CLUSTER FASHION-CLOTHING-PERSONALIZED · 7 ENTRIES · EARLIEST ENTRY 2025-06-17

Fashion try-on and outfit recommendation apps

This cluster consists of fashion shopping apps that provide personalized outfit recommendations, virtual try-on, and shoppable links. The dated history shows an isolated entry in February 2023, followed by a series of entries from June 2025 through April 2026, with the most entries in March 2026. Traction leaders by activity signal are ShopMy, blush-riot, and FitFi, all with zero stars.

Entry timeline

ENTRY TIMELINE

FEB 2023: 1JUN 2025: 1JUL 2025: 1OCT 2025: 1MAR 2026: 1APR 2026: 1OCT 2026: 1
FEB 2023OCT 2026

Members by activity

Activity signals, not revenue.

Build origin · 2 ai-built · 5 unlabelled

Operator · 2 hobby · 1 indie · 1 commercial · 3 unlabelled

Two labels per entry, never collapsed into one: how it was built, and who stands behind it. Counted over the 7 listed below. Unlabelled means nothing has decided yet — not that the answer is small.

  1. 01vuyo-store0★13 days—Fashion and lifestyle shopping app that lets users browse products, save favorites, and complete checkout with multiple payment options.
  2. 02FitFi0★2 mo—indieApp that gives personalized fashion and outfit recommendations based on user style preferences, with integrated payment support for purchases.
  3. 03ShopMy0★12 dayslivecommercialApp that connects shoppers with curated product recommendations from trusted tastemakers and lets creators earn commissions with shoppable links.
  4. 04Stylify0★4 mo—hobbyWeb app that recommends clothing outfits and visualizes them on a virtual avatar for shoppers.
  5. 05Lucky11★6 mostale—App that recommends outfits based on user's photo and personality, and shows where to buy them from online stores.
  6. 06tryon-chic-wishlist0★7 mostale—ai-builthobbyFashion shopping app that lets users save clothing to a wishlist and try on outfits virtually.
  7. 07StyleAI0★1 yrabandoned—ai-builtApp that lets shoppers virtually try on clothing and receive personalized fashion recommendations based on their style.