In depth
Semantic search uses a language model to convert a shopper's question into a numerical representation (an embedding) that captures meaning, then compares it to the same kind of representation for every product in your catalog. The closest matches in meaning surface, regardless of whether the shopper used the same words as the product description. This solves the classic keyword-search failure mode where a query like 'gift for a 10-year-old who loves dinosaurs' returns nothing useful because the catalog doesn't literally contain those words. Emporiqa runs semantic search over your synced catalog, then a reranking model reorders the top candidates so the closest match to the question comes first.
See also