AI Closet
Your wardrobe, digitized — outfits styled by AI, tried on virtually, and shared with a community.
AI Closet is a cross-platform fashion-tech application built end-to-end in Flutter for a fashion-tech founder. It lets users photograph every item they own, classify it with AI, generate outfit suggestions on demand, try the final look on a personal avatar, and share it in a built-in social feed — all inside one polished, mobile-first experience running on iOS, Android, and Web from a single codebase.
One app that replaces the whole fashion-app stack
AI Closet is a production-grade Flutter application architected around real fashion-tech workflows. Users authenticate with Firebase (email/password + Google Sign-In), upload items into their personal closet, ask the AI for outfit suggestions, try on a final look on a virtual avatar, and post that look to a social feed other users can discover. Every feature — closet, outfit engine, try-on, feed, profile, settings — was designed, implemented, and wired together as a single cohesive product ready to ship across iOS, Android, and Web from one codebase.
Fashion apps are fragmented — and none of them stick
A fashion-tech founder targeting AI wardrobe and styling users has to compete with a market where every function lives in a different app. Users install one app to catalog clothes, another to generate outfits, a third to try on looks, and a fourth to share them socially. That fragmentation kills retention, makes monetization impossible, and leaves the core idea — an intelligent personal wardrobe — unfulfilled. The founder needed a single production-ready mobile product that could credibly own the full fashion-tech loop on day one.
- Users juggle 3–4 separate apps for closet, styling, try-on, and social
- No single product unifies AI outfits with virtual try-on in one flow
- Fashion founders need cross-platform reach (iOS + Android + Web) from the start
- Social and styling features are usually bolted on instead of designed together
One Flutter app — seven connected fashion flows
I delivered a single Flutter application organized around seven tightly connected flows — Auth, Closet, Outfit Suggestions, Virtual Try-On with Avatar, Social Feed, Profile, and Settings — built on a clean MVVM architecture with Provider state management, GetIt dependency injection, GoRouter declarative navigation, and Dio for networking. Firebase Auth powers sign-up, login, and Google Sign-In out of the box. Every screen was implemented natively in Flutter so the app runs from one codebase on iOS, Android, and Web without rebuilding the UI three times.
Every feature a fashion-tech user actually opens
- Digital Closet — upload, categorize, and browse every item in the user's real wardrobe
- AI Clothing Analysis — automatic type / color / pattern detection on upload
- AI Outfit Suggestions — generate full looks from the user's own closet on demand
- Virtual Try-On — dress a personal avatar with selected items before committing to a look
- Social Feed — post outfits, follow other users, and discover real-world styling ideas
- Firebase Auth — email/password + Google Sign-In with clean onboarding flow
- Profile & Settings — manage avatar, preferences, and account from one place
- Cross-Platform — single Flutter codebase shipping to iOS, Android, and Web
From a founder's vision to a shipped, cross-platform app
- 01
Product framing
Worked with the founder to pin down the core promise — a user's entire wardrobe, styled by AI and tried on virtually — and ruthlessly scoped the MVP around the flows that prove that promise end-to-end.
- 02
Architecture design
Chose Flutter for a single iOS + Android + Web codebase and designed a clean MVVM structure (models / views / viewmodels / services) with GetIt for DI, Provider for state, and GoRouter for navigation — a foundation that stays maintainable as the product grows.
- 03
Auth & onboarding
Built the full auth stack on Firebase — email/password, Google Sign-In, register and signup flows — so users get from app launch to a working closet in under a minute.
- 04
Closet & AI analysis
Implemented the closet module end-to-end: upload, clothing details, list/grid views, and an AI service that auto-tags new items by type, color, and pattern to keep the catalog clean without manual work.
- 05
Outfit engine & virtual try-on
Wired the outfit suggestions viewmodel to the user's closet and built a dedicated try-on flow where users dress an avatar with generated looks — the emotional core of the product.
- 06
Social feed & profile
Added a feed with posting, a profile area, and a settings screen so the product has a true social loop and a clear account surface — not just a personal utility.
- 07
Cross-platform polish
Tuned theming (light + dark), Google Fonts typography, splash, routing, and responsive layouts so the app feels native on iOS, Android, and Web from a single build.
A production Flutter stack built for scale
AI Closet is built natively on Android using a focused, modern stack chosen for performance, maintainability, and a clean user experience.
The decisions that shaped the product
Keeping seven flows from becoming seven apps
Closet, outfit engine, try-on, feed, profile, auth, and settings are each substantial surfaces. I designed a shared theme, a single router, and a consistent view/viewmodel/service pattern so every new flow plugged into the same architecture instead of drifting into its own silo.
Making virtual try-on feel like the hero, not a gimmick
The avatar try-on screen is the emotional payoff of the entire product. I treated it as a first-class flow with its own viewmodel and service layer so it could evolve from a simple avatar preview into a real AI-driven try-on without rewriting the surrounding app.
Shipping iOS, Android, and Web from one codebase
Cross-platform sounds simple until typography, routing, and auth start behaving differently on each target. Choosing Flutter plus a carefully abstracted service layer (auth, clothing, user, avatar, AI) kept the UI identical and the platform-specific work minimal.
Designing for an AI roadmap, not a demo
The AI service was built as a clean, swappable boundary — today it returns structured clothing attributes, tomorrow it can plug into a real vision model or a styling LLM without touching the UI layer. That future-proofs the product for the founder's actual roadmap.
Why this project matters
AI Closet demonstrates exactly what a fashion-tech founder gets when they hire LogicsBeat: a real product, not a demo. It's a cross-platform Flutter app with a clean MVVM architecture, Firebase-powered auth, a full social + styling + try-on loop, and a service layer ready for production AI — all shipped from a single codebase to iOS, Android, and Web. For any founder building in the AI wardrobe, virtual try-on, or AI styling space, this is the blueprint: scoped tightly, engineered cleanly, and designed to be the foundation of a real business — not a throwaway MVP.
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