An embedding is a list of numbers — usually 768 to 3,072 of them — that captures the semantic gist of a chunk of content. Two pieces of text with similar meaning produce vectors that point in similar directions, which is how "find documents related to this question" works under the hood of every modern search.
For agencies, embeddings are the quiet workhorse behind useful AI features: similar-case-study search across pitch archives, deduplicating audience research, finding internal docs related to a brief. They are far cheaper to compute than generation and far more reliable.
The model that produces the embeddings matters. Two different embedding models cannot be mixed — vectors from one will not align with vectors from another. Pick one, commit to it across your corpus, and budget for the work to re-embed everything if you ever switch.