can capture.
A forecast is more useful when you understand what drove it.
Thousands of rows of sales data can hide the signals that matter.
Bring your historical sales data into the platform.
Automatically uncover trends, seasonality, data quality issues, and business patterns.
Generate demand forecasts for 1, 7, 14, and 30-day horizons.
Understand the factors driving every prediction.
Identify emerging demand changes, risks, and opportunities.
Predict future demand across stores and products using time-series machine learning.
Understand which factors are increasing or decreasing each prediction.
Compare store performance and identify emerging demand patterns.
Discover high-growth, declining, and high-demand products.
Evaluate forecasting performance across multiple horizons.
Ask questions about your data and get business-focused answers.
Every prediction can be analyzed through its underlying drivers, giving you a clearer path from prediction to decision.
Explore ExplainabilityDemand is expected to increase significantly.
Recent demand is trending downward.
Projected demand is approaching available inventory.
Ask questions about your demand, products, stores, and forecasts in natural language.
Meet the AI Demand AnalystModels are evaluated using chronological walk-forward validation rather than random train/test splits.
| Forecast Horizon | Model | CV WAPE | Test WAPE |
|---|---|---|---|
| 1 Day | CatBoost | 11.83% | 10.46% |
| 7 Days | CatBoost | 11.27% | 10.13% |
| 14 Days | CatBoost | 11.42% | 10.17% |
| 30 Days | CatBoost | 11.98% | 11.61% |
* Lower WAPE indicates lower forecast error.
Walk-forward evaluation designed for forecasting data.
Multiple baselines and machine-learning models are evaluated before selection.
Optuna-based hyperparameter optimization.
SHAP-based prediction analysis.
MLflow experiment tracking and model registry.
FastAPI + Docker with production model loading.
A complete machine-learning pipeline turns historical sales data into continuously usable demand intelligence.
Upload your historical sales data and discover what demand is likely to do next.