Embeddings
Embeddings are numeric representations of text or other data that place items with similar meaning close together so machines can compare them.
Embeddings are lists of numbers that represent the meaning of a piece of text, an image, or other data. A model converts each item into a point in a shared space, where items with similar meaning land close together and unrelated items land far apart.
This matters because computers compare numbers easily but not raw words. Once text is turned into embeddings, a system can measure how similar two pieces of text are by how close their points are, which is the basis for search by meaning and for matching questions to answers.
In context
In customer support, embeddings let a system find the right help article or past answer even when the customer's wording does not match the source text. A question about "money back" can still surface a "refund policy" article because their embeddings sit close together.
They also power grouping and deduplication, such as clustering similar tickets together or spotting that a new question has already been answered before, which keeps a knowledge base tidy and replies consistent.
How Auralis uses Embeddings
Auralis turns each client's help content into embeddings so its done-for-you service can match customer questions to the right answer by meaning, which keeps automated replies accurate and supports a +26% CSAT lift.