This conversation examines graph databases and their role in shaping artificial intelligence. Emil Eifrem of Neo4j, founder and CEO, appears on theCUBE Research's NYSE Wired program Mixture of Experts and GraphTalks in Town with hosts John Furrier of theCUBE Research and Dave Vellante of theCUBE Research. Eifrem draws on two decades of experience building Neo4j and explains why knowledge graphs are an information-dense representation for context, how graph traversal accelerates discovery and the role of ontologies and Graph Query Language GQL in mapping business intent to enterprise data. They highlight explainability, governance and Neo4j innovations such as Virtual Graph, which allows queries across Snowflake and Databricks without data movement.
Eifrem argues that graphs provide contextual retrieval that reduces AI hallucinations and enables agents to reach production-grade accuracy. They underscore practitioner-led adoption and the emerging role of graph as connective data infrastructure for enterprise AI, with implications for explainability, governance, performance and enterprise data strategy.
Forgot Password
Almost there!
We just sent you a verification email. Please verify your account to gain access to
theCUBE + NYSE Wired: Mixture of Experts Series. If you don’t think you received an email check your
spam folder.
Sign in to theCUBE + NYSE Wired: Mixture of Experts Series.
In order to sign in, enter the email address you used to registered for the event. Once completed, you will receive an email with a verification link. Open this link to automatically sign into the site.
Register For theCUBE + NYSE Wired: Mixture of Experts Series
Please fill out the information below. You will recieve an email with a verification link confirming your registration. Click the link to automatically sign into the site.
You’re almost there!
We just sent you a verification email. Please click the verification button in the email. Once your email address is verified, you will have full access to all event content for theCUBE + NYSE Wired: Mixture of Experts Series.
I want my badge and interests to be visible to all attendees.
Checking this box will display your presense on the attendees list, view your profile and allow other attendees to contact you via 1-1 chat. Read the Privacy Policy. At any time, you can choose to disable this preference.
Select your Interests!
add
Upload your photo
Uploading..
OR
Connect via Twitter
Connect via Linkedin
EDIT PASSWORD
Share
Forgot Password
Almost there!
We just sent you a verification email. Please verify your account to gain access to
theCUBE + NYSE Wired: Mixture of Experts Series. If you don’t think you received an email check your
spam folder.
Sign in to theCUBE + NYSE Wired: Mixture of Experts Series.
In order to sign in, enter the email address you used to registered for the event. Once completed, you will receive an email with a verification link. Open this link to automatically sign into the site.
Sign in to gain access to theCUBE + NYSE Wired: Mixture of Experts Series
Please sign in with LinkedIn to continue to theCUBE + NYSE Wired: Mixture of Experts Series. Signing in with LinkedIn ensures a professional environment.
Are you sure you want to remove access rights for this user?
Details
Manage Access
email address
Community Invitation
Emil Eifrem, Neo4j
This conversation examines graph databases and their role in shaping artificial intelligence. Emil Eifrem of Neo4j, founder and CEO, appears on theCUBE Research's NYSE Wired program Mixture of Experts and GraphTalks in Town with hosts John Furrier of theCUBE Research and Dave Vellante of theCUBE Research. Eifrem draws on two decades of experience building Neo4j and explains why knowledge graphs are an information-dense representation for context, how graph traversal accelerates discovery and the role of ontologies and Graph Query Language GQL in mapping business intent to enterprise data. They highlight explainability, governance and Neo4j innovations such as Virtual Graph, which allows queries across Snowflake and Databricks without data movement.
Eifrem argues that graphs provide contextual retrieval that reduces AI hallucinations and enables agents to reach production-grade accuracy. They underscore practitioner-led adoption and the emerging role of graph as connective data infrastructure for enterprise AI, with implications for explainability, governance, performance and enterprise data strategy.
>> Palo Alto studio connecting Silicon Valley and Wall Street. I'm John Furrier, co-hosting theCUBE here with Dave Vellante, my co-host. Here at theCUBE's NYSE studio, of course, we have our Palo Alto studio connecting Silicon Valley to Wall Street. This is our New York Stock Exchange Wired program, NYSE Wired program and community with theCUBE. Of course, we've been covering the Mixture of Experts series. Of course, Graph Talks in Town with Neo4j. We're here with the CEO and founder, Emil Eifrem, my friend of theCUBE. Great to see you. Thanks for coming on. Been a while.
Emil Eifrem
>> Great to be here, John.
John Furrier
>> We've known each other since 2007 when, Facebook was just starting to talk about social graph and they would have events, but such a great success for your company and your team. Graphs are now infrastructure. Data is infrastructure. AI needs real-time. You guys have been great success. So first, start with some of the momentum. Of what you guys have. Obviously you're in town for the GraphTalk. I'll be hosting a bunch of interviews from a lot of graph practitioners who are leading the market, by the way. But get into what the momentum is you have right now.
Emil Eifrem
>> Yeah, like you mentioned, we've known each other for a few years now, 20 years. So we were, I think, 25 when we got to— yeah, yeah, something like that, 16, 17 years. and it's been like just a fantastic journey, right? We started out by saying Hey, there's got to be a better way to represent information and work with data, not force it to be squeezed into square and static tables. The world is dynamic. The world is always changing. Figuring out how things fit together felt like a worthwhile, important place to pursue in terms of using data. So that's how we started, right? Here we are now, 20 years later, hundreds of millions of dollars of revenue, billions of dollars of valuation and all that kind of stuff. But really what's exciting now is what's happening with graphs and AI. And honestly, John, it's like this match made in heaven.
