Reference · Wodara
An AI-powered digital wardrobe.
We developed the mobile app, web and admin panels, back-end services, and community experience.
Wodara- Status
- Live · in development
- Headquarters
- Switzerland
- Field
- Fashion technology
The work
For Switzerland-based Wodara, we added a measurable operating foundation—RevenueCat subscriptions, Firebase Analytics, and Sentry—to a product that helps users organize clothing, create outfits, and receive style suggestions.
Product and engineering teams worked closely throughout the project. The team photograph on this page was taken in Switzerland as part of that collaboration.
Problem
In the right market, in the wrong visibility.
Switzerland is a multilingual and narrow market; search behavior operates differently from large markets. A common visibility approach does not pay off here.
The need was a market-specific positioning on both the search engine and app store side.
Approach
Measure and change, don't guess.
First, we measured the current situation: in which queries it appears, in which it does not appear, which terms correspond on the store side. We sorted the changes by this measure.
We accelerated production on the content and metadata side with an AI-powered flow; the measurement that made the decision was the tool that accelerated production. After each change, we measured again and determined the next step.
Scope
- Mobile application
- Web and admin
- Back end
- Community
- Subscriptions
Technologies
- React Native
- Next.js
- Node.js
- Python
- MongoDB
- RevenueCat
- Firebase
- Sentry
Result
Market-specific, measurement-based visibility.
The study placed visibility in a measurement-dependent loop, not a prediction. For us, this case is a record of being able to work in a non-EU market and accelerate the visibility layer with AI.
- Market-specific query and term analysis
- Search and store side together
- Each change was verified by measurement
