Tailor Me! Virtual Wardrobe Mobile Application

Tailor Me! – Developer Brief Overview Tailor Me! is a mobile-first app designed to organize users’ wardrobes, help them build outfits, and share fashion inspirations within a community. It integrates sustainability through resale platforms and partnerships with brands while fostering a social network where users can interact with and draw inspiration from each other. The app will combine features of a wardrobe organizer, outfit builder, and fashion-centric social platform. Below are the key components, features, and technical considerations to guide development. Core Features 1. Virtual Closet • Functionality: • Users upload photos of their clothing. • AI categorizes clothing into types (e.g., tops, bottoms, accessories). • Users can tag and categorize items (e.g., seasonality, color, brand). • Advanced filters for searching (e.g., “Show winter coats”). • Tech Considerations: • Use machine learning (ML) models for image recognition and categorization. • Cloud storage for photos, possibly leveraging AWS S3 or Firebase Storage. 2. Outfit Builder • Functionality: • Drag-and-drop interface for creating outfits. • AI-powered recommendations for missing pieces (e.g., “Add sneakers to complete this look”). • Save outfits for future reference. • Tech Considerations: • Build drag-and-drop UI with frameworks like Flutter or React Native. • Backend logic to suggest outfits based on wardrobe data (e.g., TensorFlow for AI). 3. Social Feed and Interactions • Functionality: • Live Feed: Users can post photos of their outfits or individual clothing items. • Likes and Comments: Others can like and comment on posts. • Follow/Followers System: Users can follow other wardrobes for inspiration. • Ability to share outfits posted by others. • Tech Considerations: • Backend: Real-time database for feeds (e.g., Firebase Realtime Database or MongoDB Atlas). • UI: Implement an infinite scroll feature for the feed. • Social graph for followers/following relationships (e.g., Neo4j or GraphQL for managing connections). 4. Resale Platform Integration • Functionality: • Users can list items directly from their closet to platforms like Depop or eBay. • Tailor Me! pulls market trends (e.g., pricing suggestions) from APIs. • Notifications for items with high resale demand. • Tech Considerations: • API integrations with Depop and eBay (OAuth for user authentication). • Real-time notifications via services like Firebase Cloud Messaging (FCM). 5. Brand Partnerships and Recommendations • Functionality: • Sponsored ads in the closet or feed (e.g., “Shop Similar” suggestions). • Tailored outfit recommendations based on wardrobe gaps and preferences. • Exclusive discounts and promotions from partner brands. • Tech Considerations: • Use APIs to display sponsored content dynamically. • Machine learning for personalized recommendations based on user data. Additional Features 1. Weather Integration: • Outfit suggestions based on real-time weather data via an API like OpenWeatherMap. 2. Event-Based Outfits: • Users input upcoming events (e.g., “formal dinner”), and the app suggests outfits. 3. Inspiration Board: • Users save outfits they like from others or upload imported images for reference. Tech Stack Recommendations Frontend: • Framework: React Native or Flutter (cross-platform for iOS and Android). • UI Libraries: TailwindCSS, Material Design for React Native. Backend: • API Management: Node.js with Express or Python Flask/Django. • Database: • NoSQL (MongoDB) for user data and posts. • Relational DB (PostgreSQL) for managing structured data like wardrobe items. • Authentication: OAuth 2.0 or Firebase Authentication for login (social sign-ins like Google/Apple). AI and Machine Learning: • Image Recognition: TensorFlow Lite or AWS Rekognition for clothing categorization. • Recommendation System: TensorFlow or custom ML models for outfit suggestions. Cloud and Hosting: • Cloud Storage: Firebase Storage or AWS S3 for wardrobe photos. • Server Hosting: AWS EC2, Google Cloud, or Azure. Social Features: • Real-time updates via Firebase Realtime Database or Socket.io. • Push notifications with Firebase Cloud Messaging. Development Milestones 1. MVP Development (6-8 Months): 1. Virtual Closet: • User profile setup, photo uploads, and categorization. 2. Outfit Builder: • Drag-and-drop functionality with manual item selection. 3. Live Feed: • Users can post, like, and comment. 2. Advanced Features (8-12 Months): 1. AI Recommendations: • Outfit suggestions, brand ads, and resale demand notifications. 2. Social Features: • Follower system, shared posts, and community boards. 3. Resale Integration: • APIs for Depop/eBay. 3. Scalability (12+ Months): • Partnerships with major brands for sponsored content. • Advanced analytics for personalization. • Premium features (e.g., ad-free, exclusive discounts). Challenges and Considerations 1. Data Privacy: • Ensure secure photo storage and compliance with regulations like GDPR. 2. AI Accuracy: • Fine-tune ML models for accurate clothing categorization and recommendations. 3. Scalability: • Build a robust infrastructure for growing user base and high engagement levels.