John Furrier
>> I mean, it's a computer science match to recursive nature of the graphs. AI speaks graph, math is graph.
Emil Eifrem
>> It's exactly right. And I think what's happening is that the graph representation of data, so organizing your information in nodes and the relationships between them, and key-value properties on both ends up being this very information-dense or knowledge-rich way to represent your data. This is why in 2012, 5 years after you and I met, Google launched their Knowledge Graph. And this is the thing that we now take for given. When you search for New York City, on Google, you're gonna get a side panel with information about New York, and then you can click through to the mayor, you can click through to its state, New York State, which is in the country of the US. That's all on the back end powered by their knowledge graph. They call it the knowledge graph just because it's this amazingly rich way of representing information, right? And so that is this heavenly match with the stochastic transformer-based AI models, which really is a 1+ 1= at least 3. And we see that happening up and down the AI stack. And I'm very happy to talk you through some of the key technical patterns and use cases.
John Furrier
>> Yeah, I want to get to the— I want to get into the tech, but I also want to keep zoomed out because a lot of people who are seeing graphs now for the first time are like, oh, magic's happening. But there's a lot of people who have worked hard over the years to get here. And I mentioned Facebook at the beginning, you mentioned Google a few years later. If you remember the social media evolution, social graph was the concept. We all use LinkedIn. Some people still use Facebook and others, but that was based on graph concepts and the power that came out of those monster companies, specifically Facebook, is now available to every company. So most people think, oh, Facebook has this graph. They were highly efficient in targeting with advertising, obviously, but they own the data. But now that it's opened up, it's become democratized. You guys really drove that. Talk about that impact to the market because as you said the AI world is now going AI native. Everyone I talk to that's AI native have multiple databases and a graph. I won't say overlay, but I think that's the wrong word, but graph connected. Talk about that dynamic, because now everyone could have the power of Facebook, but
John Furrier
>> without—right.
John Furrier
>> And done, done right. Yeah.
Emil Eifrem
>> Yeah. Yeah. And I think maybe the first 10, 15 years of this company was taking what is in Silicon Valley. You have a generation of— remember Web 2.0 was the term right at the time, right? So this is like Facebook and LinkedIn, but also some Web 1.0 companies like eBay and PayPal. You look inside of the machinery of them, it's all based on graph technology, right? But if you're in the enterprise outside of Silicon Valley, it was very hard— or a small startup— it was very hard to get access to that technology. We democratize access to that. We gave the same platform that Google was built on, like on tap for the big financial services, the big telecom companies, the big life science companies. Right.
John Furrier
>> A lot of people talk about AI as being bad and there's bad narratives out there, oh, it's gonna kill us, the Terminator, Skynet. But if you think about where we are from a tech perspective, there are the giants that built the AI generation besides the algorithms that came out to make AI work were pioneers, Uber, Neo4j. They had to build the stuff from scratch, right? But there was a lot of work done. So this is building on the shoulders of those giants. This is the next era. There's a lot of work that's gone into it. Okay, that's known. So there's a lot of domain body of work done to— that's in the AI era. So now explain why that's important when people start thinking, okay, how does AI work better? How do I get contextually relevant information as context? How is it safe? Because people just don't understand that it's there. They think two kids in a dorm room started AI and it's gonna take over the military-industrial complex and kill everyone. Which, technically WarGames is a scenario, but we don't know. But it's kind of a fantasy. Talk about the reliability and the efficacy of where the AI generation is.
Emil Eifrem
>> Yeah. At this point, if you look at the current generation of AI models, but even the prior generations, even the ones that released, call it a year ago, which feels like Stone Age these days, right? Honestly? They're phenomenal. The intelligence in those models is just— if that's all that we did as a society, it would take decades to just diffuse that technology throughout the world and we would get a ton of value from it. When I look across our customer base today, we're not bottlenecked by model intelligence. We're bottlenecked on what context do we give the models. It's that classic thing of the model is like a PhD in some narrow area that goes into work every day and has to be taught and learn everything from scratch, right? Because they don't know anything about your company, right? And so the game now is about how do we give that model the right contextual information? And this is where everyone is circling around the same thing. It turns out that context— what is context? Context is my context is how I relate to the rest of the world. I grew up in Sweden. I'm the CEO of Neo4j. I have 3 young kids. I'm married to Madeline. I drive a Volvo stereotypically since I'm Swedish.
John Furrier
>> Of course, right?
Emil Eifrem
>> Of course, right? So that is my context. It's very graphic. It's like how I relate to the rest of the world. So everyone is converging on this approach to giving the AI models the right information at the right time.
John Furrier
>> I said on theCUBE, you got to feed the beast in a way. You got to feed the AI. But it's— you don't know the context. Static web was database, search, get results. That was a search paradigm. We're not living in a search paradigm, but we are living in a discovery paradigm right now with agents. Explain this, because search and discovery has been one of the categories. Go back to the early days of the web. Search engines had a discovery mechanism, keyword, get results, whatever, click on a link, navigate to a page. Now with AI, you still need that discovery layer, but you need it fast and accurate.
John Furrier
>> Yes.
John Furrier
>> Talk about that piece because people see agents going off the rails, hallucinations. They might not understand that there actually is a solution.
