The CDI CTO Podcast - Will Huber & Fred Koopmans

Want to kick off your new year on the right foot? Check out the latest podcast from CDI Studios, The CDI CTO with CDI's Chief Technology Officer Will Huber and Big Panda's Chief Product Officer Fred Koopmans! Learn more about the partnership between CDI and Big Panda, what Big Panda does, how the company is growing, and the inspiration behind the name and logo!

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Transcript

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Welcome to the CDI CTO Podcast presented by CDI Studios. Hello and welcome everybody to the latest episode of the CDI CTO podcast. My name is Will Huber. I'm the chief technology officer at CDI and today I'm super happy to introduce our guest for this week's episode. We have the Chief Product Officer of one of CDI’s strategic partners and also a vendor organization of CDI. We're actually a customer of this organization, so we have the Chief Product Officer of Big Panda with us today, Mr. Fred Koopmans. Fred, how are you doing man? Doin’ great Will! Thank you so much for having me on today. Yeah, yeah. Thanks for, thanks for joining. Where in the world are you joining us from? I'm in the Bay Area, actually. I'm in my home office in Burlingame, California. Nice, nice. Very cool. So is that is that that's headquarters or Big Panda, right. Or nearby the Bay Area? You're you're correct. We moved into the new headquarters this week. We're now based in Redwood Shores, California, only ten miles away from where I am now. Awesome. Awesome. So short commute. Are you guys going back to the office or you're staying? It looks like you're home now. I'm home now. But we are going back a little bit more. So building a an active sort of full function office going forward, probably like two or three days a week would be pretty common for us. Very cool. Very cool. Yeah. We're starting to see more and more of that, which I'm a fan of. Right. I think it's a it's time and it's good to be back in person with people and sort of move on with things. It's good. Yeah, absolutely. I know I joined Big Panda during the pandemic and clearly are in a fully remote scenario. It's a little bit hard to establish those relationships and connections even internally. And just the more we're coming back to the office, the easier that's been. Yeah, well, actually, you joined there during the pandemic, right? You actually joined I think I have it here in December of 2021, right? So what was that like joining a company? Any company really as a senior executive? Right. During a pandemic when you can't see people. The whole process was a little strange. A lot of the interviewing part, scheduling interviews, like between meetings of the prior company and all of that. But then also let's say the getting to know each other, part of interviewing in a way, it actually worked out really well because we just went really slowly and I had an opportunity to meet all of the executive team members two, three, four times, you know, the founders multiple times. And we kind of we just took our time with the whole process. So actually, I felt really confident that it was the right choice for me throughout the process and in an odd way, rather than having like a one day onsite and then like make your decision either for the company or for the candidate. It was easier when it stretched out a little bit. A little bit more. Yeah. So. So speaking of wish, making the right choice. So what was it about Big Panda? I mean, you haven't been there that long, right? You're coming up on, I guess, your one year anniversary. Coming up on a year. November 15th or something like that. That would be one year for me. Look, there are three things that really stood out to me, or are the things I was looking for. I put it that way first off, was looking for a great company. That's almost redundant to say. Of course, everyone is. Myself in particular, I've only worked at three companies prior to Big Panda I tend to stay a while. Yeah. And it's really important to me that I pick the right company so it's the right fit for me and the right fit for the market. So big market with strong tailwinds where it was super critical for me was looking for a product market fit company. So it's already established that the zealous customer base, of course, the management team and the people and the culture in general, you know, life's too short to to work with anything other than great people that you respect and you'll learn from that will make you better, etc. So the first thing Big Panda knocked out of the park and all of those. They’re in the great company category next I was looking for a product that was a good fit for me and what I was looking to do next. So I've always specialized in a very technical product for technical buyers. So even though let's say it's SaaS like the underlying consumer and the technology is pretty complex, very technical, it's a technical sale. I like that. I love those hard problems. Number one. Number two There is data everywhere. The whole product and industry really is sort of underpinned by data by previous background at Cloudera was obviously managing a data platform there. So I wanted to leverage that strength and that those foundations of data for an application. So specifically I was looking to kind of target a technical application sitting on top of the data platform that could kind of describe it that way. I wanted to move a little closer to the customer from where I was previous company. Born in the cloud as well. That was critical for me. Not another on prem company pivoting to the cloud. That's really hard. Very few companies achieve that successfully. I wanted a company that was born in the cloud from day one with the right kind of architecture DNA inside of the company; Big Panda hit that as well. Last but not least, some of the product category, you know, something that is good for society. You