Embeddings
A numeric vector representation of text (or other data) positioned so that items with similar meaning fall close together in vector space.
In more detail
An embedding turns a piece of content into a list of numbers arranged so that meaning maps to position. Two passages that say similar things end up near each other in the vector space, even if they share no words. This is what lets software compare content by meaning rather than by exact keyword match.
Embeddings are the backbone of semantic search and retrieval-augmented generation. To find material relevant to a question, you embed the question and look for the nearest stored embeddings. The quality of that retrieval sets a ceiling on how grounded any downstream generation can be.
Where this shows up at Ceven
Ceven uses embedding-based retrieval so research and knowledge steps can pull material by meaning rather than exact wording. When a workflow needs to ground an answer in the customer's documents or in gathered sources, semantic retrieval finds the relevant passages, and the model reasons over those, keeping the output tied to real material.