Lamoda Launches AI Virtual Wardrobe Built From Past Purchases
Lamoda has added a virtual wardrobe tool that pulls items from customers' purchase history and uses AI to assemble ready-made outfits and style suggestions.

Lamoda has launched a virtual wardrobe that helps customers build their own style, the company's press service said. The AI tool compiles items already bought on the marketplace and assembles ready-made outfits from the clothes a user owns plus recommendations from the catalog.
The new feature extends the company's "Г(ии)д по стилю" (roughly, "AI Style Guide") ecosystem for wardrobe management.
How it works
The "Virtual Wardrobe" tab sits in the user's personal account and has two sections: "My Items" and "Outfits". The first automatically gathers clothing, shoes and accessories the customer previously bought on the marketplace. There is no need to photograph anything, because the system loads items from purchase history. Outdated entries can be deleted manually.
In "Outfits", the system automatically creates ready-made fashion capsules based on what the user already owns and on personal recommendations from the site's AI assistant. Users can also assemble looks themselves, drawing on suggestions from the smart assistant and on items in their favorites. Combinations they like can be saved.
Why Lamoda built it
The company points to an internal customer survey. According to it, 62.8% of respondents say they sometimes have nothing to wear when getting ready, 54.4% would like to organize their wardrobe but don't know how, and 47% struggle to plan their wardrobe for a new season.
Lamoda introduced the "Г(ии)д по стилю" suite of digital tools on its platforms in early 2026. Besides the virtual wardrobe, users can already access a chatbot stylist, online try-on, photo-based product search and smart outfit selections.
A standalone online fitting room had already been running on the platforms in 2023. It recommended sizes for marketplace clothing after customers entered their gender, height, weight and age.
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