Case study
How Kleidn launched a smart outfit editor with Claid's custom image intelligence workflow
With Claid AI, Kleidn enabled a custom visual pipeline for the outfit composition layer: analyzing product images, identifying flatlay assets, and removing backgrounds so items could be cleanly assembled inside the app.
About Kleidn
Kleidn is a fashion app that offers an innovative shopping experience built around outfit creation. Users can browse fashion items, mix and match them into looks, and shop through outfit combinations. Its shop is stocked with over 50,000 fashion and accessory items from partner brands and retailers.
The challenge: messy and inconsistent inputs
For Kleidn, launching an outfit editor depended on image quality and structure. The app needed items that could be dragged, layered, and styled together naturally.
That made image processing a foundational part of the product. Kleidn needed an automated way to turn inconsistent scraped fashion imagery into assets that were clean enough, isolated enough, and reliable enough to support the core outfit-building experience.
The AI solution had to tackle:
The solution: custom image analysis and background removal pipeline
With Claid, Kleidn built a visual intelligence layer designed around a specific product experience. It helps the company turn a noisy stream of scraped fashion imagery into structured assets that users could actually style with.
This is how the process goes:
- Kleidn's tech scrapes product images across its sources
- Custom pipeline enabled by Claid AI analyzes those images and identifies usable flatlays
- We remove the backgrounds and deliver cleaner, composition-ready cutouts that slot directly into Kleidn's editor
The results: clean, scalable foundation for the outfit editor
By cooperating with Claid AI, Kleidn got a more usable pipeline for assets used in its outfit editor. The custom workflow automatically detects flatlay images across large volumes of scraped data and removes backgrounds cleanly to ensure every item is ready for outfit composition.
This functionality helped enable the product experience Kleidn wanted to launch. It helped Kleidn:
- Launch the outfit editor with cleaner assets
- Improve the quality of the "compose the outfit" experience
- Reduce manual asset preparation
- Support outfit-building with a broader, continuously refreshed catalog
Partnership that fits the product's logic and operational needs
Our AI-native team designs and runs bespoke visual workflows tailored to a particular use case. For Kleidn, we helped power an innovative shopping experience with image analysis and asset preparation built for how people actually discover and buy fashion.
On top of the visual intelligence layer that makes scraped product images usable inside Kleidn's app, Claid AI ensured the workflow matched Kleidn's product logic and needs:
- Customization: the workflow was tailored to Kleidn's editor and catalog structure
- Hands-on support: Claid's team worked closely on setup, refinement, and ongoing optimization
- Operational flexibility: the pipeline can adapt to the realities of scraped fashion data at scale
Need this at scale?
Process thousands of images via API, or let our team handle it for you.