Observability, Per-Person Insights, & AI UIs with Ariel Assaraf

Speaker A: So if you are relentless about success and you're gonna, you know, run through the wall, climb through the chimney, do whatever it takes, pivot the product, listen to your customers, work really hard, gather a group of people around you that are as persistent and have the same grit, eventually you'll get to where you want. That's how, uh, we view it. By the way, as a company, our motto is hungry, humble and smart, in that order. Um, it's about grit, humility, and, uh, a strong will to overcome challenges. Not to win and celebrate wins, but to overcome challenges and become better.

Speaker B: This is SaaS scaled the podcast where data meets action with host Arman Shrakhi. Each week, Arman will be sitting down with CEOs and industry leaders from the technology sector, giving you the insight to innovate with Reinventing the Wheel. They'll discuss challenges, best practices, and how to identify the right metrics. So if you want to get to market faster and in a way that matters, then subscribe and join us every week as we discuss SaaS Scale. This episode is brought to you by Curve, the modern no code analytics solution. The tools you need to take action with your data on a platform built for maximum scalability, security and cost efficiencies. If you're ready to reduce complexity and dramatically lower costs, then contact us today@crave.com that's Q R V Y dot com.

Speaker C: Hello, welcome to another episode of SAS Scale. Uh, as you know, we talk with SaaS leaders, and this time as well, we have a guest, a true entrepreneur with a great journey, a great story to share with us, and I would like to welcome Ariel to the show. And please introduce yourself and tell us a little bit about your company and the story that you have, uh, just to give us the right context where you're coming from and how you started and you did.

Speaker A: Yeah, um, thank you very much for having me. First of all, um, my name is Ariel. I'm co founder CEO at Coralogix. Coralogix is an observability platform that provides a, uh, full stack observability and security in a different manner than what was provided until today. So Corelogix analyzes all the data in stream, then builds a data lake to the customer's own storage to provide more flexibility, data ownership, open formats, an agenda clear on top of it, and all of that at a price that's significantly lower than our competitors. We started about, uh, ten and a half years ago, went, uh, through a very interesting journey. We can talk about this a little bit, uh, uh, more in depth but first, uh, five years, very uh, difficult kind of looking for our fit and our unique voice in the market. And over the past five, five and a half years significant growth that we've experienced.

Speaker C: Fantastic. And uh, definitely we live in a different world now. So it's the age of AI and how do you see kind of the AI capabilities and especially the newer version of these large language models coming to the market can really um, converge or can work with what you guys do?

Speaker A: Yeah, I think the, there are a few aspects to AI and basically the future work and the future of data. Um, when it comes to the human factor, um, we believe that observability being the most voluminous, truthful real time data organization has, um, is a resource that was underutilized for many years because it's technical, because there's so much data, because it is so complex. It was used mainly by engineers to keep things up and running. So we were mainly using data to manage the, what we call the downside of software. So you don't want your software to go down, you don't want it to have latency. So you're using this very valuable data for that, which is important. But it's not all. We believe that now this data can actually be correlated, analyzed in full, but also accessible to people outside of engineering. We think that because this is the highest volume of data with huge amount of people in the organization relying on it, it's going to gravitate a lot of the other data sources in the organization and basically allow companies to resonate on top that data layer and make decisions. Now at the core of things we believe that engineering is about the ability to analyze and solve problems. That is the core of engineering. It is not coding. Engineering exists for you know, centuries or more than that. And coding is a uh, thing of the past 30, 40 years. Yes, coding is going to change dramatically, if not get completely, you know, switched uh, by AI models that write your code, but understanding problems, analyzing them and then providing solutions and iterating that is going to still be uh, an engineering problem that still is going to stay and obviously assisted with AI but it's not going to completely get replaced. And so we think engineers or engineering skills are going to find themselves in more areas of the organization. How can engineers solve my go to market challenges, my finance, my HR, my uh, facilities? You know in CoreLogic is funny. Everything sends telemetry to CoreLogic. So the coffee machine in the office will send a log saying I'm running out of coffee and it'll trigger an Alert to Slack. So people know, right, the it, the computers and obviously the routers are connected to the IT system of CoreLogix. And they tell our teams if a computer needs an upgrade, the lights, uh, in the office or the you know, uh, uh, air conditioner, they'll tell us if the uh, office is empty, the lights went off and the air conditioner. So all these things are, they're very simple by the way. And many people do that in their smart homes. But at the big business level there are still silos. You got engineers looking at observability, security looking at sim and cyber products, you got BI looking at bi, you got marketing looking at marketing and funnel, you got product looking at product analytics. But they're all being produced from the same software. So those were all reductions of the actual telemetry because telemetry was too complicated. And this is where we see the big opportunity, democratizes for the entire org, correlate insights with real time truthful data and have uh, problem solving skills in every department. Um, so it's a very exciting period of time. Now,