know, it's not just about making money for myself or my family or the employees, but it's something that contributes to the world. At Big Panda we help keep the digital world running. It's a pretty useful thing. Like we're using probably 15 different digital services right now as we record this podcast, but any one of those went down via the internet or streaming provider, etc. like that would be bad, annoying. So I like the societal benefit as well. Yeah, I like that. I like that connection of, you know, we always challenge our employees internally here to, to find the connection of what is CDI’s role in the bigger sort of ecosystem of things. Right. How do the things that we do every single day impact our customers and our customers ability to impact their customers? And right. And before long, that starts to have cascading effects that have societal impact. Right. So I love that answer. It's a great answer. Cool. Yeah, yeah. Obviously really important to me as well. I said I don't change companies a whole lot, so I try to be really thoughtful when I do. And then the last thing there just thinking Big Panda; I was specifically looking for something a little bit earlier than I had done in the past, at least as of recently. So I wanted to find something that had a lot of runway still out ahead of it, where there was an exploding market, strong product market fit. So a lot of opportunity. And that's exactly what I’ve found here at Big Panda. We are strong and, you know, in a strong position, but just getting started with what we can do. Yeah, no, that's great. So tell me about the name. Right? There's all these all these crazy company names, right, in the tech startup scene. Can you tell me what the origin of the name Big Panda, where does that come from? Yeah, you know, I love the name as well. It it's great swag, by the way. Say that again? I said the great swag, by the way. Yes, it's awesome. Yeah. There was a lot you can do with it. It is a lot of potential. It's a fun brand having sort of the the kind of the animal aspect or the personification, let's say, that's been a lot of fun and it's kind of unique, right? There's a lot of tech companies, enterprise IP software in particular, that have sort of made up names. And they're very hard to take that name and convert it into a t-shirt and ethos. But that wasn’t the approach of Big Panda. You know, no crazy origin story per say. This was really just our to founders, Assaf and Elik, kind of kicking around ideas and talking about what kind of company do they want to build and what would be the brand and the ethos of that company, and how can they kind of stand apart even from the beginning from from others? And, you know, they were looking for something fun and memorable, animated, like I said before, and then kind of the rest just kind of came from that. Definitely they were trying to distance themselves from corporate tech speak kind of thing. You know, and I don't know for certain- but, timing wise this was, you know, ten years ago or so, it was right around the time of the Kung Fu Panda movies. So I'm guessing whether, you know, explicitly or subconsciously I think there is an influence there that might have made its way at a minimum into the conference rooms at our at our corporate headquarters, all named after Kung Fu Panda characters. Oh nice, nice. I love that. I love the different like themes and schematics for for conference rooms and office locations and stuff like that. That's cool. So, so in your intro, one of the things you mentioned about wanting to what drew you to Big Panda was solving complicated problems, right? As a, as a product person, you want to help build and bring to market products and capabilities that solve problems for customers. Right? That's what we're in the business of doing. So I guess if I could ask you to expand on that and maybe talk about some of the trends and the problems that our customers are faced with today, and then we'll sort of get into Big Panda and the product and platform and how it helps address those problems. What are you seeing out there? Yeah, let's see. I would say the first challenge is data. There's sort of like every organization I've seen is dealing with too much data and not enough data all at the same time. It's sort of fragmented and, you know, they were sort of early part of the either their data journey or their digital transformation journey that moved them to a period of collecting a lot of data. But at some point they kind of stand back and say, okay, what do I do with all this data? It isn’t actionable, sort of too fragmented, or there's too much of it. And from there they say, okay, well, let's go build a data link. Or in today's vernacular, you might call it a data cloud. Same concept, but stored in a cloud platform instead. That's one big challenge, right? Any kind of business application, these days or at least many of them is going to start with this relationship to data. And how do you pragmatically extract the value of that data that's very dependent on the particular use cases. So that’s one trend. Another trend is that of efficiency. You know, how do I do more with less? You know, every year you're supposed to be able to do deliver more to the business with less resource and getting more and more efficient kind of every year. And hey, in today's economic environment, that's never been more true. So certainly still true today. Lots of ways of doing that. But for data rich problem sets the secret weapon here is really A.I. using artificial intelligence to address problems more efficiently, identifying the manual repeated sort of labor intensive components, and then squeezing the inefficiencies out of that, using the latest technology we have. So that's the second one. And I'll mention one more. And this one's been kind of fascinating to me just from the kinds of companies I've worked at. But if you go back, you know, one or two decades, there