I’m building a cross-platform mobile app that acts as a personal styling and beauty assistant for both men and women. The core of the product is an AI engine that delivers highly-personalized outfit recommendations. Key functionality • The assistant studies three user-supplied inputs—body measurements, skin tone and face shape—and combines them with each person’s stated style preferences, their body type and real-time fashion trend data. • For every request it returns a clear set of outfit suggestions, indicating why each piece flatters the user’s proportions, complexion and current tastes. • A lightweight dashboard lets me update trend data, brand catalogs and style rules without redeploying the app. Technology expectations The recommendation layer can be built with Python (TensorFlow / PyTorch) or a similarly capable stack, and computer-vision models (e.g., OpenCV, Mediapipe) will be needed to validate measurements from photos if users choose that option. The mobile front‐end may be native (Swift / Kotlin) or React Native / Flutter as long as performance remains smooth. Cloud hosting on AWS, GCP or Azure is fine; I’ll provide the keys. Deliverables • Model code, fully commented and trainable on new datasets • iOS & Android builds with onboarding flow, profile editor and recommendation screen • Admin panel for trend and catalog management • API documentation and a short video walkthrough A successful build should process a new user profile and return at least three complete outfit suggestions in under three seconds on a mid-range device.