AI product intelligence for fashion
Fomofy's fashion discovery was noise. Generic feeds ignored fit, body type and taste, and manual photography could not scale with the catalogue.
Fashion discovery was noise. Generic feeds ignored size, body type and taste, static images did not reflect real fit, and size charts confused more than they helped.
Colour mismatches drove returns, and manual product photography could not scale with the catalogue.
Virtual try-on: a shopper uploads a photo and the garment is overlaid on their body in real time, across tops, bottoms, dresses and outerwear.
Skin-tone colour intelligence: detects skin tone and undertone, maps it to a curated palette, and flags which product colours complement or clash, powering a “best for you” filter.
Outfit creator (in progress): takes destination, dates and occasion, pulls weather and local dress norms, filters by skin tone and live inventory, and returns a packing-ready look-book.
Discovery that fits the shopper, not the average.
Return rates down, add-to-cart conversion up.
Colour-related returns reduced.
Less dependence on physical model photography.
Broader size and body-type inclusivity.
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HSR LAYOUT, BENGALURU