Rather than treat generative AI as an experiment or point solution, Wayfair embedded OpenAI models into core operational workflows. The company focused first where complexity and need for scale were highest: routing and resolving supplier support requests and improving tens of thousands of product attributes consistently across a catalog of roughly 30 million items.

Wayfair’s catalog team manages tens of millions of products across nearly a thousand different product classes. Consistent and accurate product attribute tags—such as color, material, size or specific features—are essential for search, recommendations and merchandising.

"The better our data quality, the more trust we build with the customer. It's essential because it empowers shoppers to make the right buying decisions, directly reducing costly downstream issues like returns from misrepresented products," said Jessica D'Arcy, Associate Director of Catalog Merchandising at Wayfair.

Before OpenAI, tagging improvements primarily relied on suppliers and customers to tell Wayfair that something looked wrong. Manual effort could not keep up with the volume. Early custom AI models for individual tags were effective, but proved expensive to build and maintain. “We started by building bespoke models for individual tags, and technically that worked,” said Carolyn Phillips, Wayfair’s staff machine learning scientist. “But when you’re looking at 47,000 tags, that approach just doesn’t scale.”