Inspiration:

An apparel return is often a decision about appearance, not a defective product. A shopper can see a replacement on a product page, but cannot easily see how that alternative looks on the same person before choosing an exchange or refund.

What it does:

Reconsider turns a style-related return into a visual replacement decision. A shopper starts with a sample order, sees three eligible garments, switches between their product images and matching YouCam virtual try-on results, reviews a server-calculated price difference, and saves a sample exchange or refund decision. For fit, damage, and other concerns, the app gives reason-specific guidance instead of treating virtual try-on as proof of fit or product condition.

How we built it:

The local TypeScript/Node.js application has a Fastify API, SQLite-backed order and decision logic, and a server-side YouCam integration. With consent, the adapter uploads a shopper photo and garment reference through YouCam’s File API, creates a Clothes V4 upper_body task, polls for completion, and makes the result available to the owning session. Three successful tasks produced the authentic synthetic-shopper results shown in the public demo.

The public Vercel experience uses those previously recorded YouCam results, isolated sample sessions, a simulated catalog, and server-calculated quotes. It does not start new YouCam jobs on each visit. The sample decisions do not change a real retailer order or process a payment or refund.

Challenges and lessons:

The main design challenge was making an exchange decision useful without overstating what virtual try-on can prove. YouCam helps compare appearance; it does not predict garment fit, detect damage, or guarantee stock. We kept those boundaries visible and built separate paths for non-style reasons.

What’s next:

A merchant pilot would add real order authorization, live inventory and fulfillment integrations, production image-retention controls, and measurement of whether visual comparisons improve exchange decisions.

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