was very much a single vendor mentality. You know, there is there is, you know, behemoth companies that try to be all things to all people within a particular organization. I don't need to name names. I think everybody knows that kind of the monoliths of the parts of the past and I think in our on-prem world that actually made a lot of sense. The I.T organization was constrained with how many different vendors they could work with, how do they get everything to work together well, but as everything is shifted or evolve in many ways, we're still starting the shift to cloud for some organizations. But there's there's been a complete sort of upending of that mindset. And today the cloud has made it possible to productively and efficiently select a bunch of different best of breed vendors to solve problems, as opposed to sort of one monolith. So I think that the sort of multi-decade long shift from away from monoliths towards best of breed is is sort of a challenge in how do I do it, but also a trend that we see. For sure, I can certainly resonate and relate to a lot of those things. I think you mentioned the secret weapon is AI. And I think, you know, I think Gartner maybe may have coined the term AIOps a couple of years ago, maybe at one of their symposium conferences or something. And it seems to me like a lot of vendors out there have sort of latched on to that that concept or that term. And actually, I think being critical of some of those vendors. Right. I think sometimes there's not a lot of AI in their AIOps strategy. Right. So I guess I'll ask a direct question. So what is it that Big Panda is doing? How are you leveraging AI in this space as a differentiator? Yeah, I'll start with just reflecting, let's say on the term AIOps. What does it mean? It is essentially applying A.I. to I.T. Ops. So super broad, right? Like everything from cybersecurity to like can somebody at the helpdesk please help me reset my password to, let's say, like service reliability assurance kind of things that Big Panda does. So very, very broad, all packed into that one word of AIOps that the mindset, at least that I take from it, though, is that it's just really about scaling your operations through AI instead of bodies, instead of throwing more people and process of a particular problem, breaking that problem down into sort of specific pain points, and then attacking those. So like if I look out to the market, I see sort of a couple of different approaches. There's sort of the general purpose AI we can program it to do, you know, everything for everyone but you know, out of the box can't exactly do anything for anyone, requires a lot of consultants to go kind of make that work. Sure. On the flip side is you start much more narrow and you say, well, look, I'm not trying to build cold fusion here. I'm trying to reduce the noise of a particular kind of pattern and use AI to to predict and watch sort of what is the human operator doing if the human operator is constantly like, you know, jumping all over these alerts and dismissing without even opening those alerts, well, that's signal. That signal tells us these are actionable. Those are not actionable. That's one very kind of small example. But that's the kind of thing where you can find a often repeated manual process and then you can find a way to automate that away, regardless of whether you used a particular machine learning kind of algorithm to do that or just simple pattern matching or any other kind of technique, the how is less important, the what is what was manual today is now automated tomorrow. And that's the mindset that we employ here. Just pragmatically know your user, surround their workflow and automate all of the kind of laborious parts of it. And I mean, obviously scale is one thing, right? Doing more with less. But in that example that you just sort of explained where you're automating the outcome, you're actually generating a more positive experience too, right? You're getting to the end result faster. Right? So I think it's sort of a twofold approach there. So one of the things that you said before, which I want to latch and latch onto and drill into a little bit, is the best of breed approach, right? So we have we work with a lot of businesses and they're all the same, right? They all have existing investments in technology, in the I.T. operations space. So how how does Big Panda take an approach to integrations sort of of that ecosystem to sort of bring it all together? What is your approach there? And if you could just drill into that. Yeah, and let me like for anyone not super familiar with Big Panda, let me kind of do like the, the two minute overview and then we'll go into that detail there. At the highest level Big Panda is, it's an intelligence platform that helps businesses prevent unplanned downtime or outages and specifically all of their digital services for a company like Big Panda SaaS Company, well, our product is a digital service. Pretty important that we keep that thing running 24 by 7. It’s obvious for a SaaS company but even for traditional businesses. Well, look, they have lots of both internal for their employees and external for their customer digital services. You know as an example, take an airline, right? So internally there might be some sort of scheduling app that decides which pilots in which crew on which flights, well that’s critical mission critical digital service that is been built into an application that needs to be up and running 24 by 7 if that goes down. Clearly the productivity, the efficiency of that organization falls precipitously. Similarly, like the check-in app or the bag tracking app or just the mobile app in general. Like all of those things, you add it up for that airline they literally have hundreds of different digital services that need to be up and running. A historical approach was people in process. We've got a staff, very large organization to keep an eye on these