From Traditional Keyword Search to AI-Powered Search: Our Journey

“In January 2024, we launched our in house search solution, transitioning away from third-party providers to gain greater control over relevance, performance, and innovation. Our initial focus was identifier search, ensuring customers could quickly find products using SKUs, part numbers, and other structured identifiers.

With this foundation in place, we began our journey toward AI-powered search to enhance recall and relevance for more complex queries. We integrated AI-driven reranking for head terms, leveraging machine learning to reorder results based on behavioral signals. Recognizing the limitations of strict lexical matching, we introduced semantic expansion using synonym models, improving query understanding and recall.

To enhance the search experience further, we built a typeahead model, making real-time query suggestions more intuitive and personalized. As our AI capabilities matured, we implemented KNN search, enabling vector-based retrieval to surface results that go beyond traditional term matching. To fine-tune ranking, we introduced Learning to Rank (LTR), optimizing results based on click-through rates and conversion signals.

Our journey from standing up our first traditional keyword search solution to AI-powered retrieval has transformed our search experience, but it hasn’t been without challenges. We’ll share key lessons from this evolution—balancing precision vs. recall, managing infrastructure complexity, and evaluating AI-driven improvements. This session will provide a practical roadmap for search teams looking to transition from keyword-based search to AI-enhanced discovery in an eCommerce setting.”

Jon Vivers

Zoro