Emil Eifrem
>> Yeah. So let's tee off of what we just said, which is the real game here is giving the super smart AI models the right context at the right time. If you think about that, that in computer science is called IR or information retrieval. It's a retrieval problem, right? And so on some level it is. Let's say that you're a human being, you contact customer support. Let's take the classic AI enterprise use case, customer support, right? So John contacts some provider of yours, right? And you say, I need help with my Wi-Fi. Let's say your Wi-Fi isn't working and you describe it, say, this is the one that I bought. Or maybe the system knows it already because it knows who you are. right? And it says, these LEDs are flashing yellow and my Wi-Fi is shaky, right? Okay. The agent has to take that natural language that you typed in or that you said over voice. It needs to go from that intent to let's find the, call it, top 10 documents across my entire corpus of support documents inside of this company. Give that to the model at the right time to answer that question. Now, if you think about that problem, it turns out if you take a step back, as humanity, we've solved that problem before. The problem is the same as you go into a search box on the web, you search, right? Find me the top 10 blue links. And you and I are, sadly old enough to remember that there is a world pre-Google where AltaVista, Lycos, Yahoo!, Excite.
Emil Eifrem
>> Yeah, yeah.
Emil Eifrem
>> These days people don't even know those names. There's tons of search engines, right?
John Furrier
>> All dead except for Yahoo!, basically.
Emil Eifrem
>> Exactly. And one of the problems was people call it the AltaVista effect. You search for a result and you're gonna get a million results, but the top 10 best ones were maybe on page 99 or 433, right? Then Google came along and they said, you know what, I'm gonna search exactly to your point But then I'm going to rank the search result based on what? Based on the graph. That's the PageRank algorithm. How the documents are linked on the web. So that exact same approach is what the enterprise is now using to get the reliability of the retrieval.
John Furrier
>> And the results of PageRank, by the way, the ranking technology called PageRank created massive wealth for Google. Huge. They created a whole ad industry for online. But what does it mean for enterprise? Because this comes up a lot. I've done a lot of interviews with some of your practitioners as well as other graph enthusiasts. They're all having an experience of almost like a superpower, but they weren't in the organization pecking order, and now they're moving the needle on the business and they're being elevated up because they just discovered, it's like a caveman discovering fire. It's like they get pushed right to the top. This is where you start to see the graphs. Why is that happening?
Emil Eifrem
>> Yeah, it's spot on. Although I'm not going to describe my customers as cavemen. So that was fire.
John Furrier
>> Oh, the wheel. The wheel was revolutionary.
Emil Eifrem
>> Yeah, fair enough. I'll take that part of the analogy. Right. So the key thing here is what's called accuracy.
John Furrier
>> Yeah.
Emil Eifrem
>> And accuracy is the inverse of hallucination. the higher accuracy that you have in your AI system, the fewer hallucinations you do. Right. And there's a threshold that people talk about as the escape velocity for accuracy. And when people use graph as part of their agents, they reach this escape velocity where it actually works in production. And that's the key thing. Every single big organization right now is on this AI transformation journey. But if their agents can't retrieve the right data at the right time, they'll never have accurate enough answers for it to be able to be used in production. So that's the superpower that our champions get by using graphs as part of their agenda.
John Furrier
>> And by the way, it solves a lot of hard problems they've been grinding on. And other mechanisms. All right. Talk about the impact of the AI infrastructure because a lot of database, a lot of software, especially, you know, control layer software or connective tissue, whatever you want to call it, glue layer, people call it, was kind of constrained by the fact that you need a lot of compute. So now you're in an era where the new architecture on whether you have an AI factory, you have unlimited capabilities from a horsepower standpoint, you now have engines that can pump out tokens, which is now currency. Those tokens have changed the game on how data is interacted with. How has that affected graphs specifically? Because this is where I think the AI connection to the computer science of AI with data.
Emil Eifrem
>> Yeah. So there's two sides to that coin. The first one we've talked about, which is how graphs in Neo4j are embedded in our customers' AI systems to help them become better. We've talked a lot about accuracy. There's also an explainability and governance and transparency angle that is really relevant and important too. But we can get to that in a moment. So that's one side of the coin. The other side of the coin is if you look at graph technology, there are basically two big areas of friction to adopt graph technology. The first one is how do I get my data in there and how do I model my data? Let's say you have lots of data, unstructured, or it might be in your Snowflake or your Databricks or your Oracle data systems. How do you get it into the graph? That's the first one. The second one is how do you query it? Right. So there's a query language now called GQL. It's the first sibling to SQL that has been standardized by ISO ever. Right. So SQL was invented 40 years ago, standardized as the one universal database query language. GQL, the Graph Query Language, is the only sibling to that, which is just pretty phenomenal. Right. But it's a new language. You have to learn it. Well, it turns out that AI helps with both of those problems. And a modern AI model can look at your unstructured data and create the knowledge graph out of that from scratch, right? Without any human manual intervention. And then how do you query it? Well, these days you don't need to type SQL. You speak English to it, right? So that barrier to adoption has been just completely collapsed. And that's a huge part of what's driving momentum for Neo4j right now.
John Furrier
>> You know what's interesting? You brought up SQL, Structured Query Language. People don't know the acronym. That has been the standard for querying databases. I want to tie this to intent because one of the things that we've learned here on theCUBE and we see successful companies doing is they've changed the intent equation. I'll give you an example. I used to do a lot of SQL queries when I was doing co-op work in the '80s. You have to think about the business logic formulation first, then actually construct the query. The query then is my query to the databases. I need a report. Okay. Now I create the logic, then the query goes in. So I have to formulate the query. Sometimes there are huge queries. Now that intent is in the logic of the AI. So I just say I want the top sales by region or whatever my ask is. It does the logic on the other side. That wasn't possible. Explain this, 'cause this is like a game-changing shift in user experience, but also technical implementation.
