How can high-reliability organizations transform their data from flat, 2D spreadsheets into a dynamic, 3D world of interconnected insights? In the Season 4 premiere of the Talking AI podcast, host Matt Paige dives deep with Jon Brewton, Founder & CEO at data², and Daniel Bukowski, CTO at data², to explore how leveraging knowledge graphs and AI is revolutionizing data transparency and explainability.

Jon and Daniel share their journey from traditional analytics to building a modular, cloud-agnostic, zero-trust AI platform that empowers industries such as defense, intelligence, energy, finance, and healthcare. They break down the core concepts of graph databases versus relational systems, discuss how graphs provide the connective tissue needed to support AI-driven decision making, and reveal how integrating LLMs with structured graph data can overcome challenges like hallucinations and unreliable outputs.

Learn how data² is transforming complex data into contextualized, three-dimensional insights that not only answer hard questions but also elevate the work of analysts and decision-makers. Whether you’re curious about how to improve data reliability or interested in the future of AI-augmented workflows, this episode is packed with practical insights and forward-thinking strategies.

Key Moments:

The data² Journey: From Flat Data to Graphs
Demystifying Knowledge Graphs & Their Value
Graphs vs. Relational Databases: What’s the Difference?
Integrating LLMs with Graph Data for Better Accuracy
Transforming 2D Data into 3D Insights
Real-World Use Cases in High-Reliability Industries
Empowering Teams with Transparent, Explainable AI
The Future of AI-Driven Analytics

Key Links:

Transforming Data from 2D to 3D with Graphs and AI