Role
Software Architect
Platform
iOS & Android
Year
2026
Stack
Flutter · Spring Boot · AWS · GPT-4 Vision
Eliminating Outfit Fatigue
Individual style is personal, yet finding the perfect match across a fragmented wardrobe is a complex cognitive task. Style Match was designed to eliminate "outfit fatigue" by leveraging AI to understand the nuances of personal fashion choices.
Intelligent Matching Engine
By building a scalable integrated architecture, we've created a system that processes high-resolution garment images and provides real-time matching suggestions — blending computer vision for item detection with a sophisticated recommendation engine.


The Experience
Elevating personal fashion through a curated set of intelligent tools designed for the modern wardrobe.
AI Predictive Match
Our neural engine learns your style preferences to suggest the perfect daily combinations.
Digital Wardrobe
A high-fidelity digital twin of your physical closet, accessible anywhere, anytime.
Trend Radar
Stay ahead with real-time fashion insights synced directly from global style hubs.
Smart Search
Find any garment in seconds using multi-parameter filters and visual recognition.
Performance Core
Zero-lag experience powered by a high-performance backend and Flutter frontend.
Secure Style Vault
Your personal data and style choices are protected by enterprise-grade encryption.
System Architecture
A multi-layered stack engineered for high-availability, low-latency AI processing, and a seamless cross-platform experience.
Client Interface
Service Infrastructure
Intelligence Engine
The Workflow
Step-by-step engineering journey of how we transform raw user data into personalized fashion intelligence.
Data Capture
Secure upload of wardrobe assets via Flutter. Assets are streamed to encrypted AWS S3 buckets with real-time metadata indexing for instant retrieval.
AI Analysis
GPT-4 Vision API performs granular garment classification, color extraction, and style tagging via high-fidelity visual analysis.
Smart Matching
A hybrid recommendation engine cross-references user preferences with current trends to generate mathematically optimized outfit combinations.
