An AI-first, Gen Z–oriented fashion styling app enabling users to upload wardrobe items, receive personalized outfit suggestions using AI, and engage socially via outfit polls and feeds. The solution leverages Firebase’s scalable cloud backend and integrates OpenAI for prompt-based outfit generation. Users can manage a virtual closet, receive AI alerts on missing wardrobe items, and interact with a community through polls and comments. It also includes an admin panel for moderation, analytics, and subscription management. Monetization is driven through subscription tiers and integrated ads. The product is designed as a progressive MVP with clearly defined “must-have” and “nice-to-have” features to facilitate phased rollout. Post-launch, the client aims to validate market fit and scale with affiliate shopping and influencer integrations.

A marketplace required a web & mobile platform for live video shopping, enabling sellers to engage customers in real-time and drive sales through interactive live streaming. The system needed to support product showcasing, live chat, social media streaming, and seamless order management. The objective was to increase customer engagement, boost conversions, and offer a seamless shopping experience through live commerce.
This system was designed to:

• Enable sellers to conduct live streaming sessions for product demonstrations.
• Allow customers to view, interact, and purchase directly from live streams.
• Integrate with social media platforms for expanded reach.
• Facilitate real-time order and claim management.
• Support payment processing and secure transactions.
• Provide an intuitive admin dashboard for user and inventory management.

The Chat project involves the implementation of dynamic and interactive data visualization features, specifically focusing on Bar Charts and Pie Charts. These charts are designed to display multiple measures with customizable display options, enhancing the user's ability to analyze and interpret data effectively.