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AI Mobile App
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Purely Scan

A React Native mobile app that scans packaged food and care product ingredient lists using AI, surfacing positive and negative effects, recommended consumption, and a health rating — with user login and subscription plans.

Team Size

3 - 5

professionals

Duration

2 - 3 Months

development time

Budget

$3K - $5K

total cost in USD

Category

AI Mobile App

specialization

Technology Stack

React Native
TypeScript
Next.js
MongoDB
OpenAI API
Cloudflare R2
Razorpay
PayPal

Project Links

Project Overview

Purely Scan is a React Native mobile app that helps users instantly understand what's inside packaged food and personal care products. By scanning a product's ingredients list with the camera, logged-in users get an AI-generated health rating, the ingredient's positive and negative effects, and recommended consumption or usage guidance.

Challenge

Consumers often struggle to interpret complex ingredient lists on food packets and personal care products. The challenge was to simplify this process on mobile with fast in-app scanning, accurate AI classification, proper user accounts, and a subscription model that gives the app a sustainable, recurring revenue base instead of one-off usage.

Solution

We built a React Native and TypeScript app backed by a Next.js and MongoDB API. Users log in, then scan a product's ingredient label directly from the camera; the image is processed via OCR and sent to AI models (like GPT-4), returning a health rating, positive/negative effects, and recommended consumption or usage per ingredient. Subscription plans are managed in-app with Razorpay and PayPal handling billing, and Cloudflare R2 stores scan images reliably at scale.

Results & Impact

In-app camera scanning of ingredient labels with instant AI health ratings
Positive/negative effect breakdown and recommended consumption per scan
Cross-category support: food, skincare, haircare, makeup, etc.
User login with subscription plans billed via Razorpay and PayPal
Seamless AI pipeline: OCR → AI Analysis → User Feedback in seconds
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