Emil Eifrem
>> This one is huge. In order to pull this one off at enterprise scale, you require a technology that is absolutely fundamental, which is ontologies. And ontologies have been around forever. Aristotle talked
John Furrier
>> about—I did one in '88. I did one in '88. Taxonomy by hand.
Emil Eifrem
>> Yeah, exactly. Tom Gruber, coined the most common definition of ontologies for computer science in 1993 at Stanford. He then went on to co-found Siri, by the way, the same Tom Gruber. But really, the company that has popularized this in modern times is Palantir, right? And they started talking about ontology being their secret sauce, right? And what an ontology does, it's a graph model. So it is exactly one-to-one with what we've been doing for 20 years. And a business-facing ontology is the key asset here. And what it is, is it takes the world of your company. Let's say you're a financial services institution. You have customers. The customers have bank accounts. They have credit cards connected to them. There are derivatives. There are all the concepts that exist in your universe and how they relate. A business-facing ontology. Because it turns out that a key part of your job as an enterprise data architect today is that you have to design the world that your agents think in. And your agents think in the terminology of the business. They think customers, they think credit cards, they think accounts, they think insurance plans, that kind of thing, right? And then you require a technical ontology, which is all my data sources, the physical data layer in my enterprise. I have an Oracle database over there, I have a Snowflake database over there, and then a mapping between the two. So all of a sudden, you know that a customer first name maps to that Oracle database with a column called F_name. They need to know that F_name is the first name of your customer. But with these key ontologies to connect them, you can do exactly what you said. Your agents can interpret intent and map that to the data that they need.
John Furrier
>> And the graphs make it faster. So talk about the— how— think about graphs. It's almost like picking a fork in the road. You can say, okay, down this lane is a series of graphs, but it makes it very efficient. the word recursion comes up a lot in graphs. graphs are nodes with an arc connected to another node, and there's data in these things, right? So that's computer science principle 101. You traverse the nodes and you go see what's in there. So take us through why that works in AI now that you have intelligence and horsepower, you have compute and all the GPUs and all the vector embeds and all the other data.
Emil Eifrem
>> So the key secret sauce here, if you take, we talked before about the knowledge graph of describing all the key concepts and how they relate and all that. You can take that data structure and you can store it in anything. You can put it in S3 buckets, you can put it in Oracle, Postgres, whatever you want, right? But what a graph database is, like Neo4j, it's written from scratch. Again, we've been at this for 20 years now, right? And we've taken every single layer in the stack of the database and we've optimized it for exactly what you said, which is traverse this deeply connected web of information at lightning speed. So we are frequently, because it's perfectly—
John Furrier
>> And it's called a knowledge graph basically these days, right?
Emil Eifrem
>> Exactly. And it is frequently not even 1,000 times, but 1 million times faster than if you put it in a classic relational database, which is great at many things. It's just not great at traversing this deeply connected data that an agent requires in order to answer.
John Furrier
>> And they're bounded by latency too, because their accuracy is only as good as the best data possible.
John Furrier
>> That's exactly right.
John Furrier
>> All right, let's talk about the momentum in the company. Obviously great success. Love the tech angles. I think every company wants their own data moat. They want a Palantir-like environment. All the smart money and smart people are using graphs with other databases. Talk about the momentum in the company. You mentioned the rev— some of the revenue figures. Can you quote the numbers? Can you share some of the momentum, where you guys are at, and what's your focus now?
Emil Eifrem
>> Yeah, so we're here at the New York Stock Exchange. We're not yet a public company, so we don't disclose our numbers publicly. Suffice to say, we're hundreds of millions of dollars of revenue. And just to give you a sense of the momentum, 2026 is off to a flying start in Q2 of this year. So we're recording this in mid-September. So our last quarter was Q2 of this year. We generated about as much revenue in just that single quarter as all of 2025 combined. Right. So that gives you just
John Furrier
>> a—so you guys have a flavor of the velocity.
Emil Eifrem
>> It is really taking off and there's this widespread recognition that AI and graph is this, again, a match made in heaven.
John Furrier
>> Talk about the community that's developing around graphs. I think this is super fascinating because the people who are doing graphs were early adopters because they saw the value and it's almost like they are discovering the superpowers and it's spreading. Talk about how that's spreading in the community and what you guys are doing about it.
Emil Eifrem
>> Yeah, one of the things that I love about this company, of course, I'm extremely biased being the founder, right? But one of the things is that we've always had this practitioner-led adoption where the people who do the real work, they find us. We're open source. We have now in the cloud world, we have a free tier of our cloud offering. They self-onboard, they fall in love, hopefully, right? And they can build it themselves. Right. And so we only ever sell to people that are just champions. We never sell to start top down. Push it down into the org. Now, we, of course, at this scale, we also engage with the real technical leadership of the Global 2000, but it's based on this foundation of the people who actually sit there doing the real work, wanting to choose to work with Neo4j.
John Furrier
>> And making it easy.
Emil Eifrem
>> And that's just great.
John Furrier
>> Making it easy, I know, is always hard. Talk about the ease of use feature. How are you guys making it easier? Because we want to get our graphs going in our company. Every company I talk to is trying to figure out the brain, for their company. They've all come to the realization that, okay, we need a company brain, we need a Google PageRank, we need to have a Palantir. We have data that's valuable. How do we protect it?
Emil Eifrem
>> Well, yeah. And a brain, like even the human brain, is physically a neuron connected to another neuron through synapses. It's physically— it's a graph.
John Furrier
>> It's a graph. Yeah.