things, collecting data and kind of doing manual pattern matching and correlation. And we're converting as much of that as we can into the software, to put it plainly. Like if your- if your end users are detecting that you're down before your own I.T. Team detects that you're down, you probably need Big Panda. That is kind of the way, the way I think about it, now how does it work? And I’m coming into your question about the integrations. Well, it works by first kind of gathering all of the disparate moderating and observability data throughout the environment, topology as well, change data, historical knowledge, etc.. This is all kind of institutional knowledge sitting in 20 or 30 different platforms. You got to bring it together and you got to bring the data together. You got to translate it or normalize it all into a single language. And only then can you start to transform that into actionable intelligence. And once you've done that, okay, now I can do triage and I can investigate and I can route it to the right team to repair and a bunch of other things. It all starts with that, that sort of data collection. And to the point you made earlier, do you have to integrate with everything? To do this well, you have to be agnostic to all the different data sources. And you can't say, well, I will do AIOps but only for my own moderate data, because, well nobody has one, if you go to a large organization. Well, a couple of things. First off, they've got many different generations. They have mainframes, maybe they have servers, they have VMs, they have cloud. Now they have microservices in the cloud and hybrid cloud with this or multiple generations of infrastructure and services. And those monitoring tools have been deployed over the years, kind of all in that same generation. So inevitably you're going to have dozens, if not hundreds of different sources, and you got to be agnostic to all of that. Just like one big mindset change. So if someone Big Panda comes in, we're not looking to displace any of that. We want to integrate with all of that. We want to get more intelligence actionability out of those data sources. That's kind of like to the, let's say to the left or upstream. Yeah. Yeah. The front end. On the back end. Well once you've taken an incident and you’ve detected it and you verify that it's actionable and you kind of have a sense of what to do, one of two things you either know exactly how to go fix it. Let's go restart a server and you want that to be fully automated and you need to connect the panel platform into whatever automation platform might be best suited for that particular remediation. Or you don't know exactly what to do, but you know who to talk to. And now I need either open a ticket or a slack or a page somebody through my on-call system. So there's sort of automated mobilization or collaboration component built in there as well. So again, like every team, every company has a different platform. Within one organization you might have some folks that want to work out of a ticketing system, some people might want to work out of a chat. It's more of a chat ops approach, whether that's just sort of like, by persona or maybe it's like this half of the business or it’s by business unit is pretty common. So the mindset again is agnostic. We try to connect to all the different sources around us in the ecosystem. Yeah. And then I think sort of closing the loop on that too, right. So you've got a service experiences some sort of operational disruption, let's say, whether it's the the pilot scheduling app or whatever it is that's going to generate some some noise, probably not just from one thing, but from multiple things, right? Maybe from multiple generations of those monitoring tools that you were describing, because this is a an application that's probably evolved over an extended period of time. So you sort of centralize all of that, determine what do you want to do about it, go do something or notify someone, but then close the loop on that once the service is restored to sort of go back and say, hey, all these alerts cleared, I need to maybe append a note to this ticket or maybe the automation did something. And I want to, you know, notify some group or, you know, put some sort of audit activity against that or some sort of log dump or something and then close that ticket. Right. And I think what's really powerful about that sort of scenario is you can do all of that without a human being involved. Right. Potentially. Right. Whether you want to or not is a different story, I think. But yeah. Here's how I think about that. The software, you know, not any time soon, dare I say never, we’ll be able to do all of that fully autonomous remediation for a pattern that's never been seen before. But for patterns that have been seen before. The goal is to automate as much as you can so it might start with something simple like, okay, well, let's just say that based on this topology, when these five alerts go off, it's really only a one incident. And let me just automate it down to two, correlating to a single incident first and second. Once I know that I know who's responsible for that, I'm going to go talk to that team. Sure. And then later, after you fix it, three or four or five times, they keep getting that repeat escalation. They might say, you know what, here's a run book. When that happens again, execute this run book. Let me know if it fails, but otherwise, just run the run book. And then for that one pattern, you just went through kind of a maturity

lifecycle that went from: 24:13

