 We think of observability as the ability to do open-ended exploration. What do I mean by that? We're adjusting all this data. We talked about the volume. But then people need to make sense of it. They need to explore the data. When problems happen in a network, you want to be as quick as possible to figure out what's going on. And so we have this notion of, we call it data explorer. It's this ability to very quickly and openly explore your network data and figure out what might be going on in real time. Hi, this is your host, Saptan Bharti. And we are here at CubeCon in Chicago. And today we have with us Christophe, Mr. Chief Product Officer at Kentic. Christophe, it's great to have you on the show. Yeah, thanks for having me, man. Yeah, and it's my pleasure to host you today. And if I'm not wrong, this is the first time I'm talking to someone from Kentic. That's a problem. Ha, ha, ha. I'm glad you do. But that also means that we have now cracked open the door. And I can see there are a lot of things that you folks are doing, which means that there are a lot to talk about. But let's start with some basics, which is talking a bit about what do you folks do? What is Kentic all about? Right, so Kentic is a company focused on network observability. So basically think of it as an observability company, but with a network-first approach. You know, there's many companies out there that do APM, application level observability. There's very few that are focused on the network. And part of the reason for that is that network is a very complex space, very high cardinality data. And so many observability, traditional observability back ends, they choke a little bit when they see all this network telemetry. So we ingest flow, VPC flow, SNMP, of course, EBPF. Now we'll talk about Qube in a bit. And we ingest all that into a big data backend. And then we help our customers do analytics on that data. Now, to give you a sense of the volume that we're talking about, so we did some checking last year alone, we ingested 200 trillion with the T telemetry records. So I'll do some math for you. That's about 6 million per second. And so you can imagine that's really, really high volume type stuff. And that's why we think we're a little bit unique and a little bit different. Now, since we are here at this event, talk a bit about what is the importance of events like these and so far, what has been your observation no pun intended? Yeah, it's pretty early days. But it's crowded. That's good. Lots of momentum and excitement in the ecosystem and the community. We love that. Yesterday, I was at Silium Day. I don't know if you're the new CNI, basically, or one of the CNIs, very popular. And they do also EBWF-based approaches. And so we're very similar. I think Kubernetes, in particular, has become kind of an operating system for the cloud. And it allows people and companies to be a bit more cloud independent, whether you run on AKS or EKS or GKE, the Google stuff. And that's one of the things that we help customers with, meaning we support all of these backends and clouds with our Kubernetes approach. But let me talk a little bit about observability. We have been talking about observability at TFR for a very long time. But we have not talked a lot about network observability. What is it? So let's talk about our approach to observability. So one of the things we think we do slightly differently. And there's one other company in the APM space who has a similar definition as we do. So we think of observability as the ability to do open-ended exploration. What do I mean by that? We're adjusting all this data. We talked about the volume. But then people need to make sense of it. They need to explore the data. When problems happen in a network, you want to be as quick as possible to figure out what's going on. And so we have this notion of, we call it data explorer. It's this ability to very quickly and openly explore your network data and figure out what might be going on in real time. That's very different from just looking at a dashboard or looking at some alerting. We actually allow customers to dig into all this massive data and figure out what's going on. And we think that's very differentiated. And then, as I said, networks have huge volumes. And so that's why probably Kenting is one of the only companies that does this stuff well. What does that mean for the whole ecosystem if you are the only folks who are doing it? Does that mean that there are a lot of awareness education needed in the space? Or you see that, no, a lot of ground work has already been laid. And your potential customers actually are not talking about what it is. But they are talking more about, hey, give us the tools that we need. Talk about the state of network observability. Yeah, so I will start with maybe another statistic. We have well over 400 customers. So we're not small. Many customers, we started with service providers. Then we went to what you call the digital enterprise, which is basically the SaaS companies of this world. So whether it's a Salesforce or a booking.com or a Zoom, they're all Kenting customers. Why? Because the network is fundamental to their business. Where we have a bit more education to do is in the more traditional enterprise. Because these traditional enterprises have been focused mostly on their on-prem networks. And now all of a sudden, they get these cloud workloads. They get Kubernetes workloads. There's the internet that's becoming the new backbone. And so how do you get visibility into all of that? And that's where we have education to do now. You may have or may not, may have not seen some articles in the New York Times or Wall Street Journal about Kenting. Why is that? It's because we have very unique insight into the internet. We appeared with thousands of networks. And so when something breaks in Ukraine or a subsea cable to St. Helena has a problem, we see the traffic impact and the latency impact in public clouds, as an example. And that is a very unique value proposition. That's why customers rely on us and our capabilities. Did you folks make any announcement here at the show? Yes. So we announced what we call Kenticube, pun intended. And Kenticube is a