The Challenge: Making Text Meaningful to Machines

Business data is increasingly text-heavy - emails, documents, chats, social media, and product descriptions. But machines can't inherently 'understand' this unstructured data, creating significant bottlenecks:

• Inability to effectively search through large text repositories
• Challenges in categorizing and organizing text data
• Difficulty detecting similarities between documents
• Limited ability to build intelligent text-based systems

Embeddings solve this by converting text into numerical vectors that preserve semantic relationships. When text is represented as vectors, computers can perform mathematical operations to determine meaning, similarity, and relationships - unlocking the value hidden in your text data.