Profitero needed a scalable way to tag e-commerce datasets that was both faster and more accurate than its existing solution. By leveraging super.AI, the company was able to improve latency while achieving more than 99% tag output accuracy.

The company, a leading retail & classifieds global group, reached out to super.AI to get help with cleaning up its user-generated product tags, with the ultimate goal to enhance the product search and the recommendation engine. We worked with them on a consistent product taxonomy and tagging categories, including for subjective tags such as style, and then process hundreds of thousands of images within a record timeframe. The client is now able to offer much more granular product results and recommendations to its user base and ultimately increase engagement and revenue.

Target, one of the largest retailers in the U.S., needed to leverage multiple data inputs to build a useful product recommendation engine and ensure product listing quality for its e-commerce business. Through its partnership with super.AI, the company was able to automatically categorize product images and score product listing quality using artificial intelligence (AI). This information was then used to flag product listings that do not satisfy quality standards as well as make relevant product recommendations.