Emil Eifrem
>> And associatively, we think associatively, which is also a graph, right? Yeah. It comes back to what we talked about before. The fact that we now have AI, I spend most of my time thinking about how will this, how can Neo4j help my customers' AI applications become better? But it's also a massive superpower for us internally as architects of our own product because all of a sudden we have a way of making it so much easier to get data into the database and then query in pure English. Those two things are the main building blocks.
John Furrier
>> No one really has to give up anything to use Neo4j. They can still use their data lakes. They can still use everything else. You just connect into it.
Emil Eifrem
>> Well, and the other thing is we've also done a lot of investment in— we actually are tomorrow at our event, we're talking about a new product offering called Virtual Graph, which is super, super exciting. What Virtual Graph does is it's the entire Neo4j product platform. So all of the tools, all of the solutions running on top of it, we can talk about GraphAware later if you want to. As an example of the solutions running on top of Neo4j. All of this, all the AI capabilities, but it sits right on top of your Snowflake or your Databricks. So you don't have to move your data. It uses, in the weeds, technically it's called predicate pushdown queries. So it runs all of this directly on your petabyte data lake within minutes to get started. And so that's really exciting.
John Furrier
>> That's really onboarding fast. All right. Well, great to see you, and I know you got to go. I really appreciate your valuable time coming on theCUBE. It's been a couple of years. We have a lot of your team members on, and you got a great team. And again, graphs are just the beginning. People are starting— this will be standard, first ISO standard in the database space. Congratulations. And we'll talk more later. Thank
John Furrier
>> you.
John Furrier
>> Pleasure.All right. Founder and CEO of Neo4j. Again, the graph database is turning out to be the heart and soul, the connective tissue, the brain of organizations. And there's benefits. AI is highly compatible. You don't really have to get rid of all your data and databases to really make it happen. Of course, the results are fantastic with AI. I'm John Furrier, host of theCUBE. Thanks for watching.
>> Palo Alto studio connecting Silicon Valley and Wall Street. I'm John Furrier, co-hosting theCUBE here with Dave Vellante, my co-host. Here at theCUBE's NYSE studio, of course, we have our Palo Alto studio connecting Silicon Valley to Wall Street. This is our New York Stock Exchange Wired program, NYSE Wired program and community with theCUBE. Of course, we've been covering the Mixture of Experts series. Of course, Graph Talks in Town with Neo4j. We're here with the CEO and founder, Emil Eifrem, my friend of theCUBE. Great to see you. Thanks for coming on. Been a while.
Emil Eifrem
>> Great to be here, John.
John Furrier
>> We've known each other since 2007 when, Facebook was just starting to talk about social graph and they would have events, but such a great success for your company and your team. Graphs are now infrastructure. Data is infrastructure. AI needs real-time. You guys have been great success. So first, start with some of the momentum. Of what you guys have. Obviously you're in town for the GraphTalk. I'll be hosting a bunch of interviews from a lot of graph practitioners who are leading the market, by the way. But get into what the momentum is you have right now.
Emil Eifrem
>> Yeah, like you mentioned, we've known each other for a few years now, 20 years. So we were, I think, 25 when we got to— yeah, yeah, something like that, 16, 17 years. and it's been like just a fantastic journey, right? We started out by saying Hey, there's got to be a better way to represent information and work with data, not force it to be squeezed into square and static tables. The world is dynamic. The world is always changing. Figuring out how things fit together felt like a worthwhile, important place to pursue in terms of using data. So that's how we started, right? Here we are now, 20 years later, hundreds of millions of dollars of revenue, billions of dollars of valuation and all that kind of stuff. But really what's exciting now is what's happening with graphs and AI. And honestly, John, it's like this match made in heaven.
John Furrier
>> I mean, it's a computer science match to recursive nature of the graphs. AI speaks graph, math is graph.
Emil Eifrem
>> It's exactly right. And I think what's happening is that the graph representation of data, so organizing your information in nodes and the relationships between them, and key-value properties on both ends up being this very information-dense or knowledge-rich way to represent your data. This is why in 2012, 5 years after you and I met, Google launched their Knowledge Graph. And this is the thing that we now take for given. When you search for New York City, on Google, you're gonna get a side panel with information about New York, and then you can click through to the mayor, you can click through to its state, New York State, which is in the country of the US. That's all on the back end powered by their knowledge graph. They call it the knowledge graph just because it's this amazingly rich way of representing information, right? And so that is this heavenly match with the stochastic transformer-based AI models, which really is a 1+ 1= at least 3. And we see that happening up and down the AI stack. And I'm very happy to talk you through some of the key technical patterns and use cases.
John Furrier
>> Yeah, I want to get to the— I want to get into the tech, but I also want to keep zoomed out because a lot of people who are seeing graphs now for the first time are like, oh, magic's happening. But there's a lot of people who have worked hard over the years to get here. And I mentioned Facebook at the beginning, you mentioned Google a few years later. If you remember the social media evolution, social graph was the concept. We all use LinkedIn. Some people still use Facebook and others, but that was based on graph concepts and the power that came out of those monster companies, specifically Facebook, is now available to every company. So most people think, oh, Facebook has this graph. They were highly efficient in targeting with advertising, obviously, but they own the data. But now that it's opened up, it's become democratized. You guys really drove that. Talk about that impact to the market because as you said the AI world is now going AI native. Everyone I talk to that's AI native have multiple databases and a graph. I won't say overlay, but I think that's the wrong word, but graph connected. Talk about that dynamic, because now everyone could have the power of Facebook, but
John Furrier
>> without—right.
