Searching by semantic meaning using embeddings: numerical representations of text that capture meaning.
Products, questions, and policies are converted to high-dimensional vectors by an embedding model. A query is converted the same way. The search finds the closest vectors in space. This is how 'gift for dad' can return tools, grilling gear, or headphones without any keyword overlap.
Connect your products and watch the salesperson handle shopper questions on your catalog.
Your dashboard shows what each conversation led to. After about 100 free conversations, your own numbers decide.