Dailyglowup — AI face rating and glow up routines doing ₹22.4L a month
Scan your face, get an AI rating, receive a personalized plan to maximize your looks. $26,690 last 30 days, $13,137 MRR, 1,055 active subscriptions, 4.8 stars, 51% month-on-month growth, 50% profit margin. Solo founder. SwiftUI only. Running TikTok and Meta ads at 2-3x ROAS. Listed for sale at $600,000.
₹22.4L
Last 30 days revenue (RevenueCat verified)
51%
Month-on-month growth rate
50%
Profit margin — 1,055 active subscriptions
4.8★
App Store rating from 1,171 reviews
01 / HOW IT WORKS
What the app actually does
Dailyglowup's insight is that everyone wants to improve how they look but most people don't know where to start. A face scan that gives you an honest rating and then tells you exactly what to do — skincare, grooming, fitness, style — each day is the structure people need. The AI isn't just judging you, it's building a roadmap.
1
User scans their face via the front camera
A short selfie scan captures facial features, skin texture, symmetry, and key attributes. No special lighting required — the AI is trained to work with real, everyday phone camera quality.
2
AI rates the face across key dimensions
Facial symmetry, skin clarity, jawline definition, eye area, grooming level — each scored with specific observations. Honest, not brutal. The rating is the hook that drives the paywall conversion.
3
A personalized daily glow up plan is generated
Based on the rating, the AI builds a custom plan: morning skincare routine, diet tweaks, grooming habits, exercise recommendations, and style adjustments — specific to what the face scan actually revealed.
4
Daily check-ins track progress over time
Users re-scan weekly and see before/after comparisons of their actual face as the routine takes effect — this is what drives retention. Progress is the product.
02 / INDIA POTENTIAL
Does this work as an India play
India's grooming and personal care market is one of the fastest growing in Asia — men's grooming alone is a ₹13,000Cr+ market. But the glow up / looksmaxxing content trend on Instagram Reels has barely been tapped by an Indian-first product. Generic global apps give Western skin and grooming advice to Indian users.
Indian skin
Indian skin types (Fitzpatrick IV-VI) need fundamentally different skincare routines than the Western default — most global glow up apps give advice built for fair, dry Western skin. An India-trained model is a genuine moat.
Pre-wedding
Indian weddings — the "groom glow up" and "bride glow up" before a wedding is a massive search category on Instagram. A 90-day pre-wedding glow up plan is a high-urgency, high-willingness-to-pay product.
₹99/wk
Realistic India-priced entry tier — weekly subscription matches how Indian users prefer to pay (low commitment, low upfront) and the original does well at weekly pricing too.
Ayurveda
Ayurvedic skincare and grooming routines as a specific plan type — backed by dosha analysis, Indian herbs (neem, turmeric, ashwagandha) and culturally familiar practices no global app recommends.
03 / THE WEEKEND BUILD
Friday to Sunday, hour by hour
The original is iOS-only (SwiftUI). Build cross-platform with React Native for v1 to hit Android immediately — Indian users are overwhelmingly Android. Scope to face scan, AI rating, and a 7-day starter routine. The Ayurvedic routine mode is the India launch differentiator.
Friday
Evening · 3 hrs
7–8 PM Set up a React Native (Expo) project with camera permissions. Build the face capture screen — front camera, detect a face in frame, auto-capture on alignment.
8–9 PM Integrate a face analysis API (AWS Rekognition or a Vision API) to extract facial attributes: symmetry, skin texture estimates, key landmarks. Map these to a 1-10 rating across 5 dimensions.
9–10 PM Build the rating reveal screen — animated score reveal, breakdown by dimension, honest assessment copy. Gate the full breakdown behind the paywall.
Saturday
Full day · 7 hrs
Morning Build the AI plan generator — take the face rating inputs, send to Gemini API, receive a personalized 30-day glow up plan: morning routine, evening routine, diet tweaks, grooming habits, exercise.
Afternoon Build the daily check-in system — each day shows 3-5 tasks from the plan. Users mark them done. Streaks tracked in Supabase. Push notification reminder each morning.
Evening Add the Ayurvedic routine mode — a special plan variant that incorporates neem, turmeric, ashwagandha, and Ayurvedic practices. Indian skin types (Fitzpatrick IV-VI) handled specifically.
Sunday
5 hrs
Morning Build the progress tracker — weekly re-scan compares new face score to baseline. Before/after side-by-side. This is the retention mechanic.
Afternoon Implement Razorpay for Indian users (Rs99/week or Rs299/month) and RevenueCat for App Store/Play Store billing. RevenueCat handles everything else globally.
Evening Submit to Play Store first via Expo EAS Build. Record the face scan → rating reveal → plan generation flow for your launch reel.
