Jans Aasman

In a typical enterprise with hundreds to thousands of applications and databases it becomes nearly impossible to have your agents run around your IT infrastructure in an ad hoc fashion and generate trustable conclusions from federated cross-database joins.

The rapid success in extracting 'de-hallucinated' graphs from Large Language Models (LLMs) marks a big step forward in AI. Knowledge Graphs, now the industry standard for knowledge-intensive applications in enterprises, are at the forefront of this progress.

The majority of the Knowledge Graphs we are building for and with customers contain massive amounts of unstructured data, usually in the form of informal natural language.

Enterprises are subscribed to the power of modeling data as a graph and the importance of using Knowledge Graphs for customer 360 and beyond. The ability to explain the results of AI models, and produce consistent results from them, involves modeling real-world events with the adaptive schema consistently provided via Knowledge Graphs.

This talk is based on Jans' recent article for Forbes magazine. "There’s a fascinating dichotomy in artificial intelligence between statistics and rules, machine learning and expert systems.

We describe a real world Intelligent Sales Organization that uses graph based technology for taxonomy driven entity extraction, speech recognition, machine learning and predictive analytics to improve quality of conversations, increase sales and improve business visibility.