Speaker C: in the future, looking into the future and then assuming that AI will be more dominant and people can use AI more, how do you see that with or without AI, it would have been different that people utilized this technology. Because whatever you explained, even if AI did not exist, still you could accomplish, you could accomplish through algorithms, you could accomplish through making your product more user friendly and people really go there and have better governance and then defining who can see what and who should see what and even providing them a no code environment so they can really do whatever they want. But now, moving into a different age, do you see how the utilization part can change and how it can expand your user base and your market?

Speaker A: Yeah, I think there are some things you can achieve with algorithms and simplifying and there are some things that are uh, very difficult to achieve without the power that we have today. And it is, how do you get very complex insights and relationships between data sources served to a very simple generic user that is untrained with your system. So that's a gap that AI produces dramatically. And then the second by the way, is how do you personalize the insights per person and not Persona? Meaning number one, Yes, I can simplify the workflow to help the standard day to day user achieve what he needs to achieve in front of him. I can use very advanced algorithms to provide deep insights that the experts will look at. But the combination of both being able as a simple user that doesn't know anything to prompt and in the background have very complex aggregations, very complex correlations happening. So I get an insight that's something that wasn't done before. And um, the other piece, which is also um, important is that a lot of these users now are um, having to spend a lot of time learning other systems, preparing data for those other systems. So basically if you look at BI systems and others, there's so much data massaging that you have to do and defining a schema, uh, and defining the views and defining what quotes, queries you're running. Once we unleash the basically ability to send whatever data you want and have that data automatically normalized, modeled on the fly, which is something that CoreLogix does. So you can practically just think of what is the data source that I want and what is the outcome that I want. Um, you can skip all the middle. We'll see a lot more adoption and a lot more creativity coming from teams.

Speaker C: And uh, we, so far we have been in a kind of environment that human being has been the user. So you design your software, you design your analytics, you design the way data is getting ingested for human being. Now there are some companies looking and say in the future I may need to also serve digital workers. And these digital workers like humans alongside with them, they will log in, they will look at my data, maybe even, you know, humans over time May said, we are getting bored out of, you know, doing these kind of redundant jobs. And some of these agents can log in every morning without any delay, check everything. And even I have seen in some cases that more complex UI and more complex, uh, and more complex, yeah, that could be edited. Uh, whatever you touched, it just created some kind of noise. But we will cut it. But I see that some companies are creating uh, more complex UI and more complex, even reports more complex visualization. Not for human, they create this for AI agents. And AI agents have no problem of that limit. You know, the capacity that they have is bigger, meaning that they can ingest faster, they can ingest more data and complexity doesn't annoy them and they can really gain all of that information and insight. Do you see that? Not tomorrow, of course, but as AI agents need time to get, you know, broader acceptance and trust of human being. And actually people bring them in and actually ask them to do real stuff. And it will happen gradually in my view, during the, some, maybe, you know, a few years, but not during the few months, probably a few years. But do you see that also you will create a different user interface, a different UI in your software for AI agents in addition to supporting humans.

Speaker A: So we divide this into four phases that we believe will play out over the next two, three years. Phase one is where we are today. There's an interface and it lives side by side to sometimes AI access with an agent. Phase two is an assisted experience which is where we're currently serving most of our customers, which is um, within the interface you get an AI assistant in every single area. You can ask broader questions or you can ask specifically about the screen you're looking at or the product you're looking at. Next phase is uh, an AI experience, meaning our CLI that we've already released where you don't even have to look at the UI. You basically load all of the CoreLogix capabilities as skills inside your cloud code, your codex, your cursor. You just operate from there kind of having a single pane of glass. And then the fourth phase is just completely autonomous. And uh, we're going to release now this week. Um, and you feature specifically around that our AI agent, our SRE agent is called ali. We're going to release what we call Ollie Watch, which will basically be a ah, new agentic, uh, um, interface where you define a series of agents that are tied to the CoreLogic Notification center so they can notify you and create workflows and all. But you basically define questions for them, research questions that they will periodically run and let you know if there's anything that you need to know through our uh, standard notification service, uh, capabilities. So you can have like 10 agents running every five minutes checking whether there's customer degradation. The other one will check whether there's revenue loss associated with software, uh, issues. The other one will check if there's a cost spike. The other one will check if there's any issues with any of the clouds that I'm running on and they'll do that for me and basically replace thousands of manual alerts that people run today. Um, we believe that that's going to be the fourth phase. Mainly people designing systems or designing harness uh, uh, uh, or building harnesses for agents and those agents will directly interact with the data that CoreLogic provides.

