What it is
An embedding turns a word, phrase or document into a list of numbers. Texts with similar meaning end up close together in that space, even when they use different words.
Why it matters to an agency
They are the base of semantic search and RAG: they let you find the right document by meaning, not by exact word match.
Example
Searching for "how do I cancel my plan" and finding the article "managing your subscription", even though they share no words.
By the numbers
Typical embedding models produce vectors of 384 to 3072 dimensions (OpenAI's text-embedding-3-small uses 1536). Unlike keyword search, which demands an exact match, embedding search finds "unpaid invoice" when the user typed "customer owes me money". Generating the embedding for 1 document takes milliseconds and costs cents per 1000 pages: the real cost is keeping the index up to date.