By embedding OpenAI models in supplier and catalog systems, Wayfair improved data accuracy and automated workflows for millions of products.

Wayfair, one of the world’s largest home goods retailers, has integrated OpenAI models into critical internal systems to improve supplier support workflows and product catalog quality at scale. What began as value-testing small scale releases in 2024 has evolved into a full production system that reduces manual effort, accelerates decision-making and improves data quality across millions of products.

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.

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