Knowledge Graph AI

A few years ago, when I came across Stardog, I saw a new foundation AI technology emerging—where knowledge graphs would become a critical component for AI-driven decision-making. While it is fantastic to see Stardog making the Fast Company’s Most Innovative Companies of 2025 , it is more important to see the maturation of organizational understanding of the importance of knowledge graphs.

Knowledge Graph is a foundational technology in AI

One of the big challenges in AI today is hallucination—when AI generates information that sounds plausible but isn’t actually true. That’s a problem when businesses and government agencies need reliable, auditable insights. Stardog tackled this issue early, building knowledge graphs and inference systems with grounded data models and semantic layers as part of the AI stack.

Before the arrival of GenAI, knowledge graphs were sort of the forgotten relatives in the enterprise data/AI landscape. What Stardog built wasn't really considered AI systems by most investors. "Knowledge graphs? That's boring and we have databases!"

Fast forward to now, things look very different. AI systems need trusted enterprise data and, more importantly, ground truth knowledge, to feed the data-hungry foundational models. Knowledge graphs address this enterprise data problem at the highest and most elegant manner by linking different data points together to create representations of knowledge that can be queried and reused. AI is about creating representations and infer from representations of existing knowledge. While LLMs have learned huge amount and know how to represent commonly known knowledge, LLMs do not have enterprise knowledge. Stardog's customers have enterprise knowledge graphs, and they use that to enable LLMs in many ways. knowledge graphs are more foundational than LLMs in the AI stack.

The knowledge graph AI systems that Stardog has built for clients don’t just generate responses but ensure those responses are backed by an organization’s own trusted data, without the complexities and dependencies that come with RAGs. Stardog's semantic parsing layer that ensures AI-generated insights are anchored in verified information. This shift—from AI as a black box to AI as a transparent, fact-based tool—represents the kind of innovation that makes generative AI usable in high-stakes environments.

NASA and the Shift Toward Knowledge Graph AI

Stardog’s long-standing relationship with NASA is good example. Their work started in 2006, developing data analytics tools, and evolved into critical applications that supports the Artemis Orion Spacecraft certification. More recently, Stardog's knowledge graph AI technology became part of NASA’s Gateway program, helping manage risk and improve decision-making for the upcoming lunar-orbit space station.

NASA’s use of knowledge graphs highlights a broader shift: AI isn’t just about automation—it’s about connecting, structuring, and making sense of vast amounts of information in a way that humans can trust in the most critical moments. There is no margin for error. That’s why organizations like the U.S. Department of Defense and Fortune 500 companies are also turning to knowledge graph AI.

The Bigger Picture: Knowledge Graph AI and Agents

In order to build AI agents that can perform complex tasks autonomously, it needs knowledge. I posit that one of the most effective ways to enable agents and their knowledge is through Knowledge Graph AI where a unique knowledge graph or part of the knowledge graph is associated with a unique agent. This creates an efficient compute paradigm for scaling network of knowledgeable agents (pun intended) and enable them to "connecting the dots" through network of knowledge graphs.