Speaker C: That's great, that's a great vision to have. And by the way talking to SaaS leaders and software leaders are here, um, this kind of expanded vision and we should admit that software still is very young and definitely during every decade software is elevating and uh, is going to a different level and we are empowered also by many technologically advanced technologies and foundations and platforms that empower us. But it allows us to think bigger, to do more. Now, if you think of really SaaS as a kind of overall industry and software in a even more general overall industry, uh, and you look at the new SaaS, companies coming to market, the new entrepreneurs, the younger guys that are thinking about, okay, I wanted to start my companies. Now, if you wanted to tell them that, what would be the winning card, what makes them, uh, successful, what would be their unfair advantage? And you have only one word to select. What do you say? Is it speed? They need to be fast. Is it. They need to be correct. Is it. They need to be what, what is that one thing that you would tell them that's your winning car?

Speaker A: Uh, persistence, I'd say. Um, the way I view this, by the way, especially today, there's no more meaning to being very smart. Um, the ability to plan and then even build has been improved so much over the past 24 months. And it's gonna get so much better in 24 months that if you're very smart and you can write very capable algorithms or very robust software, it's not gonna mean that much. It's about, like I said, identifying a problem, designing a solution, and making sure you deliver it. But then I think there are no winners in startups. Um, you know, once in a blue moon, you get a whiz that's a clear winner, you know, going to a billion in four years. But it's usually there are no winners. It's very hard. It's not about if you want to win because everyone wants to win. It's more about if you're not willing to lose no matter what. So if you are relentless about success and you're gonna, you know, run through the wall, climb through the chimney, do whatever it takes, pivot the product, listen to your customers, work really hard, gather a group of people around you that are as persistent and have the same grit, eventually you'll get to where you want. That's, that's how, uh, we view it. By the way, as a company, our motto is hungry, humble and smart, in that order. Um, it's about grit, humility, and, uh, a strong will to overcome challenges. Not to win and celebrate wins, but to overcome challenges and become better.

Speaker C: Fantastic advice and insight. I appreciate it. Uh, I'm going to ask you a very random question before jumping into, uh, some other related, uh, question to the context. Uh, if you, if I ask you, can you suggest, uh, a city, a place that I should go for two weeks and have a great time? Based on your experience, which city would Be that city that you would say Armand. Uh, definitely you should go and just spend two weeks time that you have in this particular place.

Speaker A: Perspective.

Speaker C: No, just having fun with no technology. Turn off the mobile and just having fun. Good.

Speaker A: That's a good question. Well, you know, my natural inclination is to say Tel Aviv because I miss home. I've been out in the US for a while now. Um, but if I rule out Tel Aviv, like, which is the immediate response, like nice beaches, good food, good people and good technology. One of the places I discovered here that was like incredible, uh, town, uh, right outside of Boston is Provincetown. You take a ferry, it's at the very tip of Cape Cod. And it's like you went back to uh, a desert uh, island in the 60s, uh, only it's middle of the U.S. um, I really enjoyed it. Both the weather, the views, the nature over there and everything.

Speaker C: Fantastic. No, that's beautiful. Uh, for the first time in my life I was in Maine. Um, I'd never been in the state of Maine because we were in Vermont and moving up, but never kind of changed the way to go to Maine. And last year we said we should visit Maine. We have been talking about it, so for stuff. So we went there and stayed there for a month and we wanted to leave and then extended that once for another month moving north to Canada, to Nova Scotia and then another month even moving forward, going up. So we moved up, we moved up and then we ran into sceneries that I thought, and I was telling Kathy, my, my spouse that if we were here some millions of years ago when dinosaurs were roaming around, still this was the scenery. It was so gorgeous and it was untouched. It was unreal and it was beautiful because you could not see anything. Anything touches that. Right. So, um, you know, whales are just roaming around and water is there and you uh, know everything that you see is just has been out of touch. Nobody has touched it. It's the same as it was millions of years ago. And that makes it so beautiful. So I get it, I get exactly what you say.

