Semantic Search

Semantic search is an information retrieval approach that interprets the meaning and intent behind a query—rather than matching literal keywords—to surface results that are conceptually relevant even when they do not share vocabulary with the search terms. Semantic search systems typically represent queries and documents as dense vector embeddings in a shared high-dimensional space, where proximity reflects semantic similarity. This allows the system to understand that a query for "running shoes" is relevant to results containing "athletic footwear" or "jogging sneakers," and to handle natural language questions, synonyms, and conceptual relationships that keyword search cannot bridge.

Semantic search is transforming product discovery, enterprise knowledge retrieval, and customer service in commerce contexts. In product search, semantic understanding reduces zero-result rates for natural language queries, improves matching on descriptive or category-level searches, and enables intent-aware ranking that considers what the customer is trying to accomplish rather than just which words they used. In enterprise deployments, semantic search over internal knowledge bases, documentation, and historical data allows employees to find relevant information by describing their need in plain language rather than guessing the exact terminology used in the source document. As organizations adopt retrieval-augmented generation (RAG) architectures, semantic search becomes the retrieval backbone that determines what context AI models receive—making its quality directly upstream of the accuracy of AI-generated answers.