AI-powered search that understands meaning, not just keywords
Text is converted into high-dimensional vectors (1,536 dimensions) that capture semantic meaning. These vectors enable semantic retrieval — the retrieval layer used in Retrieval-Augmented Generation (RAG) systems.
When you search, your query is also converted to a vector. We find documents with the highest cosine similarity—meaning the closest conceptual match.
Traditional search requires exact word matches. Semantic search understands that "ancient trickster" relates to "fox spirit" even without shared words.