With bots that scan all your data sources and incorporate them into a knowledge fabric, implementation goes super quickly.
Whole-Brain AI
Empathi AI uses a large network of concepts to understand and interpret natural language, helping to clear up any confusion. Each concept is connected to related ideas, showing how words come together to represent events and objects. Our approach captures both clear and subtle meanings, assigning confidence levels to each piece of knowledge based on its relevance.
While neural networks are great for simple tasks like speech and text recognition, they struggle with more complex issues and require a lot of computing power. This is why Large Language Models (LLMs) can be very costly to develop and run—they try to use basic patterns for complicated results.
Our tools will also incorporate music, film, and storytelling to enhance the insights we provide for those with complex needs.
The fabric is woven from knowledge graphs that describe relationships between concepts in the context of the area of knowledge (finance knowledge, product knowledge, troubleshooting knowledge) in which they are meaningful and a catalog of information assets that contain the answers. Our knowledge fabric includes meaning-mapped ontologies showing the taxonomy of actionable knowledge, vocabulary and glossary, along with full-text indices of digital assets for providing bibliographic source information to back up answers.
