bobbie: an AI fashion assistant
AI-powered digital wardrobe management that reduces returns and builds customer confidence in online apparel.
- Client
- bobbie.ai
- Industry
- Fashion & retail
- Services
- AI / ML · cross-platform app
Product · bobbie.ai- 2,000+
Active users
In MVP phase
- 40%
Conversion rate
To paid features
- 800+
Paid subscribers
Premium storage
Online fashion retail loses billions to returns and dissatisfaction.
The online fashion sector carries significant overhead and consumer dissatisfaction from garment fit issues. High return rates, often from improper sizing or unrealistic product representation, are a substantial cost for retailers and customers alike, with real logistical and environmental impact.
There is clear demand for a way to visualise fit before purchase and to draw on peer styling insight, but no one had built a consumer product that combined both at scale.
The numbers make the case. Returns can erase 20 to 30% of online apparel revenue, and a large share trace back to fit uncertainty at the point of purchase. bobbie set out to close that gap before checkout rather than absorb the cost afterward.
Build bobbie, a comprehensive digital wardrobe platform.
Our team was contracted to build bobbie, an AI-powered fashion assistant. In its MVP it works as a comprehensive digital wardrobe: users curate, assemble, and manage their clothing collections in one place.
Under the hood, bobbie pairs a Flutter cross-platform client with a Node.js and PostgreSQL backend, object storage on Cloudflare R2 for wardrobe imagery, and a recommendation stack on TensorFlow and PyTorch. Users photograph or import garments, the models tag and embed each item, and the outfit engine assembles looks from what the user actually owns.
The engagement ran lean and fast. We scoped the MVP around the two features users cared about most, the digital wardrobe and AI outfit generation, shipped them to the App Store and Google Play, and instrumented everything so the team could watch real behaviour instead of guessing. Weekly builds kept the founders in the loop.
The roadmap extends the same data rather than starting over: a social and marketplace layer where users follow stylists, shop looks they can already visualise on their own wardrobe, and check out with fit confidence built in. That is what makes the path to scale realistic rather than aspirational.
“The Automators turned our concept into a real, polished product fast. The AI wardrobe and outfit generation are exactly what our users wanted, and adoption in the MVP phase has spoken for itself.”
Co-founder & CEO, bobbie
What actually went live.
Digital wardrobe success
Users built digital wardrobes averaging 80+ catalogued items each, curating outfits and managing collections from a single app.
AI personalisation
The outfit-generation model produced personalised looks from user-imported garments, with roughly 4 in 5 generated outfits saved or worn.
Rapid user adoption
Over 2,000 active users joined within the initial MVP phase, growing about 25% month over month on organic referral alone.
Strong monetisation
800+ users (around 40%) upgraded to paid storage within weeks, a clear signal of willingness to pay and a path to scale.
The tools we shipped with.
Production-grade and vendor-agnostic, chosen for reliability and scale.
- TensorFlow
- PyTorch
- Recommendation models
- Flutter
- iOS · Android · Web
- Cross-platform UI
- Node.js · Express
- PostgreSQL
- Cloudflare R2 storage
- AWS EC2
- AWS S3
- AWS Lambda
Other case studies.
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