Wayfair boosts catalog accuracy and support speed with OpenAI

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.

“What’s been most valuable is the thought partnership. It’s not just access to the models. It’s working through new use cases together and being able to move quickly.”

—Fiona Tan, Chief Technology Officer

Solving catalog quality at scale

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. 

Building a reusable AI architecture

Rethinking supplier support workflows with Wilma

“If you don’t route the issue correctly at the start, everything downstream slows down. Triage is foundational.”

–Graham Ganssle, supplier support operations, Wayfair

Results at a glance

Wayfair reports measurable improvements since integrating OpenAI models into internal systems.

Operationally, teams report: