Etsy is doubling down on a hybrid approach to artificial intelligence that keeps humans in the loop and ensures shoppers find what they want. The company is pursuing a strategy it calls “algotorial curation,” which blends recommendations by Etsy’s staff with advanced machine learning algorithms to scale curation across its inventory, Chief Product Officer Nick Daniel said. The process starts with human experts identifying trends and selecting listings that are examples of these trends. “After a collection is identified, our engineers use machine learning to expand it from roughly 50 human-curated listings to about 1,000. Finally, we use LLMs to make sure the full collection is aesthetically cohesive, represents a variety of products and meets our standards for quality.” The company uses Google’s Gemini multimodal model to power these experiences. Despite advances in generative AI, Etsy isn’t looking to eliminate humans from the equation. Instead, the company sees AI as a way to enhance human insight at scale, Daniel said. “Rather than removing human expertise from our merchandising work as AI becomes more powerful, we’re leveraging these tools to amplify the expertise of our team and create a more personalized experience. We’re putting human touch — from our buyers to our teams of employees to our sellers — at the center of shopping on Etsy. Because each item on Etsy is listed individually by a real seller, the data we have isn’t uniform — we’re not like a traditional eCommerce marketplace with a catalog or SKUs. AI can help us bridge this gap. We’re leveraging LLMs to extract key product details, like size and color, from listings, which improves search and helps connect the right items to the right buyers,” Daniel said. This strategy has yielded measurable results, boosting visibility and sales. “We used LLMs to generate alt text for listings that didn’t already have it and saw a nearly 5% increase in SEO visits and a nearly 3% increase in conversions to sales attributed to those visits,” he said.