I've never seen it before. I don't know what to do to let's fully automate the remediation of this particular incident. We have built Big Panda to help progress every kind of pattern as far to the right as you can to that maturity model. Yeah, that's great. I mean, one of the I think a lot of low hanging fruit that we've seen with customers is they already have some of the definitions of what to do. Like they have those SOPs, right? Hey, when this happens, go do this. And today they're still executing that with people. That becomes a huge opportunity like right off the bat to see some pretty immediate value by being able to automate those like the things that are known, right? The known patterns, the known issues, and then sort of work to the right, like you were saying, and become more mature over time. Yeah. Know, and frankly, that's exactly how our customers have experienced Big Panda. I used to spend all of my day kind of chasing repeat issues and firefighting and, you know, I've had multiple customers say something like, we never used to have time to even go to the bathroom before Big Panda. But now, like day by day, week by week, platform gives me time back what I used to what I used to kind of waste time like your bad escalations or, you know, manual toil kind of, you know, experiences having to search and swept around between different tools. It all gets a little bit more organized. And it's, it's, it's less. So I think it's that time for the response team to kind of up their game and start to get into, well, how would I approach this if my goal was to never have that kind of incident happen again? It's a very different mindset than just like trying to make it through your eight hour shift kind of thing and not having the time. You'll never kind of grow out or mature out of that mindset. So it’s been a very frankly, personally rewarding component of working at Big Panda is the idea that your software helps an organization get better over time and helps like career progression in a way, as those first line incident response teams become second line and third line and subject matter experts by virtue of having more time to to invest there. Yeah, no, I think I think, you know, said another way, right? We're helping customers sort of focus on the things that are important and not getting clobbered by hundreds of thousands of incidents every month for the same things over and over and over again. Right. Shifting gears a little bit. So so you're the Chief Product Officer, right? So who better to ask about the future of Big Panda? Right. The vision, where are you investing? What's your outlook for the next 12, 24 months is pretty far out in our in our business these days. But what are you looking at? Yeah, that's funny. You know, in enterprise software, sometimes you'll be asked for like a five year roadmap, like, okay, let me see what I can come up with. One to two years. Pretty normal, though, so a few things are top of mind for me. So like, look in SaaS, ease of use is the name of the game. The easier product will beat the otherwise better product pretty much every time. So while I would say our software is comparatively quite easy to use compared to a lot of others out on the market, especially sort of like, you know, platforms born on-prem, it's not necessarily best in class. If you think about SaaS in general, we got to fix that. So what we’re building now is more kind of in product setup and product enablement. Learning sort of gamifying the experience, both for like kind of the operators, the end users as well as the admins, right. I don't want to have to like send an army of professional services teams out there necessarily to to kind of teach the basics. You know, I want them to be consulting and figuring out how to transform the organization. The product set up part should be kind of, you know, fully out of the box. That also makes the product in the context of this discussion quite a bit more Channel ready also. Right. So we have to kind of automate and and standardize the way we do deployment. We can put some of that on the product and we can leverage on those channel partners to go much further. So that's one big component that is a big focus for us. Ease of use in general. Second thing is moving towards fully autonomous. So if the goal is over the next two years to automate either all of or as much as possible of that first line incident management function, by this I mean kind of detection, triage, routing, what happens? How important is this thing right now and who would I go to or what run book would I go to if I were to fix it? Right now, there's a human in the loop. At most organizations, we want to kind of steadily remove the human from the loop. Like I said before, you never going to have it be fully autonomous. But for the repeat incidents, kind of the way that human will look at as far as possible as you can before somebody has to look at manually. This is about quality of life efficiency and performance and cost and that's just like enormous benefits. Every little step you make. And in that regard, we're good and we're well along the way in this area. But there's there's more to be done. The third thing then is, is kind of switching from a very responsive mode, like I will detect as quickly as possible sort of what happened and help you fix it right away to getting on the other side of that, and starting to predict and then ultimately prevent incidents. So by virtue of having access to a lot of data, sometimes you see a lot of telltale signs that are just kind of warning messages here and there aren't enough to say something's broken and go fix it right now. But there are early warning signs that indicate, hey, something is about to happen. And identifying those patterns specifically for the highest risk incidents is something we're already researching to create, basically, incident prediction or predict that a major incident is going to happen in the next one hour. Here's the reasons why. And then the human operators can can jump on that one early and say, okay, well, number one, maybe I can prevent it if we if we can react quickly enough and you'll never be able to react quickly enough unless you have some software to help you out. Or number two, even if I can't fix it quickly enough, I can at least maybe shave