Semantic search is a retrieval method that matches the meaning of a query to content, not just matching words. It uses AI models to understand intent and context, returning relevant results even when exact keywords don't appear.
How it differs from keyword search
Traditional keyword search matches exact words or phrases, so it misses synonyms, related concepts, or differently worded questions. Semantic search converts text into embeddings—numeric representations of meaning—and compares them, so 'affordable laptop' can match 'cheap notebook computer' even without shared words.
Why it matters for commerce
Shoppers rarely use the exact terms a product catalog uses, so keyword-only search loses sales when queries don't match listed terms. Semantic search improves product discovery, reduces 'no results found' pages, and helps customers find what they actually want, which directly affects conversion rates.
Where it shows up
Semantic search powers modern search engines, internal knowledge bases, customer support tools, and RAG systems that retrieve context for AI chatbots. It's often combined with keyword search in a hybrid approach to balance precision and recall.
What leaders should know
Adopting semantic search usually means investing in embedding models and vector databases, plus rethinking how content is structured for retrieval. The payoff is fewer dead-end searches and better self-service experiences, but it requires monitoring for relevance drift and edge cases keyword systems handled by accident.
Frequently asked
Is semantic search the same as AI search?
Not exactly—semantic search is one technique often used inside broader AI-powered search or chatbot systems.
Does semantic search replace keyword search?
Usually not; most production systems combine both in a hybrid setup to catch exact matches and conceptual matches.
What powers semantic search under the hood?
Embedding models convert text to vectors, and a vector database finds the closest matches by meaning.