Machine Learning
Large Language Models like ChatGPT have incredible learning abilities, but using them can be very costly, especially for businesses seeking specific outcomes. While larger organizations can afford the infrastructure needed to train and manage these models, smaller companies often struggle.
In addition to high startup costs, there’s another challenge: the “P” in GPT means it learns everything at once and can’t easily update its knowledge without starting over.
Our solution works differently. We can take in new information from various sources—like social media, manufacturing logs, or ERP transactions—and add it incrementally to our central knowledge catalog. This catalog supports self-service analytics and search, allowing Subject Matter Experts to classify new knowledge correctly.
Once properly classified, this information can be quickly matched with inquiries and used for valuable insights.
The Knowledge Catalog contains proprietary information specific to each customer and is never shared. It works alongside our General DLU Knowledge Model, integrating Empathi’s deep understanding of how different digital assets relate to one another. To ensure security, our metadata model uses tags for controlled access to data, allowing only authorized users to view specific information.
