This project aims to build a scalable AI-driven personalization engine that analyzes user-provided inputs to generate structured, explainable style recommendations including body type classification, face shape analysis, and color palette suggestions.
This project builds a scalable, AI-driven personalization system that analyzes user inputs to generate structured and explainable fashion recommendations. The system focuses on identifying:
- Face Shape
- Body Type
- Color Palette
- Personal Aesthetic
V0 is a fully functional prototype that demonstrates an end-to-end rule-based personalization pipeline. Users answer a structured questionnaire, and the backend processes these responses using deterministic logic to generate basic style classifications.
- Collects user input via questionnaire
- Processes responses using rule-based logic
- Classifies:
- Face Shape
- Body Type
- Color Profile
- Aesthetic Style
- Displays results on the frontend
User Input → Backend Processing → Rule Engine → Output Generation
- Validate system flow end-to-end
- Establish baseline logic for personalization
- Create a foundation for future ML integration
- Hardcoded rules (no learning capability)
- Static questionnaire
- Limited personalization depth
- No explainability layer
- Frontend: Collects questionnaire responses
- Backend: Processes inputs and applies rules
- Logic Layer: Deterministic rule engine
- Output Layer: Displays classification results
V1 will transition from rule-based logic to a weighted personalization engine, introducing:
- Weighted scoring system
- Confidence scores
- Persistent user data
- More nuanced personalization
- Frontend: Next.js + Tailwind
- Backend: FastAPI
- Auth: Firebase Auth / Auth0
- Database: PostgreSQL
- Cache: Redis
- Hosting: Vercel + Render/AWS
V0 successfully validates a complete personalization pipeline using rule-based logic. While simplistic, it establishes a strong structural foundation for scaling into a more intelligent, data-driven system in future versions.