~/work/stylesyncs
StyleSyncs
AI-powered virtual try-on for fashion
A generative-AI fashion visualization platform that lets users virtually try on clothes. Grew to 100+ registered users and raised $9K+ through pitch competitions.
React
TypeScript
Supabase
Gemini API
GCP Cloud Run
Python100+
registered users
$9K+
raised in pitch competitions
3
generative features: video try-on, static try-on, digital avatars
stylesyncs.com
01 · the problem
Online shoppers can't tell how clothes will actually look on them, and that uncertainty drives returns, abandoned carts, and frustration. StyleSyncs set out to close that gap with generative AI: upload a photo, pick a garment, and see yourself wearing it.
02 · what I built
- Co-founded the company and engineered the product end to end, from the generative pipeline to production deployment.
- Currently developing the flagship real-time video try-on feature, which overlays clothes on live camera feeds.
- Engineered a generative pipeline based on Gemini 3 Pro Image to power the platform's static virtual try-on and digital avatar creation features.
- Built the product on React + TypeScript with Supabase for auth and data, deployed on GCP Cloud Run.
03 · the pipeline
- 1
ingest
User uploads a photo and selects a garment through the React/TypeScript frontend, with Supabase handling auth and storage.
- 2
analyze
A multimodal Gemini model analyzes the clothing item (category, fit, texture) to condition the downstream generation.
- 3
avatar
Gemini image models (Gemini 3 Pro Image and Gemini 3 Flash Image) create a clean digital avatar from the user's photo.
- 4
generate
Gemini 3 Pro Image renders the garment onto the avatar with realistic drape and lighting.
- 5
serve
Results are served from GCP Cloud Run back to the user's gallery.
04 · key decisions
One image model family across the pipeline
Consolidating avatar creation and try-on rendering on Gemini 3 Pro Image simplified the pipeline and kept output style consistent between stages.
Ship to real users early
We launched before the pipeline was perfect and let real usage drive priorities. That's how real-time video try-on became the next bet; users kept asking for it.
05 · where it stands
- ✓100+ registered users on the live platform at stylesyncs.com.
- ✓$9,000+ raised through pitch competitions.
- ✓Real-time video try-on in active development.
ReactTypeScriptSupabaseGemini APIGCP Cloud RunPython