VirtualTry.Lab is an AI-powered virtual try-on and styling platform I built for a fashion e-commerce brand. Users paste a product link and upload a photo of themselves, and the system generates a realistic try-on preview along with an HD render, letting shoppers see how a garment actually looks on them before buying. Behind the scenes, I built an AI-driven segmentation step that isolates clothing from a photo, a personalization layer that detects skin undertone and learns each user's taste over time, and a trend-tracking system that scores fashion trends from multiple sources. I also built an AI shopping assistant that reasons over the live product catalog to recommend outfits and answer styling questions, with a reliable fallback system so the assistant never breaks even if a component fails. The project spans roughly 13,000 lines of code across a Next.js frontend, a NestJS backend, and a Chrome extension, and required solving real-world engineering problems like handling anti-bot protections, coordinating multiple AI providers for image generation, and designing a storage layer that can scale from local disk to cloud storage.