04 / APP STACK
What you're actually building with
Rn
React Native (Expo)
App framework
Cross-platform iOS and Android — Android first for India, unlike the original which is iOS-only SwiftUI.
Fa
AWS Rekognition or Vision API
Face analysis
Extracts facial attributes, symmetry scores, skin texture signals — the raw inputs for the AI rating.
AI
Gemini API
Plan generation
Takes the face rating dimensions and generates a personalized, India-aware daily glow up routine.
Sb
Supabase
Auth + database
Stores user profiles, daily check-ins, streak data, and weekly scan history for progress tracking.
Rc
RevenueCat
Subscription management
Handles App Store and Play Store in-app subscriptions across iOS and Android from one integration.
Rz
Razorpay
India payments
Direct UPI/card payments for Indian users who prefer to pay outside the app stores.
05 / WHERE & HOW TO DEPLOY
Going live
Where: iOS App Store and Google Play Store via Expo EAS Build. Supabase for the database. AWS Rekognition or Google Vision API for face analysis (hosted, no ML infrastructure to manage). RevenueCat for cross-platform subscription management.
Submit to Play Store first — Indian users are overwhelmingly Android, and the original is iOS-only, meaning Android is an uncontested market for this concept.
Run: eas build --platform all to build iOS and Android simultaneously.
Test the face scan on multiple Indian skin tones specifically — the face analysis API must perform accurately on Fitzpatrick IV-VI skin types.
Record the rating reveal animation on a real device for your launch reel — the moment the score appears is the hook.
06 / MARKETING & REVENUE
Getting paying users
How to market it
Post the face scan → rating reveal moment as a reel — people cannot resist watching an AI rate someone's face. The curiosity is the entire hook.
Run 3-5 reels/day targeting different audiences: gym-going men (jawline, fitness), college women (skincare, makeup complement), groom/bride pre-wedding glow up.
The Ayurvedic routine mode is a dedicated reel: "AI built me a 30-day glow up plan using Ayurveda based on my face scan" — this pulls a wellness audience no Western app can reach.
The 90-day pre-wedding glow up plan is the highest-value content angle: "I started this 3 months before my wedding, here is week 8" — before/after format, huge Indian audience.
TikTok and Meta ads at 2-3x ROAS proven by the original — the creative format (face scan reveal) is already validated. Replicate the ad structure with Indian faces and Indian skin contexts.
Who pays, and why
Indian men 18-30 getting into fitness and grooming who want specific, data-driven guidance on what to improve beyond just going to the gym.
Indian women 18-28 building a consistent skincare routine who want AI-personalised advice for Indian skin types rather than generic Western recommendations.
Brides and grooms 3-6 months before their wedding looking for a structured, daily plan to look their best — highest urgency, highest willingness to pay.
Scenario
Paying users/mo
Revenue/mo
Slow start
500 paid × Rs99/wk avg
Rs49,500/mo
Viral reel + wedding content angle
5,000 paid × Rs99/wk avg
Rs4,95,000/mo
3-5 reels/day + TikTok/Meta ads at 2-3x ROAS
25,000 paid × Rs99/wk avg
Rs24,75,000/mo
07 / START BUILDING
Paste this into Claude or GPT
This prompt sets up the full build context so the AI scopes, plans, and starts coding the project with you from message one.
BUILD_PROMPT.txt
I want to build an AI face rating and glow up routines mobile app for the Indian market, inspired by Dailyglowup: Maximize Looks (which does Rs22.4L/month, 51% MoM growth, 50% profit margin), scoped to ship a working ver
sion in a single weekend.
Core flow:
1. User opens the app and scans their face using the front camera. The app auto-captures when a face is in frame.
2. An AI face analysis API (AWS Rekognition or Google Vision API) extracts facial attributes — symmetry, skin texture, key landmarks — and maps them to a 1-10 rating across 5 dimensions: facial symmetry, skin clarity, jawline definition, grooming level, eye area.
3. The rating reveal screen shows the overall score with an animated reveal. Detailed dimension breakdown is gated behind a subscription paywall.
4. Gemini API takes the face rating dimensions and generates a personalized 30-day glow up plan: morning skincare routine, evening routine, diet changes, grooming habits, exercise recommendations — all specific to what the face scan revealed.
5. Daily check-in: 3-5 tasks each day from the plan. User marks them done. Streaks tracked in Supabase. Morning push notification reminder.
6. Ayurvedic routine mode: a special plan variant incorporating neem, turmeric, ashwagandha, and Ayurvedic practices for Indian skin types (Fitzpatrick IV-VI specifically).
