DripCheck Fashion Try-On Sunset
DripCheck let shoppers upload a photo and see themselves wearing a store's clothing before buying. Customers submitted an image, the app generated a try-on preview, and merchants could style the button, cap monthly usage, and track try-on volume against conversions.
The two-step shopper flow: upload a photo, see the garment on yourself.
Architecture
Serverless on AWS: Lambda behind API Gateway, S3 for image storage, DynamoDB for per-store usage metering and quota enforcement, and the HeyBeauty generative try-on API for image synthesis. Asynchronous generation so the storefront never blocked on inference.
Try-on requests in blue; merchant admin and billing in grey.
The 99% cost cut
The original pipeline had a cost structure that could not support the price point. Re-architecting image handling and storage cut the AWS bill by 99% and turned per-transaction margin positive. Full writeup on Medium →
Unit economics
Pricing was modeled against the per-inference API cost rather than guessed: $9.99/month including 50 try-ons, then $0.13 per additional try-on, with merchant-configurable caps so a viral product page could not produce a surprise bill on either side.
Shipping it
Passing Shopify App Store review meant a working OAuth install flow, the mandatory GDPR compliance webhooks, Billing API integration, and meeting embedded-app requirements.
Why I shut it down
It topped out at two paying merchants. The hard lesson: distribution is hard.
Then image models such as Gemini's nano banana model commoditized the core feature. When a general purpose model can do virtual try-on well with no integration work, a single purpose try-on app has no moat left. I shut it down rather than keep funding it.