Tamba’s Case Study

Transforming Wardrobe Management with AI-Powered Styling

Vibe is a wardrobe management app that helps users digitally organize clothing and receive AI-powered outfit recommendations.

From Vision to Reality

The Tech and Talent Powering Vibe

Expertise

AI & Machine Learning, Web & Mobile Development, Cloud Storage, Product & UX Design

Tech Stack

React Native, Node.js, PostgreSQL, AWS S3, OpenAI's LLM & Custom ML models, Firebase, Vercel

Deliverables

Mobile application, Web-based admin panel, Secure cloud storage, Privacy protection

Innovative Features

Elevating Wardrobe Management with AI

Precision Color Matching

Personalized Outfit Suggestions for Every Occasion

AI-Powered Wardrobe Categorization

Redefining Wardrobe Management

Smart, Seamless, and Personalized Goals

Vibe is designed to make wardrobe management effortless, blending technology with personal style to create a seamless experience. By simplifying organization and enhancing styling possibilities, it empowers users to curate their wardrobes with confidence and ease.

Seamless Wardrobe Management

Enable users to easily upload, categorize, and organize their clothing digitally.

AI-Powered Recommendations

Provide smart styling suggestions based on wardrobe contents, color compatibility, and occasions.

Privacy-First Personalization

Ensure outfit recommendations without collecting personal details like gender, age, or skin color.

Concept to Reality

Crafting an Intelligent Wardrobe Experience

Bringing Vibe to life required a structured approach, blending research, design, development, and continuous refinement. Every step was carefully executed to ensure a seamless, AI-powered styling experience.

Discovery & Strategy

Conducted in-depth market research to uncover gaps in wardrobe management solutions.

Design & User Experience

Developed intuitive wireframes and prototypes, ensuring a smooth wardrobe organization process.

Development & Integration

Built AI-driven styling, color analysis, and cloud-based wardrobe management features.

Testing & Refinement

Optimized AI recommendations, improved color detection accuracy, and gathered user feedback for enhancements.

Overcoming Challenges

Refining AI for a Flawless  Styling Experience

Lighting variations affected color accuracy, causing inconsistencies in wardrobe representation. Shadows and different light sources altered how colors appeared, leading to inaccurate outfit recommendations.

Early AI-generated outfit recommendations lacked contextual awareness, often suggesting combinations that didn’t align with fashion principles or seasonal trends. This limited the AI’s ability to provide truly relevant styling advice.

To improve this, we trained the model on color theory, fashion guidelines, and seasonal trends, enhancing its ability to curate stylish, context-aware outfit recommendations.

To solve this, we implemented OpenCV-based algorithms that analyzed highlights, mid-tones, and shadows separately, ensuring precise and true-to-life color matching.