{"id":988375,"date":"2026-09-10T14:00:00","date_gmt":"2026-09-10T14:00:00","guid":{"rendered":"https:\/\/croncodestudio.com\/en\/?post_type=croncode_post&p=988375"},"modified":"2026-09-08T22:58:41","modified_gmt":"2026-09-08T22:58:41","slug":"yourstyle-ai-stylist-digital-wardrobe-guide","status":"publish","type":"croncode_post","link":"https:\/\/croncodestudio.com\/en\/journal\/yourstyle-ai-stylist-digital-wardrobe-guide\/","title":{"rendered":"YourStyle AI Stylist: A Digital Wardrobe Guide"},"content":{"rendered":"
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YourStyle is a Croncode consumer app that stores a photographed wardrobe and proposes outfits from that wardrobe. This guide explains the working loop: catalog, constrain, combine, save. It does not claim a trophy and it does not invent a user count.<\/p>\n

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YourStyle is a digital wardrobe plus an outfit combiner for clothes you photograph. Log wearable items, add occasion and weather, save looks you would actually wear; it is not a shop and not a human fitting.<\/p>\n<\/blockquote>\n

The product page is YourStyle, AI stylist from your closet<\/a>. The App Store listing is id6754201876<\/a>. Croncode LLC (Albuquerque) and Cron Creative Studio (Istanbul) ship it. Founder Goktug Unver<\/a> and co-founder Yusufhan Sokur<\/a> treat the closet as the source of truth. The studio is not affiliated with croncodetech.com. Other apps, including Numira<\/a>, stay on their own jobs.<\/p>\n

What should you put in the catalog first?<\/h2>\n
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  1. Shoes you walked in last week.<\/li>\n
  2. Bottoms that still button and sit well.<\/li>\n
  3. Tops you actually wore, including the boring ones.<\/li>\n
  4. One warm layer and one rain or wind layer if you use them.<\/li>\n
  5. Skip gifts, wrong sizes, and \u201csomeday\u201d event pieces until they are wearable.<\/li>\n<\/ol>\n

    Boring shirts are the engine. If you only log statement pieces, every suggestion looks like a costume. Photograph in daylight against a plain surface. A messy bed in the background is a privacy problem and a thumbnail problem.<\/p>\n

    Onboarding should stay small. We designed consumer first sessions around proof, not a lecture; see trust-first onboarding<\/a>. Twelve items are enough to learn the loop.<\/p>\n

    How does a look get built inside the product?<\/h2>\n\n\n\n\n\n\n\n\n
    Step<\/th>\nYou provide<\/th>\nThe app can use<\/th>\n<\/tr>\n<\/thead>\n
    Catalog<\/td>\nPhotos and simple tags<\/td>\nOnly those items<\/td>\n<\/tr>\n
    Constraint<\/td>\nOccasion, weather, walking<\/td>\nA smaller eligible set<\/td>\n<\/tr>\n
    Combine<\/td>\nA reject or accept<\/td>\nA saved look or a retry<\/td>\n<\/tr>\n
    Reuse<\/td>\nLaundry reality<\/td>\nLooks that still have available pieces<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    If a step is missing, the output gets theatrical. No weather means sandals in rain. No occasion means a satin shirt on a warehouse day. The model is infrastructure for combining, not a taste implant. That is the same studio position as AI as infrastructure<\/a>.<\/p>\n

    Personalization should stay on items you offered. If a closet tool starts inferring a lifestyle you did not type, it has crossed a line we describe in personalization without creepiness<\/a>.<\/p>\n

    What is YourStyle not for?<\/h2>\n