Daniel Davis: Grounding Generative AI with Context Graphs – Episode 49

Daniel Davis
Long before Foundation Capital published their "trillion dollar opportunity" article about them, Daniel Davis had been building a platform for context graphs.
Daniel's work in complex domains like aircraft safety and autonomous vehicles, as well as his study of quantum mechanics, gave him insights that led him to explore ways to ground probabilistic AI systems in the logic and knowledge they'd need to deliver trustworthy information. He settled on context graphs as the best way to accomplish this.
Daniel was introduced to knowledge graphs by his co-founder Mark Adams, and he has immediately become an RDF evangelist, aiming to not only proselytize the tech but to also make Mark's cat Fred famous in the process.
We talked about:
his role as co-founder at TrustGraph
his work to make his co-founder Mark Adam's cat Fred famous
his diverse background in defense, autonomous vehicles, and cybersecurity
how the complexity and vast scope of compliance requirements around autonomous vehicles led to his interest in context graphs
how the arrival of ChatGPT and GPT-3, and his knowledge that probabilistic systems wouldn't be up to the task of delivering legally compliant information, served as a catalyst for his current work
how a friend's article about the Foundation Capital "trillion dollor opportunity" post led to his Context Graph Manifesto
his hypothesis, based on conversations with several friends at big consultancies, that the sudden interest in context graphs arose from executives reviewing their many failed 2025 AI proofs of concept
his definition of a context graph: "a graph structure that is optimized for AI usage"
the influence of his friend Vicky Froyen's 2019 presentation on context graphs at the first Knowledge Graph Conference
the three elements he sees in a context graph - decision traces, provenance and explainability, and feedback - and the power of combining them in a single graph system
their use of ontologies like PROV-O
the importance of a context capability in complex domains like military airworthiness
how his background in quantum mechanics and mathematics led to his awareness of the limitations LLMs from their introduction
how he balances the probabilistic nature of the universe with the needs of practical applications that entail legal obligations
his surprise at the lack of attention that a lawsuit between Amazon and Perplexity is getting, given its huge implications for AI agent systems
their goal at TrustGraph of making graph technology and ontology design easier and more accessible
a cliffhanger about the implications of LLMs not understanding time
Daniel's bio
From military aerospace, space-to-air-to-sea mesh networks, autonomous vehicles, and enterprise infrastructure, Daniel has made of career of making the most complex systems work together. Whether it's cyberphysical systems or data, interoperability and guaranteed performance have always been top priorities with a mission-first mindset. Co-Founding TrustGraph represents a multi-decade quest to improve decision making through access to better knowledge.
Connect with Daniel online
LinkedIn
X
Resources mentioned in the interview
TrustGraph.ai
TrustGraphAI YouTube channel
Context Graph Manifesto
Collibra's Context Graph, Vicky Froyen's 2019 Knowledge Graph Conference presentation
Video
Here’s the video version of our conversation:
https://youtu.be/npjErvR7oXY
Podcast intro transcript
This is the Knowledge Graph Insights podcast, episode number 49. When Foundation Capital published their article about the trillion-dollar opportunity presented by context graphs, many people were hearing about the concept for the first time. Not Daniel Davis. He's been developing an open-source context graph platform since 2023. His work in complex domains like aircraft safety and autonomous vehicles, as well as his study of quantum mechanics, have led him to explore ways to ground probabilistic AI systems in logic and knowledge.
Interview transcript
Larry:
Here we go. Hi everyone. Welcome to episode number 49 of The Knowledge Graph Insights podcast. I am really delighted today to welcome to the show Daniel Davis. Daniel's the co-founder and co-creator at TrustGraph, which is an open-source software project that builds graph stuff that we'll talk about today, based in San Francisco. And welcome to the show, Daniel. Tell the folks a little bit more about what you're up to these days.
