AI FITTING LABS

Inspiration

Growing up in Surat, the heart of India's textile industry, I saw firsthand how apparel brands bleed massive amounts of capital on return logistics. Traditional e-commerce relies on flat images and generic size charts that fail to give shoppers confidence. Brands burn lakhs on model photoshoots, and then spend heavy ad budgets just for customers to click and leave immediately. I wanted to build a zero-friction solution that helps online merchants increase customer stay-time and slash return rates. That is how AI Fitting Labs was born.

What it does

AI Fitting Labs is a plug-and-play, 100% inline Virtual Try-On (VTON) widget for e-commerce websites. When users swipe through products, the engine automatically detects the garment. Shoppers click "See it on you" and instantly visualize the clothing on their own photos without being redirected to external pages. It keeps visual hierarchy clean by hiding behind the checkout cart menu and uses a strict 4-trial daily limit to protect merchants from server resource abuse.

How we built it

Since I am a solo founder who doesn't come from a traditional coding background, I leveraged Gemini Pro as my development partner to architect and write the system.

Frontend: Hosted on Vercel for maximum responsiveness and lightning-fast loading speeds on e-commerce storefronts.
Backend & Security: Built a zero-trust loop on Cloudflare to handle request validation, customer permissions, and anti-abuse rate-limiting rules.
Obfuscation: The frontend code is entirely scrambled to protect our proprietary business logic and commercial pathways.

Challenges we ran into

Building a highly responsive widget that stays completely in-page without slowing down the merchant's website was incredibly tough. Managing server API costs was another massive hurdle. Because each generative try-on request costs money, we faced the risk of denial-of-wallet bot attacks. We solved this by implementing strict Cloudflare guardrails that limit users to 4 trials a day. Additionally, our current generation engine is trained strictly on Western wear, making it a challenge to deploy for local Indian ethnic wear (kurtis/sarees) immediately.

Accomplishments that we're proud of

We successfully transformed a complex technical idea into a fully working MVP in just 4 months using AI-assisted development. We engineered a seamless UI that naturally adapts to page environments (like hiding behind slide-out shopping carts) and built automated user guidance popups to ensure high-quality user inputs equal high-fidelity outputs.

What we learned

I learned how to think like a product manager and a true systems architect. I discovered that building the core generative AI feature is only 20% of the battle—the remaining 80% is handling real-world user edge cases, securing backend APIs against automated exploitation, and designing a viable B2B business model that can save real businesses money on day one.

What's next for AI FITTING LABS

Our immediate 90-day goal for the Gemini XPRIZE is rapid market validation and revenue generation using a two-tier funnel. To remove friction, we allow new e-commerce merchants to buy a one-time low-tier Trial Pack (ranging from ₹999 for 49 credits to ₹2,299 for 150 credits) to test our widget live on their storefront. Once this initial evaluation pack is exhausted, merchants must upgrade to our recurring core enterprise tiers, priced at ₹30,000 (2,000 credits) and ₹70,000 (5,500 credits), to maintain the virtual try-on service.

However, our ultimate infrastructure and data vision is divided into three distinct phases:

1. The Neural Draping Engine (Cost-Efficient Layering)

Instead of spending millions training a core Virtual Try-On (VTON) model from scratch, we are building a proprietary Neural Draping Engine as a specialized layer on top of existing open-source diffusion models. This layering architecture is highly GPU cost-efficient. It focuses computational power entirely on learning the complex folding physics and fabric dynamics of traditional Indian ethnic wear—specifically mastering the intricate 6-drape logic required for sarees, lehengas, and kurtis.

2. Private Server Infrastructure & B2B API Licensing

Currently, relying on public third-party APIs results in a 15–20 second generation latency. We plan to migrate our neural layer to a dedicated Private GPU Server Network. Latency Reduction: This will slash processing times down to a lightning-fast 5–10 seconds maximum, drastically improving e-commerce conversion rates. B2B API Monetization: Just like international platforms (e.g., Fashn.ai), we will package this proprietary neural draping layer into a premium API. We will license this API to global fashion marketplaces, independent software developers, and enterprise platforms who want to add ethnic VTON capabilities to their software.

3. The Ultimate Goal: Real-Time Fashion Trend Prediction

By operating the execution layer for thousands of fashion websites, AI Fitting Labs will sit on top of a massive, real-time data hub. Unlike traditional analytics that show what people bought weeks ago, our system tracks exactly what garments hundreds of thousands of users are swiping and trying on right now. By aggregating this intent data anonymously, we will transform into an AI-driven Trend Prediction Engine—giving designers and textile manufacturers in hubs like Surat the ability to predict viral fashion trends before they even hit the retail market.