Find what you see, visual AI that understands style.

To train an AI model effectively, we first need a high-quality dataset. This involves collecting product images and organizing them using a clear set of categories—such as patterns (e.g., floral, striped), styles (e.g., bohemian, minimalist), and design elements (e.g., ruffle, V-neck). We use data augmentation techniques (like cropping or color adjustment) to make the dataset more robust and pass images through an annotation tool where humans verify the labels. A smart feedback loop helps us improve this dataset continuously by identifying and fixing gaps.

We take a powerful image recognition model (called a Vision Transformer), and re-train it to understand fashion-specific features. By showing it many examples from our clothing catalog, it learns to identify things like floral patterns, ruffled trims, bohemian styles, or V-shaped necklines—just like a human stylist would. Once trained, this model can look at any new product image and automatically tag it with the right visual attributes.

To make sure the model is working correctly, we build an automatic testing system. Every time the model is updated, this system checks how accurately it identifies each attribute—like whether it’s getting floral patterns right or missing certain styles. If performance drops or specific attributes become inconsistent, the system automatically flags those cases for correction or retraining—keeping quality high without needing constant human review.

New products—around 500 each week—enter a fully automated pipeline. High-confidence tags are instantly saved to the product database, while uncertain ones go into reports for deeper review. All tags feed into the search system on the website, enabling customers to filter by pattern, style, or neckline. A dashboard tracks everything—how many products are processed, tagging accuracy, and whether anything needs attention.

We create user-friendly APIs (application programming interfaces) that let other systems interact with the tagging model. For example, a web team can send an image to the API and receive visual tags in return. These APIs are documented and designed to be secure, scalable, and easy to integrate with other tools—like inventory systems, search engines, or even third-party apps.

To keep the system reliable, we set up automated testing pipelines. These tests make sure the model behaves correctly, the APIs respond quickly, and everything works smoothly under load. Whenever changes are made—like improving the model or updating an endpoint—these tests run automatically. Any issues get flagged for developers to fix, ensuring a steady pace of refinement and stability.