Embeddings
Embeddings are numerical representations of text, images or other data that capture meaning, so an AI can measure how similar two things are. Words and documents with related meanings sit close together in this vector space — making embeddings the foundation of semantic search and RAG.
Last updated: June 2026
Embeddings are what let an AI find the right paragraph among thousands of documents, even when the user's wording doesn't match the text exactly. The choice of embedding model directly affects retrieval accuracy, latency and cost.
We select and tune embedding models for your language and domain — including Arabic — so semantic search actually understands your content.
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