Mobile App

DrapeAI - Try On

AI-powered virtual clothing try-on app

DrapeAI - Try On — Mobile App · Atekian
Result
MVP: 4 weeks
Technologies used
FlutterPythonComputer VisionSupabase

Context

DrapeAI came to us with an idea for a mobile app that lets users try on clothing on their own photos before buying. The idea was clear, but technical feasibility had to be proven quickly.

Problem

Two things had to work at once for the try-on experience to land: image processing quality had to be convincing, and the app had to respond within a reasonable time on a mobile device. A working product was needed before investor conversations.

Solution

We chose Flutter to ship iOS and Android from a single codebase. Image processing was built as a separate Python service, so model improvements could ship without releasing a new mobile build. Supabase handled auth, storage and database, cutting backend setup from days to hours.

Case summary

Outcomes

From MVP to launch, fast

  • MVP went from idea to launch in 4 weeks
  • iOS and Android delivered simultaneously from one codebase
  • Image processing service became independently deployable
  • Supabase removed backend setup as a separate workstream

Frequently asked questions

What technologies was DrapeAI built with?

The mobile app was built with Flutter, targeting iOS and Android from a single codebase. The image processing and computer vision layer runs as a separate Python service. Supabase provides authentication, file storage and the database.

Why was the MVP finished in 4 weeks?

Scope was narrowed upfront to a single verifiable flow: upload a photo, pick a garment, see the result. Secondary features were left out of the MVP. Flutter's single codebase and Supabase's managed backend collapsed what are normally two separate workstreams into one.

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