Embeddings are numeric representations of the meaning of a text (or an image): lists of numbers in which similar content ends up close together, making it possible to search by meaning and not just by exact words.
An embedding model turns each piece of text into a vector. Questions and documents about the same subject produce similar vectors, even when they use different words. It's the basis of semantic search and RAG: the company's documents are converted into embeddings and stored in a vector database (such as Pinecone or Supabase's pgvector), and for each question the system fetches the closest passages to hand to the model.
Talk to the SINOON team and find out which agent, squad or engagement makes sense for where you are now.