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SOLUTIONS · RETAIL

Retail ML, solved end-to-end

Real commerce problems worked start to finish on the tables you already have — orders, products, customers and reviews. Each solution ships a live model and a shareable dashboard, with your data isolated to your account.

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Case studies

Solutions your teams can ship

Each one is a working example — the problem, the trained model, the questions you can ask it in plain English, and the dashboard. Filter by product track or ML task.

Track

ML task

Data Science Multi-class classification

Predict a review's star rating from its text

Score every product review 1–5 from its text and verified-purchase flag, so a moderation queue can be ordered by where the predicted rating and the given rating disagree. A worked example of why accuracy is the wrong metric when 63.8% of your labels are the same label.

A predicted 1–5 rating for every review — and a way to rank the queue by disagreement.

Aug 13, 2026 · 9 min read Read the solution →

On the roadmap

More retail solutions in progress

Analytics + Science Regression
Coming soon

Demand forecasting

Forecast units per SKU per location from order history, price, promotion and seasonality — so buying is planned rather than reactive.

Data Science Binary classification
Coming soon

Stockout prediction

Flag which SKU-location pairs are heading for a stockout in the next replenishment window, while there is still time to move stock.

Data Science Binary classification
Coming soon

Customer churn and repeat purchase

Score which customers are unlikely to order again, from order cadence, basket mix and support history.

Data Science Link prediction
Coming soon

Product recommendation

Predict the customer–product edges that have not happened yet, learning from the purchase graph rather than a hand-tuned similarity score.

Want one of these worked on your data first? Tell us what you're solving →

Point it at your own catalogue

Connect your orders, products and reviews, pick the column you want predicted, and train — no feature engineering and no infrastructure to run.

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