John Furrier
>> And done, done right. Yeah.
Emil Eifrem
>> Yeah. Yeah. And I think maybe the first 10, 15 years of this company was taking what is in Silicon Valley. You have a generation of— remember Web 2.0 was the term right at the time, right? So this is like Facebook and LinkedIn, but also some Web 1.0 companies like eBay and PayPal. You look inside of the machinery of them, it's all based on graph technology, right? But if you're in the enterprise outside of Silicon Valley, it was very hard— or a small startup— it was very hard to get access to that technology. We democratize access to that. We gave the same platform that Google was built on, like on tap for the big financial services, the big telecom companies, the big life science companies. Right.
John Furrier
>> A lot of people talk about AI as being bad and there's bad narratives out there, oh, it's gonna kill us, the Terminator, Skynet. But if you think about where we are from a tech perspective, there are the giants that built the AI generation besides the algorithms that came out to make AI work were pioneers, Uber, Neo4j. They had to build the stuff from scratch, right? But there was a lot of work done. So this is building on the shoulders of those giants. This is the next era. There's a lot of work that's gone into it. Okay, that's known. So there's a lot of domain body of work done to— that's in the AI era. So now explain why that's important when people start thinking, okay, how does AI work better? How do I get contextually relevant information as context? How is it safe? Because people just don't understand that it's there. They think two kids in a dorm room started AI and it's gonna take over the military-industrial complex and kill everyone. Which, technically WarGames is a scenario, but we don't know. But it's kind of a fantasy. Talk about the reliability and the efficacy of where the AI generation is.
Emil Eifrem
>> Yeah. At this point, if you look at the current generation of AI models, but even the prior generations, even the ones that released, call it a year ago, which feels like Stone Age these days, right? Honestly? They're phenomenal. The intelligence in those models is just— if that's all that we did as a society, it would take decades to just diffuse that technology throughout the world and we would get a ton of value from it. When I look across our customer base today, we're not bottlenecked by model intelligence. We're bottlenecked on what context do we give the models. It's that classic thing of the model is like a PhD in some narrow area that goes into work every day and has to be taught and learn everything from scratch, right? Because they don't know anything about your company, right? And so the game now is about how do we give that model the right contextual information? And this is where everyone is circling around the same thing. It turns out that context— what is context? Context is my context is how I relate to the rest of the world. I grew up in Sweden. I'm the CEO of Neo4j. I have 3 young kids. I'm married to Madeline. I drive a Volvo stereotypically since I'm Swedish.
John Furrier
>> Of course, right?
Emil Eifrem
>> Of course, right? So that is my context. It's very graphic. It's like how I relate to the rest of the world. So everyone is converging on this approach to giving the AI models the right information at the right time.
John Furrier
>> I said on theCUBE, you got to feed the beast in a way. You got to feed the AI. But it's— you don't know the context. Static web was database, search, get results. That was a search paradigm. We're not living in a search paradigm, but we are living in a discovery paradigm right now with agents. Explain this, because search and discovery has been one of the categories. Go back to the early days of the web. Search engines had a discovery mechanism, keyword, get results, whatever, click on a link, navigate to a page. Now with AI, you still need that discovery layer, but you need it fast and accurate.
John Furrier
>> Yes.
John Furrier
>> Talk about that piece because people see agents going off the rails, hallucinations. They might not understand that there actually is a solution.
Emil Eifrem
>> Yeah. So let's tee off of what we just said, which is the real game here is giving the super smart AI models the right context at the right time. If you think about that, that in computer science is called IR or information retrieval. It's a retrieval problem, right? And so on some level it is. Let's say that you're a human being, you contact customer support. Let's take the classic AI enterprise use case, customer support, right? So John contacts some provider of yours, right? And you say, I need help with my Wi-Fi. Let's say your Wi-Fi isn't working and you describe it, say, this is the one that I bought. Or maybe the system knows it already because it knows who you are. right? And it says, these LEDs are flashing yellow and my Wi-Fi is shaky, right? Okay. The agent has to take that natural language that you typed in or that you said over voice. It needs to go from that intent to let's find the, call it, top 10 documents across my entire corpus of support documents inside of this company. Give that to the model at the right time to answer that question. Now, if you think about that problem, it turns out if you take a step back, as humanity, we've solved that problem before. The problem is the same as you go into a search box on the web, you search, right? Find me the top 10 blue links. And you and I are, sadly old enough to remember that there is a world pre-Google where AltaVista, Lycos, Yahoo!, Excite.
Emil Eifrem
>> Yeah, yeah.
Emil Eifrem
>> These days people don't even know those names. There's tons of search engines, right?
John Furrier
>> All dead except for Yahoo!, basically.
Emil Eifrem
>> Exactly. And one of the problems was people call it the AltaVista effect. You search for a result and you're gonna get a million results, but the top 10 best ones were maybe on page 99 or 433, right? Then Google came along and they said, you know what, I'm gonna search exactly to your point But then I'm going to rank the search result based on what? Based on the graph. That's the PageRank algorithm. How the documents are linked on the web. So that exact same approach is what the enterprise is now using to get the reliability of the retrieval.
John Furrier
>> And the results of PageRank, by the way, the ranking technology called PageRank created massive wealth for Google. Huge. They created a whole ad industry for online. But what does it mean for enterprise? Because this comes up a lot. I've done a lot of interviews with some of your practitioners as well as other graph enthusiasts. They're all having an experience of almost like a superpower, but they weren't in the organization pecking order, and now they're moving the needle on the business and they're being elevated up because they just discovered, it's like a caveman discovering fire. It's like they get pushed right to the top. This is where you start to see the graphs. Why is that happening?
