E-Commerce / Children's Fashion
A fit engine that cut returns by 28% — A kids fashion storefront with an AI right-fit and age recommendation engine built to attack the return rate directly.
Parents struggled to select exact clothing sizes and suitable fabrics for growing kids online. Size labels vary wildly between brands, children grow between order and delivery, and the result was a return rate that ate the category margin.
We built a custom AI-driven e-commerce web application in Python featuring an intelligent Right-Fit & Age Recommendation Engine. It combines the child's age, height and weight with garment measurements and historical return reasons per SKU to recommend a size and flag fabric suitability for the season.
Solved size confusion, reduced product returns by 28%, and created a smooth shopping journey from seller to buyer.
Inside The Build
Six of the capabilities that made the difference, and why each one was there.
Size recommendation from child measurements against per-SKU garment data and that SKU's historical return reasons.
Multiple children per account with growth tracked over time, so recommendations improve with each order.
Season and skin-sensitivity guidance surfaced at the point of choice rather than buried in the description.
Saved addresses, wallet and UPI support, and honest delivery estimates before the payment screen.
Reason-coded returns that feed straight back into the fit model as training signal.
Natural-language catalogue search that handles "warm outfit for a two year old" properly.
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