Product Vision
Stop tearing through your closet trying on endless combinations or wondering if that jacket matches your trousers. Dresscode puts an AI-powered virtual fitting room and intelligent personal stylist right in your pocket.
Users simply snap photos of their clothes or upload a personal portrait. Dresscode automatically analyzes garments, catalogs wardrobe pieces, cross-references localized weather forecasts, and generates realistic virtual try-ons directly on the user’s photo before they ever get dressed.
Core Capabilities
1. AI Virtual Try-On Direct on Your Photo
Why guess how clothes look together when you can see them rendered directly on your actual silhouette? Dresscode leverages targeted generative diffusion pipelines to project digitized tops, bottoms, outerwear, and accessories onto user portraits with realistic fabric drape, lighting alignment, and proportion preservation.
2. Automated Visual Wardrobe Digitization
Building a digital closet is effortless. Users snap photos of their garments, and our computer vision pipeline—powered by multimodal Gemma foundation models—automatically isolates items, extracts precise hex palettes, determines fabric textures, warmth levels, and formality tiers without requiring manual tagging.
3. Context-Aware & Weather-Synced Styling
Dresscode never recommends a linen shirt in freezing rain. Powered by a LangGraph multi-step agent and external weather APIs, the recommendation engine dynamically cross-references ambient temperature, wind chill, precipitation probability, and the user’s schedule (e.g. casual weekend, executive presentation, dinner date) to curate the ideal ensemble.
4. Semantic Wardrobe Search & RAG
Using a specialized Retrieval-Augmented Generation (RAG) pipeline over vectorized clothing metadata, users can pose natural language questions like “What should I wear to a breezy outdoor rooftop evening?” and receive combinations grounded in their real wardrobe.
Architectural Breakdown
┌────────────────────────────────────────────────────────┐
│ Flutter Cross-Platform App │
│ (iOS & Android Camera / Try-On) │
└───────────────────────────┬────────────────────────────┘
│ HTTPS / WebSocket
▼
┌────────────────────────────────────────────────────────┐
│ FastAPI Asynchronous Gateway │
│ - Auth, EXIF Stripping, Rate Limiting │
└─────────────┬────────────────────────────┬─────────────┘
│ │
▼ ▼
┌───────────────────────────┐ ┌──────────────────────────┐
│ Redis Cache & Broker │ │ LangGraph Styling Agent │
│ - Real-time weather cache│ │ - Dynamic tool routing │
│ - Session state store │ │ - Weather API fetcher │
│ - Async job queues │ │ - Wardrobe RAG Vector │
└─────────────┬─────────────┘ └────────────┬─────────────┘
│ │
▼ ▼
┌───────────────────────────┐ ┌──────────────────────────┐
│ Pruna-Optimized Diffusion│ │ Gemma Multimodal Engine │
│ - Virtual Try-On Pipeline│ │ - Garment Segmentation │
│ - Edge/Cloud accelerated │ │ - Attribute Extraction │
└───────────────────────────┘ └──────────────────────────┘High-Velocity Tech Stack
- Flutter & Dart: Cross-platform application engineered for smooth 60 FPS interactions, camera capture optimizations, and responsive layout scaling across iPhone, iPad, and Android form factors.
- FastAPI: Asynchronous Python backend utilizing Pydantic data schemas, non-blocking I/O event loops, and high-concurrency connection pools.
- LangGraph & LangChain: Stateful graph orchestration that models outfit recommendations as directed acyclic agent loops, managing tool executions, conditional fallbacks, and multi-turn user preferences.
- Gemma Multimodal: Foundation vision-language model performing zero-shot segmentation, garment hierarchy classification, and style attribute extraction from unstructured smartphone images.
- Pruna AI Optimization: Deep quantization, pruning, and structural compilation of large neural weights, reducing generative model latency by over 3x and minimizing cold-start overhead on cloud instances.
- Image Generation & Virtual Fitting Room: High-fidelity diffusion network specialized for human pose preservation, textile pattern transfer, and seamless outfit blending on user reference photos.
- Async Operations & Task Scheduling: Asynchronous background workers offload heavy image synthesis tasks, decoupling interactive HTTP requests from compute-intensive inference jobs.
- Redis Layer: Distributed memory store delivering sub-millisecond session lookups, caching hourly meteorological forecasts to slash redundant 3rd-party API expenses, and managing real-time task queues.
- Wardrobe RAG Pipeline: Embeddings generated for every wardrobe item stored in a vector index, enabling semantic search and retrieval based on style descriptors, color theory, and seasonal suitability.
Privacy & Security by Design
- EXIF Metadata Stripping: All geographic GPS coordinates, camera identifiers, and timestamp metadata are scrubbed at the ingress gateway before images touch storage or inference pipelines.
- Private Image Isolation: User try-on portraits and digital wardrobe photos are stored in cryptographically isolated multi-tenant object buckets with time-limited signed access URLs.
- Zero Model Training Retention: Personal user portraits are strictly processed for runtime virtual try-on inference and are never pooled into general foundation model training sets.
Live Deployments & Getting Started
Experience Dresscode today on your mobile device:
- Apple App Store: Download on iOS (Compatible with iPhone, iPad & iPod touch on iOS 15.6+)
- Google Play: Get it on Android (Optimized for Android smartphones and tablets)
- Official Website: dresscode.alkentar.store