Emil Eifrem
>> Yeah, it's spot on. Although I'm not going to describe my customers as cavemen. So that was fire.
John Furrier
>> Oh, the wheel. The wheel was revolutionary.
Emil Eifrem
>> Yeah, fair enough. I'll take that part of the analogy. Right. So the key thing here is what's called accuracy.
John Furrier
>> Yeah.
Emil Eifrem
>> And accuracy is the inverse of hallucination. the higher accuracy that you have in your AI system, the fewer hallucinations you do. Right. And there's a threshold that people talk about as the escape velocity for accuracy. And when people use graph as part of their agents, they reach this escape velocity where it actually works in production. And that's the key thing. Every single big organization right now is on this AI transformation journey. But if their agents can't retrieve the right data at the right time, they'll never have accurate enough answers for it to be able to be used in production. So that's the superpower that our champions get by using graphs as part of their agenda.
John Furrier
>> And by the way, it solves a lot of hard problems they've been grinding on. And other mechanisms. All right. Talk about the impact of the AI infrastructure because a lot of database, a lot of software, especially, you know, control layer software or connective tissue, whatever you want to call it, glue layer, people call it, was kind of constrained by the fact that you need a lot of compute. So now you're in an era where the new architecture on whether you have an AI factory, you have unlimited capabilities from a horsepower standpoint, you now have engines that can pump out tokens, which is now currency. Those tokens have changed the game on how data is interacted with. How has that affected graphs specifically? Because this is where I think the AI connection to the computer science of AI with data.
Emil Eifrem
>> Yeah. So there's two sides to that coin. The first one we've talked about, which is how graphs in Neo4j are embedded in our customers' AI systems to help them become better. We've talked a lot about accuracy. There's also an explainability and governance and transparency angle that is really relevant and important too. But we can get to that in a moment. So that's one side of the coin. The other side of the coin is if you look at graph technology, there are basically two big areas of friction to adopt graph technology. The first one is how do I get my data in there and how do I model my data? Let's say you have lots of data, unstructured, or it might be in your Snowflake or your Databricks or your Oracle data systems. How do you get it into the graph? That's the first one. The second one is how do you query it? Right. So there's a query language now called GQL. It's the first sibling to SQL that has been standardized by ISO ever. Right. So SQL was invented 40 years ago, standardized as the one universal database query language. GQL, the Graph Query Language, is the only sibling to that, which is just pretty phenomenal. Right. But it's a new language. You have to learn it. Well, it turns out that AI helps with both of those problems. And a modern AI model can look at your unstructured data and create the knowledge graph out of that from scratch, right? Without any human manual intervention. And then how do you query it? Well, these days you don't need to type SQL. You speak English to it, right? So that barrier to adoption has been just completely collapsed. And that's a huge part of what's driving momentum for Neo4j right now.
John Furrier
>> You know what's interesting? You brought up SQL, Structured Query Language. People don't know the acronym. That has been the standard for querying databases. I want to tie this to intent because one of the things that we've learned here on theCUBE and we see successful companies doing is they've changed the intent equation. I'll give you an example. I used to do a lot of SQL queries when I was doing co-op work in the '80s. You have to think about the business logic formulation first, then actually construct the query. The query then is my query to the databases. I need a report. Okay. Now I create the logic, then the query goes in. So I have to formulate the query. Sometimes there are huge queries. Now that intent is in the logic of the AI. So I just say I want the top sales by region or whatever my ask is. It does the logic on the other side. That wasn't possible. Explain this, 'cause this is like a game-changing shift in user experience, but also technical implementation.
Emil Eifrem
>> This one is huge. In order to pull this one off at enterprise scale, you require a technology that is absolutely fundamental, which is ontologies. And ontologies have been around forever. Aristotle talked
John Furrier
>> about—I did one in '88. I did one in '88. Taxonomy by hand.
Emil Eifrem
>> Yeah, exactly. Tom Gruber, coined the most common definition of ontologies for computer science in 1993 at Stanford. He then went on to co-found Siri, by the way, the same Tom Gruber. But really, the company that has popularized this in modern times is Palantir, right? And they started talking about ontology being their secret sauce, right? And what an ontology does, it's a graph model. So it is exactly one-to-one with what we've been doing for 20 years. And a business-facing ontology is the key asset here. And what it is, is it takes the world of your company. Let's say you're a financial services institution. You have customers. The customers have bank accounts. They have credit cards connected to them. There are derivatives. There are all the concepts that exist in your universe and how they relate. A business-facing ontology. Because it turns out that a key part of your job as an enterprise data architect today is that you have to design the world that your agents think in. And your agents think in the terminology of the business. They think customers, they think credit cards, they think accounts, they think insurance plans, that kind of thing, right? And then you require a technical ontology, which is all my data sources, the physical data layer in my enterprise. I have an Oracle database over there, I have a Snowflake database over there, and then a mapping between the two. So all of a sudden, you know that a customer first name maps to that Oracle database with a column called F_name. They need to know that F_name is the first name of your customer. But with these key ontologies to connect them, you can do exactly what you said. Your agents can interpret intent and map that to the data that they need.
John Furrier
>> And the graphs make it faster. So talk about the— how— think about graphs. It's almost like picking a fork in the road. You can say, okay, down this lane is a series of graphs, but it makes it very efficient. the word recursion comes up a lot in graphs. graphs are nodes with an arc connected to another node, and there's data in these things, right? So that's computer science principle 101. You traverse the nodes and you go see what's in there. So take us through why that works in AI now that you have intelligence and horsepower, you have compute and all the GPUs and all the vector embeds and all the other data.