Daniel:
Oh, wow, Larry. That's a lot to unpack there. I mean, how much time do you have? Yes, I am the co-creator of TrustGraph with Mark Adams, who is a bit more well-known in the graph community than me, but he likes building graphs. He doesn't like talking about them so much. And I'm confident that he would agree with me on that. Although I am trying to make his cat Fred famous, because I'm actually working on a new video on our guide to understanding RDF, which is something that a lot of people have asked us about, and how Mark taught me RDF so many years ago with three simple sentences about his cat Fred. But TrustGraph is what we've been working on for the past few years now. And we've had a couple of different ways of trying to explain it to people, whether it's a context operating system, context development platform.
Daniel:
Some might even think of it like a context science platform, which I think is kind of an interesting analogy as well. But I myself have quite a diverse background, spent a lot of time in DOD aerospace, came out to Silicon Valley almost 10 years ago to work on the autonomous vehicle industry, focusing on cybersecurity and safety. And that's why I write articles about things like determinism and information risk and trying to attribute value to information. But in that world, I also was doing complex knowledge work where you read one document that's 800 pages long, and then you have to read a statement that references another document, or maybe it references 12 other documents, and you just keep tracing down this chain of references, and then you have to understand which one of these documents actually takes precedence. Why did these statements conflict with each other?
Daniel:
Do they conflict with each other? How do I try to come to some sort of opinion about this? And in the safety critical world, opinions aren't allowed. It's not like auditing for enterprises that you can have opinions. They take a much grimmer view on that. And that's where that word determinism comes in and whether determinism means what people think it means. And how is that for an introduction?
Larry:
Well, it's perfect, because it sets up all the things we want to talk about. The first thing I want to talk about, I think, well, it's so hard to choose, but the reason you came to my attention is, I forget, somebody ... Oh, my friend Jochen in Munich brought you to my attention. And I was like, "Whoa, this guy's been talking about context well before December of 2025," which is when apparently the rest of the world started thinking about context and context graphs. Tell me a little bit about maybe the story of your connecting with Vicky or however. I mean, that combination, we were talking before we went on the air about your experience with autonomous vehicles, discovering Vicky and his interest in context graphs. And then a lot of what you just said is a reason to need not the context graphs to do the stuff you want to do. So, maybe talk a little bit about your journey into the context realm.
Daniel:
Well, so much of this comes from the problem I was trying to solve in the autonomous vehicle world. This is work that I've been doing for years in DOD aerospace with risk management and cybersecurity and safety, and just running complex programs. It's so much about the paperwork and how you make decisions, how you justify those decisions, how you comply with regulations, understanding the regulations. And for autonomous vehicles the scope was just unprecedented when you look at the number of things that could go wrong. And we could literally talk for the next few days, just me rattling off scenarios, and you'll go like, "Wow, I never thought of that. I never thought of that. Wow, wow." And you just start going like, "How do you manage this?" And well, that was what I was sought out. That's what I was having to solve. And looking at all the different ways of doing this and trying to combine a Bayesian approach with risk management and realizing the data sets were going to be huge and how do you manage that.
Daniel:
And it kind of turned out to be an unsolvable problem at the time. And around that time, because I was working at Lyft, I got brought up to manage a lot of the issues that were going on with the Lyft actual IPO, which again, more regulatory stuff with the SEC and how processes are applied across the entire enterprise, how these comply with SEC regulations and expectations and how this was audited. And just even how we were measuring our cybersecurity performance as a company, how that was getting reported to the board. Again, very similar problem, just slightly different problem space, slightly smaller scope. And around that time Mark's company, Trust Networks, was actually acquired by Lyft, and I met him and I got introduced more to graphs and knowledge graphs. I actually hadn't even worked with knowledge graphs prior to that. I was much more in deterministic structures and DOD aerospace.
Daniel:
I was the one always saying, "Why are we writing in this Python? We should write it all in Ada." And all the people would just look at me and go, "What is Ada?" And I would do that just as a joke, but also partially believing it. I still advocate Ada. I like Ada, even if it makes developers cry. It was designed to make developers cry, because it always works, but that's another story. And that was back in what, 2018, 2019?...