An embedding is a list of numbers, a vector, that captures the meaning of a piece of text, an image, or other content. Things that mean similar things end up close together in this numeric space, which is what makes meaning computable.

This is the foundation of semantic search and RAG. By comparing how close two embeddings are, a system can find relevant documents even when they share no exact words, matching on intent rather than literal phrasing.

arosplatforms uses embeddings to turn your documents, tickets, and records into a searchable knowledge layer. Choosing the right embedding model and keeping it consistent across your data is a quiet but critical decision we make so retrieval stays accurate over time.