Emil Eifrem
>> So the key secret sauce here, if you take, we talked before about the knowledge graph of describing all the key concepts and how they relate and all that. You can take that data structure and you can store it in anything. You can put it in S3 buckets, you can put it in Oracle, Postgres, whatever you want, right? But what a graph database is, like Neo4j, it's written from scratch. Again, we've been at this for 20 years now, right? And we've taken every single layer in the stack of the database and we've optimized it for exactly what you said, which is traverse this deeply connected web of information at lightning speed. So we are frequently, because it's perfectly—
John Furrier
>> And it's called a knowledge graph basically these days, right?
Emil Eifrem
>> Exactly. And it is frequently not even 1,000 times, but 1 million times faster than if you put it in a classic relational database, which is great at many things. It's just not great at traversing this deeply connected data that an agent requires in order to answer.
John Furrier
>> And they're bounded by latency too, because their accuracy is only as good as the best data possible.
John Furrier
>> That's exactly right.
John Furrier
>> All right, let's talk about the momentum in the company. Obviously great success. Love the tech angles. I think every company wants their own data moat. They want a Palantir-like environment. All the smart money and smart people are using graphs with other databases. Talk about the momentum in the company. You mentioned the rev— some of the revenue figures. Can you quote the numbers? Can you share some of the momentum, where you guys are at, and what's your focus now?
Emil Eifrem
>> Yeah, so we're here at the New York Stock Exchange. We're not yet a public company, so we don't disclose our numbers publicly. Suffice to say, we're hundreds of millions of dollars of revenue. And just to give you a sense of the momentum, 2026 is off to a flying start in Q2 of this year. So we're recording this in mid-September. So our last quarter was Q2 of this year. We generated about as much revenue in just that single quarter as all of 2025 combined. Right. So that gives you just
John Furrier
>> a—so you guys have a flavor of the velocity.
Emil Eifrem
>> It is really taking off and there's this widespread recognition that AI and graph is this, again, a match made in heaven.
John Furrier
>> Talk about the community that's developing around graphs. I think this is super fascinating because the people who are doing graphs were early adopters because they saw the value and it's almost like they are discovering the superpowers and it's spreading. Talk about how that's spreading in the community and what you guys are doing about it.
Emil Eifrem
>> Yeah, one of the things that I love about this company, of course, I'm extremely biased being the founder, right? But one of the things is that we've always had this practitioner-led adoption where the people who do the real work, they find us. We're open source. We have now in the cloud world, we have a free tier of our cloud offering. They self-onboard, they fall in love, hopefully, right? And they can build it themselves. Right. And so we only ever sell to people that are just champions. We never sell to start top down. Push it down into the org. Now, we, of course, at this scale, we also engage with the real technical leadership of the Global 2000, but it's based on this foundation of the people who actually sit there doing the real work, wanting to choose to work with Neo4j.
John Furrier
>> And making it easy.
Emil Eifrem
>> And that's just great.
John Furrier
>> Making it easy, I know, is always hard. Talk about the ease of use feature. How are you guys making it easier? Because we want to get our graphs going in our company. Every company I talk to is trying to figure out the brain, for their company. They've all come to the realization that, okay, we need a company brain, we need a Google PageRank, we need to have a Palantir. We have data that's valuable. How do we protect it?
Emil Eifrem
>> Well, yeah. And a brain, like even the human brain, is physically a neuron connected to another neuron through synapses. It's physically— it's a graph.
John Furrier
>> It's a graph. Yeah.
Emil Eifrem
>> And associatively, we think associatively, which is also a graph, right? Yeah. It comes back to what we talked about before. The fact that we now have AI, I spend most of my time thinking about how will this, how can Neo4j help my customers' AI applications become better? But it's also a massive superpower for us internally as architects of our own product because all of a sudden we have a way of making it so much easier to get data into the database and then query in pure English. Those two things are the main building blocks.
John Furrier
>> No one really has to give up anything to use Neo4j. They can still use their data lakes. They can still use everything else. You just connect into it.
Emil Eifrem
>> Well, and the other thing is we've also done a lot of investment in— we actually are tomorrow at our event, we're talking about a new product offering called Virtual Graph, which is super, super exciting. What Virtual Graph does is it's the entire Neo4j product platform. So all of the tools, all of the solutions running on top of it, we can talk about GraphAware later if you want to. As an example of the solutions running on top of Neo4j. All of this, all the AI capabilities, but it sits right on top of your Snowflake or your Databricks. So you don't have to move your data. It uses, in the weeds, technically it's called predicate pushdown queries. So it runs all of this directly on your petabyte data lake within minutes to get started. And so that's really exciting.
John Furrier
>> That's really onboarding fast. All right. Well, great to see you, and I know you got to go. I really appreciate your valuable time coming on theCUBE. It's been a couple of years. We have a lot of your team members on, and you got a great team. And again, graphs are just the beginning. People are starting— this will be standard, first ISO standard in the database space. Congratulations. And we'll talk more later. Thank
John Furrier
>> you.
John Furrier
>> Pleasure.All right. Founder and CEO of Neo4j. Again, the graph database is turning out to be the heart and soul, the connective tissue, the brain of organizations. And there's benefits. AI is highly compatible. You don't really have to get rid of all your data and databases to really make it happen. Of course, the results are fantastic with AI. I'm John Furrier, host of theCUBE. Thanks for watching.
John Furrier