Semantic search

Semantic search uses vector embeddings to find content by meaning rather than by exact keyword — the retrieval technique behind modern brand-context fetching in AI marketing tools.

Traditional keyword search matches strings: searching for "carousel" finds posts containing the word "carousel". Semantic search converts every piece of content into a vector (a list of numbers capturing its meaning) and finds similar vectors. A search for "carousel" returns posts about slide decks, multi-image stories, and visual sequences — even when the word "carousel" never appears.

In marketing tools, semantic search is what powers context retrieval for AI generation: when you ask the system to draft a post on a topic, semantic search finds your past posts on similar topics and includes them in the prompt. The quality of the embeddings (which model produced them) and the way matches are reranked determine whether the right context surfaces.

A generator that retrieves the right past posts produces output that builds on your existing content. One that retrieves random or off-topic posts adds noise. Semantic search is the difference.