7. Weekly re-scan tracks progress — before/after face score comparison. This is the retention mechanic.
8. Pricing: Rs99/week or Rs299/month via RevenueCat (App Store/Play Store) and Razorpay for direct Indian UPI payments.
Stack I want to use: React Native with Expo (cross-platform iOS and Android — Android first, unlike the original which is iOS SwiftUI only), AWS Rekognition or Google Vision API for face analysis, Gemini API for personalized plan generation, Supabase for auth and database, RevenueCat for cross-platform subscription management, Razorpay for Indian UPI payments, Expo EAS Build for app store submission.
Help me, step by step, one question at a time:
1. Help me evaluate AWS Rekognition vs Google Vision API for face attribute extraction — which gives more useful signals for generating a meaningful rating?
2. Build the face capture screen with auto-capture on face alignment.
3. Build the face analysis API call and the rating calculation from raw facial attributes.
4. Build the animated rating reveal screen and the gated dimension breakdown.
5. Build the Gemini plan generation call with India-specific context (Indian skin types, Ayurvedic ingredients, Indian climate considerations).
6. Build the daily check-in system with streaks and push notifications.
7. Build the weekly re-scan progress comparison.
8. Implement RevenueCat for subscriptions and Razorpay for Indian payments.
9. Walk me through Expo EAS Build submission to both stores, Play Store first.
Keep explanations short and India-context aware. Android first. Test face analysis accuracy specifically on Fitzpatrick IV-VI skin tones before committing to any API. Ayurvedic routine mode as a first-class feature. The face rating reveal animation is the most important UX moment — make it memorable. Push me to ship the smallest working version first. If I get stuck, tell me to ask @buildwithkanhaa.
Unlock the full build prompt
Enter your email — takes 5 seconds. Get new guides early.
Please enter a valid email.
I want to build an AI face rating and glow up routines mobile app for the Indian market, inspired by Dailyglowup: Maximize Looks (which does Rs22.4L/month, 51% MoM growth, 50% profit margin), scoped to ship a working version in a single weekend.
Core flow:
1. User opens the app and scans their face using the front camera. The app auto-captures when a face is in frame.
2. An AI face analysis API (AWS Rekognition or Google Vision API) extracts facial attributes — symmetry, skin texture, key landmarks — and maps them to a 1-10 rating across 5 dimensions: facial symmetry, skin clarity, jawline definition, grooming level, eye area.
3. The rating reveal screen shows the overall score with an animated reveal. Detailed dimension breakdown is gated behind a subscription paywall.
4. Gemini API takes the face rating dimensions and generates a personalized 30-day glow up plan: morning skincare routine, evening routine, diet changes, grooming habits, exercise recommendations — all specific to what the face scan revealed.
5. Daily check-in: 3-5 tasks each day from the plan. User marks them done. Streaks tracked in Supabase. Morning push notification reminder.
6. Ayurvedic routine mode: a special plan variant incorporating neem, turmeric, ashwagandha, and Ayurvedic practices for Indian skin types (Fitzpatrick IV-VI specifically).
7. Weekly re-scan tracks progress — before/after face score comparison. This is the retention mechanic.
8. Pricing: Rs99/week or Rs299/month via RevenueCat (App Store/Play Store) and Razorpay for direct Indian UPI payments.
Stack I want to use: React Native with Expo (cross-platform iOS and Android — Android first, unlike the original which is iOS SwiftUI only), AWS Rekognition or Google Vision API for face analysis, Gemini API for personalized plan generation, Supabase for auth and database, RevenueCat for cross-platform subscription management, Razorpay for Indian UPI payments, Expo EAS Build for app store submission.
Help me, step by step, one question at a time:
1. Help me evaluate AWS Rekognition vs Google Vision API for face attribute extraction — which gives more useful signals for generating a meaningful rating?
2. Build the face capture screen with auto-capture on face alignment.
3. Build the face analysis API call and the rating calculation from raw facial attributes.
4. Build the animated rating reveal screen and the gated dimension breakdown.
5. Build the Gemini plan generation call with India-specific context (Indian skin types, Ayurvedic ingredients, Indian climate considerations).
6. Build the daily check-in system with streaks and push notifications.
7. Build the weekly re-scan progress comparison.
8. Implement RevenueCat for subscriptions and Razorpay for Indian payments.
9. Walk me through Expo EAS Build submission to both stores, Play Store first.
Keep explanations short and India-context aware. Android first. Test face analysis accuracy specifically on Fitzpatrick IV-VI skin tones before committing to any API. Ayurvedic routine mode as a first-class feature. The face rating reveal animation is the most important UX moment — make it memorable. Push me to ship the smallest working version first. If I get stuck, tell me to ask @buildwithkanhaa.
Start building this weekend
Send me a screenshot of what you ship — it might be the next reel.