Speaker A: Yeah, yeah.

Speaker C: Fantastic. Now with regard to uh, you know, data itself and observing everything and uh, just having that platform that observes the data for you from different uh, devices. Uh, very recently I was speaking with a gentleman that leading, uh, a SaaS company that actually they had a patent on um, uh, getting data from outlets, uh, just electric devices and understanding which device it is just by plugging their device on the outlet and then looking at all of these kind of devices from H vac to refrigerator to different things and they uh, thought that first we need to market these to consumers and homes, and homes can really use this kind of device to understand how to save money on electricity. And then it turned out that actually restaurants are the best candidate after McDonald's contacted them and said I'm very interested because uh, you know we can really look at this data in any branch and just optimize the cost of electricity and that kind of thing. And it just from their perspective it was. And ultimately you know a restaurant actually acquired the company and uh, they are part of that entity now. And the person I talked to who started that company is now head of AI, that bigger company, but a restaurant kind of business and very interesting. So, so when, when you look at this observing data from many, so many different aspects. This was just one example to show you how you know, we are really dealing with data nowadays and how much insight exists in this data uh, around us that we may just ignore and we may not use. But there is a, ah, huge value there from business perspective or value from people and organizations can receive help and save money and time. Um, now you look at this kind of observing all of these data. Your platform, as you said, traditionally people have used it to really monitor all of these electric devices and maybe computers and some other things. Um, but when you look at the data around us and many other aspects, uh, there is no limitation. People can really plug in your probably, you know, technology or a different version of your technology in the future when you build it for them and say this is a specialized for sales data, this is a specialized for some other aspects that you are dealing with. And uh, then you have that kind of brain built in. So very quickly I can just plug in that data into my data sources or financial or maybe even some banking or some other aspects. Right? I mean from your perspective that is something you can really do as well.

Speaker A: It's a very generic data lake streaming engine and query engine. Uh, we purpose built a product that was targeted for observability which we view as the biggest problem to solve. But it is a lot more extensible. And by the way these days with AI you can actually vibe code a full business application on top of the daily internally in Corelogix we use Corelogix for everything. We pull revenue data in there, billing, customer tickets, R and D, HR. I can with my system today with CoreLogix, go in today, use the CLI and ask um, what customers should I talk to about this feature? What are the next steps that R and D need, uh, to invest and how much is it costing me on my infra. And in one place get, okay, here are the customers that are using it, here's the next thing you should do based on their usage and tickets that they've opened, here's how much they're paying you and here's how much it costs and basically make decisions directly from there. This used to be a multi siloed data uh, structure uh, that had to go through different people that I needed to ask and get in a room to get a decision done. And now it's basically a prompt. We uh, see that as the future of data and coralogics and obviously there are, you know databricks has done a lot of work in that world and we just built CoreLogic's very purpose built for observability. They started with business but there's going to be conversions at the end of those industries.

Speaker C: Yeah, and uh, there was a company that was very well known in log analysis some years ago and Cisco acquired them. Ah, sorry, Splunk. Splunk, exactly. So I do remember they had different templates for different kind of work and you could load one template if you are in a cruise ship for example and they load all of these kind of templates and uh, do some of those. But that was very heavy and that was very kind of enterprise style kind of work. Not something you can democratize. Now when you look at many businesses, I mean the simple examples are Apple or Tesla. Uh, but if you look at many of these businesses, Apple built Macintosh for many decades, for many years and then it turned out that actually the best invention is not the computer that was the hard problem to solve, it was actually iPhone. When they introduced iPhone, that was actually a much simpler version of what they had. So when they used less and democratized exactly what the device that they had, they got their biggest adoption ever. Now Tesla the same they introduced first Model S and X did not gain that much momentum. The company did not become profitable until they introduced Model 3 and Y. And then everything changed. And selling cars rather than thousands, they reached to tens of thousands and hundreds of thousands and close to million and now over a million a year. Now simplifying the product after learning how to build the complex one, after learning how to solve the most difficult problem and then reaching to the point that now you can really introduce the simple version, it changes companies one after another. And from your perspective when you look at Coralogix, is it like we are in that stage that we are actually introducing our simplified version now? Or you think that is Coming in the future.

