The Problem: Raw Text Lacks Meaning for Machines

Traditional text processing systems struggle with understanding context, meaning, and relationships between concepts. This creates significant business challenges:

• Search systems returning irrelevant results because they don't understand meaning
• Content recommendation systems failing to identify truly similar content
• Classification systems requiring extensive manual tuning and maintenance
• AI applications lacking deep semantic understanding of text input

Cohere Embeddings solves this by converting text into rich vector representations that capture semantic relationships, enabling machines to process language more like humans do - understanding context, similarity, and meaning at scale.