way to extend our value proposition into the Kubernetes ecosystem. Now, there's plenty of companies who do Kubernetes observability. But what they're missing and what we add and what we do in terms of value is that with Kentic, you can look not just at your clusters, but you can see what your clusters and pods, who they talk to. Meaning if you're interested if one of your pods talks to an embargoed country or if there's cross-region traffic originating by one of your pods or containers, which is something that costs money and may be a problem, we think we're probably the only ones who can give you that visibility. Because we stitched together the Kubernetes telemetry, which we get through EBPF, with all the context and the data we have from the internet, from public clouds, and so on, and provide a much broader picture in terms of what's going on with your Kubernetes installations. Kentic started almost in the same year when Kubernetes C&C have came to exist. And you folks both have K in your name. But a lot of things have changed ever since observability has evolved. Talk a bit about how network observability has evolved over time, or what does network observability mean for this changing landscape? Yeah, very good question. So the way I would frame it is that for the network, people, especially in the enterprise, less so service for Raspberry Enterprise, used to be focused on their on-prem stuff. They had full control. And then companies started to get interested in cloud and deploy cloud workloads. And initially, let's take AWS as an example, people had one account, a few VPCs, and everything was hunky-dory. But nowadays, there's customers who have thousands of accounts, or in Azure subscriptions, whatever the cloud calls it, thousands of accounts. The underlying technology is very complex. You have transit gateways. You have VPN gateways. You have firewalls now. And so all of a sudden, what used to be meant as kind of an abstraction is becoming, oh, it's a real network. And you have to manage it. And there's connectivity issues. And you have to configure it. And you have to make sure everything, the traffic flows. And so I think that is a very key difference from a few years ago that people start realizing that, hey, cloud is not the nirvana. There's work to be done. You need to actually be able to make sure that you observe your network, especially when it comes to hybrid cloud connectivity. Like, is my traffic actually flowing back to my data center or wherever I need it to go? And because we have this unique visibility into the clouds, into Kubernetes workloads, into data centers, and also the internet, which has become kind of the new backbone for companies, we think we can provide some pretty unique value to customers. I want to talk about some emerging trends and technologies, of course. I'm looking at Genetic AI. Talk a bit about what does Genetic AI mean for network observability, and what does network observability mean for Genetic AI workloads? Yeah, I'm glad you asked. So look, like many observability companies, we've been doing machine learning, anomaly detection, baselining, all these things for a long time. We call them insights. And we crunch all the data we have, and we provide insights to customers about what's going on on their network. Now, with Genetic AI, I think one of the biggest short-term values that can provide is the ability to, we call it, lower the barriers to entry. So I talked about this data exploration. Genetic has a data explorer. It is pretty intuitive, but still there's a bit of a learning curve. Like what if you could ask Genetic in natural language, what are the devices with the highest CPU utilization on my network? Which are the interfaces that have most traffic? And then the Gen AI, the language model, translate that natural language into the query that then gets executed, and then the result comes back. So for us, Gen AI in the first incarnation is all about lowering the barriers to entry to observability tools like Kentic. We have some pretty exciting work in the oven around this kind of stuff. We just released some stuff privately to some customers to help us test accuracy, because you want to make sure that whatever you put in, the right results come back out. And we'll make some announcements here pretty soon about this kind of stuff. So pretty exciting. No, of course, you folks are always working on the next thing. There are a lot of things in your pipeline you may or may not be able to share at this point. Of course, we'll talk about them when they're ready. But just give us a teaser, a glimpse of what kind of things we should expect from Genetic. I talked about natural language, and that's obviously just the first step. If you think about troubleshooting for networks or for any other complex system, it really is a sequence of questions you ask. You ask a question, you get a result, you drill into some more detail, you branch off into some other questioning and so on. And so the ability to do that and correlate for the engineer all the different things that get out data based on the question that he or she is asking is we think a way to, again, lower the barriers to entry. Now, if you have enough of these, we call them journeys, as an example, we believe that eventually, by training the model on enough of these journeys that customers do, so think about a customer asking 100 users, having 100 questions a day about their network, that becomes a pretty big data set. And so if you can train the model on this data set, eventually the model might be able to go from answer directly to solution. And so it's kind of the holy grail of root cause analysis that this industry has been trying to deliver on for a very long time. I've yet to meet a customer who says, hey, root cause analysis is, my root cause analysis is great because it's not. And so we think that JNAI and large language models can help with that over time. Christophe, thank you so much for taking time out today and talk about network authority. And as we can clearly see, there are a lot of things that you folks are working on. So I'm looking forward to talk to you folks soon again. Thank you. I hope so. Thanks for the great conversation.