AI-powered facial analysis app that rates your looks across 10 categories and delivers a personalized glow-up plan. The most accurate facial analysis app on the App Store.
A startup from Raleigh, the United States that is founded by Evan G..
Facial Categories
10 distinct categories scored
Personalized Plan
Custom glow-up improvement plan
Privacy
No image storage on servers
Analysis Speed
Results in seconds
Consistent Analysis
Analysis is accurate and consistent
People serious about self-improvement and appearence optimization. Anyone curious about their facial aesthetics, tracking their glow-up progress, or exploring looksmaxxing for the first time. Mogged is built for users who want honest feedback and a clear path to improvement.
Mogged was built by Evan, an indie iOS developer and founder of Aurelian Syndicate L.L.C. After noticing a gap between basic face rating apps and genuinely useful aesthetic feedback, he built Mogged to deliver real, category-level analysis with actionable improvement plans, launched April 2026.
Mogged takes privacy seriously and never stores images on their servers. Users can delete the images, which are only stored on their phones, at any time in the app. Combined with analysis across 10 specific facial categories and a personalized improvement plan, Mogged gives you the most detailed and privacy -respecting facial assessment available on iOS.
Three reasons: - privacy - depth - actionability Mogged never stores images on their servers and user's can delete the images stored in the app, on their phone, at any point. It scores 10 distinct facial categories instead of giving you a single number. Instead of just a rating, you get a personalized glow-up plan with specific actionable improvement steps tailored to your results.
Swift, SwiftUI, Core ML, RevenueCat (subscription management), and Supabase (backend infrastructure).
I don't have verified, up-to-date information about Mogged (usemogged.com) to make a confident assessment of its quality, features, or reliability. I don't have specific data on this product's user reviews, pricing, or performance to provide an informed judgment.
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