Wayfair's Applied Research team uses Cursor to compress months of machine learning and applied AI research into days. By late 2025, researchers were running up to 20+ agents in parallel. This enabled a team of five to test 110 model variants in a four-day experimentation sprint and reduce inference costs for a core e-commerce catalog enrichment workflow by 94%. In March 2026, the team repeated the same playbook with the latest models in Cursor, cutting costs by another 90%.
Cursor has changed how ML research operates at Wayfair. Wayfair's researchers drive the model improvements: crafting hypotheses, interpreting results, and refining the strongest ideas. Cursor handles the implementation: building experiments, wiring them into the testing framework, and measuring results.
Validating product attribute data against the world's largest homegoods catalog
Every product in Wayfair's catalog is described by structured "tags" describing materials, dimensions, color, and other attributes. Over 47,000 distinct attribute tags power search, filtering, recommendations, product placement, and advertising for tens of millions of products.
Wayfair's Applied AI team built a validation model that audits each tag against images, descriptions, and customer reviews on the product page. The model was accurate, but too expensive to run across large swaths of Wayfair's massive product catalog.
