Building Twin.shop: Flutter for AI Virtual Try-On
Notes from leading Flutter on an image-heavy, latency-sensitive AI shopping app — architecture, generative UX, and production standards.
Twin.shop is an AI-powered virtual try-on and shopping platform. As Lead Engineer – Flutter, I own mobile architecture, feature delivery, release planning, and production-quality standards for journeys that are image-heavy and latency-sensitive.
The hard part isn’t the button
Generative try-on looks magical in a demo. In production it’s loading states, API contracts, fallbacks when a model is slow, offline-friendly caching, and UI that still feels responsive when the network isn’t. The mobile client has to absorb that complexity without making the shopper feel it.
Architecture choices
We keep the Flutter app modular: reusable components for product discovery, try-on interactions, and personalized flows; clear ownership for state management; analytics readiness; and release workflows that don’t rely on heroics. Standards for performance and test coverage matter more as the surface area grows.
Cross-team seams
Mobile sits between design, backend, and AI. Good contracts — request/response shapes, error modes, progressive disclosure — beat clever widgets. Most of the “AI UX” work is really product engineering: what do you show while waiting, what do you do when the result is wrong, how do you recover?
Shipped where it counts
Twin is live on the Play Store and App Store. That’s the bar for this kind of work: not a prototype, but a consumer app that has to keep releasing.