Speaker A: We're not there yet. We need to get to a point where we feel that we fulfill 90% of our vision and observability and then we can reduce that to what is going to be 100% for other use cases. But we need to serve the. Like I said, uh, it's the grit and persistence to solve difficult problems. So we need to get there. We're not going to look for a shortcut. We will get there. We will solve that problem in full and reach even further scale than where we are today. We are like you mentioned, thousands of customers, significant revenue, fastest growing in our space. But there's still room to grow and go further with the product. But then we're definitely going to serve more industries and I think it's going to happen within the next 12, 24 months.

Speaker C: That's great. Um, and as my last question, I would like to ask you if you could introduce uh, a book that either personally you liked or professionally it was very impactful and you would like to share it with the audience.

Speaker A: Yeah, um, of course. So there's a. I don't know how to translate it to English exactly, but I would uh, I think the closest one is the the Laws of Character Traits. Um, it's a very interesting uh, and eye opening book by uh, Maimonides who's uh, one of the, the greatest people in Jewish history who lived about a thousand years ago in Spain. Um, and it was a very uh, strong philosopher who learned a lot also from Aristo and other Greek philosophers at the time. Um, and he created the book of Personal the Laws of Personal Traits where he basically explained um, step by step how you can work on each and every of your given traits. So every person is born with a set of traits. Someone get angry quick, someone is less patient, someone is cheap, someone is spending too much, someone is too nice and someone is too strict. How do you define and then reach what is uh, what he calls the golden path of your personality. Um, and I found it to be something you can also stretch at the macro level. And his way, by the way, very simplified. It's a very deep and interesting book, but very simplified is you go all the way to the opposite of your natural trait because you can never uh, completely dismiss it and it'll pull you towards the middle which is that golden path. So uh, as a concept, for instance, when we wanted to introduce AI and get people adopting it a lot more in the company, um, there were some voices saying we got to go step by step and do tutorials and get people and do it like a small group, a bigger group. And thankfully my co founder and I have the same mindset around these things. We said, okay, we are now at zero, we need to get way better. But we understand we're not going to get to 100 in day one, but we are going to pull towards 100 from day one if we want to be at 50. Um, and he explains that, by the way. And also whatever you want your son, um, to be or your daughter, you need to be 2x of that because people have their own traits. And so what we said is everyone now has to build only with AI, uh, including sales and hr. You got to build a tool for the next week, whatever that is, solve automate one problem, solve one business issue, you have make one structural change in your Org. And it forced everyone. What happened is that people found that middle path and now Coralogix produces about 10x more features than it used to do with, uh, about half the software issues that we used to have. So the, the benefit is huge. And we actually, I see a lot of, and I hear a lot of voices saying it's hard to measure productivity with AI. First of all, you can measure productivity of AI and cost of AI with CoreLogix itself in a very detailed way. But, and we used it, but we can definitely show today productivity cost and output gains that we had, uh, over the past year.

Speaker C: Okay, fantastic. Definitely. Is the book available in English as well? Yes, I guess so.

Speaker A: Yes. Yeah, yeah, he's, he's been a philosopher outside of Judaism. He's like huge character in Judaism, but he's also a famous philosopher. Global.

Speaker C: Definitely. I will check it out. Thank you very much. And I'm so glad we connected and uh, we could have you in this program. That is great to uh, you know, share this insight with the audience and definitely I will follow up and I will follow you on LinkedIn and I will root for the company moving forward.

Speaker A: Thank you very much. Arman.

Speaker B: Thank you for listening to SAS Scaled with Arman Eshragi. For show notes and any resources mentioned in today's episode, go to saskaled.com if you're enjoying our show, give us a five star review and share on LinkedIn. And be sure to subscribe for any updates on future episodes. Thanks for listening. This episode is brought to you by Curve, A the modern no code analytics solution. The tools you need to take action with your data on a platform built for maximum scalability, security and cost efficiencies. If you're ready to reduce complexity and, uh, dramatically lower costs. Then contact us today@crave a dot com. That's Q R, V E-Y dot com.