an hour off of the the time of repair for such an incident. So it's kind of valuable in either direction. Awesome. But ease of use, automation and kind of becoming predictive are the main themes in our roadmap. Awesome. No, that's great. Good. Good to hear. So in that last answer, you mentioned the Channel. All right. So we're obviously a part of the Big Panda Channel partner program. Can you describe Big Panda’s relationship with the Channel, what it means to Big Panda, how you leverage it and how customers ultimately can get value out of the Channel? Yeah, and I’ll kind of start by just kind of summarizing where we are right now in our journey. I mentioned that we're still early in a way I would say from a market perspective, we're quite early, but we have a very eager and energized, I might even say, zealous customer base that is seeing kind of a new way of working, kind of the new way of doing incident management, if you will. And they're really excited about it. I feel like we've caught lightning in a bottle here a little bit inside of this platform in this company, especially with the acceleration of digital transformation and the criticality of digital services that we're seeing is a big moment for us right now. This is our hockey stick moment and we're looking to scale as quickly and efficiently as we possibly can. I can't overstate that opportunity enough and we can't do that by ourselves. So if I think about the importance, I think the Channel partners are just critical to to our success and ultimately to our customers. The market success in general. We believe that a majority of our revenue should come relationship to or even through Channel partners. Not that it's a side project, but it's like the main way of of integrating, delivering value to the market. And with the right partnerships, we think, look, there's literally tens of thousands of companies just in North America, let alone the rest of the company that are are potentially the the right market for this and have the same kind of pain points and problems. If they don't have it now, chances are they're going to have it over the next two years as their business grows as well. So, you know, just a massive opportunity here. And from our perspective, we need Channels to continue to scale and be efficient about that. No, that that's awesome and the relationship has been has been great. We've been growing with you guys and I think one of the things that we and not just us but all Channel partners is that we sort of see the world through a wider lens. Right. And a lot of those front end we use the front end, back end or upstream downstream integrations that we talked about. You know, we have a lot of relationships with an experience and expertise with a lot of those technology partners and being able to sort of stitch the front end to the back end with with you guys in the middle, I think puts us in a position to be able to add value to not just yourselves but ultimately to customers, which is what it's all about in the end anyway. It's very well said that we feel like we're kind of experts on how to automate first line incident management, and we look for our Channel partners such as CDI to be experts on you know, customer by customer. Well, this is their environment, this is their ecosystem, this is their current org structure and the current process. And to go from A to B, here's all the steps that that has to take. Kind of knowing the domain and all of the players and the easiest pain points to to go attack first. Look that just takes time and relationships to get to that level of intimacy with the customer and having a Channel partner that can kind of help pull you into the right opportunities and frankly steer you away from when this isn't the right time for that organization. That's that's still a win win from my perspective. Awesome. So that I think we'll just about wrap it up for us. Any, any last words, any anywhere you'd like to direct anyone watching this to go learn more about Big Panda? I'll say two things here. To learn a little bit more about Big Panda, just head over to our website BigPanda.io and take a take a look there. You can hear a little bit more. There's a take a tour link up in the upper right hand corner. I would love for you to take a look there a little a little bit more about the product. And if you're interested from a customer perspective, we’ll be happy to to connect with you. Will, if I could, though, what I would like to end on is just ask you a question. You have recently become not only a partner, but a customer of Big Panda’s just in the last few weeks. Can you tell me a little bit about the reasons that led to that and some of the pain points that you had internally that kind of led to that selection? Yeah. Yeah. So happy to. So, you know, for those who who aren't aware, CDI’s business model, we have a couple of ways that we conduct business. So we partner with technology organizations like Big Panda and many others and the sort of data center and cloud and modern applications and I.T. operations ecosystem. And we are a reseller, right, of technology. We're also a professional services integrator of technology. So a customer makes an investment. They need to implement that technology, integrate that technology, operationalize it somehow. So we have a, a suite of services that's designed to do that. And then we have our modern I.T operations business which would align to the ongoing sort of optimization and operationalization of customers technology investments. Right. So we've recently partnered with Big Panda to incorporate their technology as part of the stack with which we use to help our customers make sure they keep their digital services running. Right. So a lot of the front end integrations in terms of monitoring, we have established partnerships and we provide monitoring services. In some cases, customers bring their own tools and then they rely on CDI and our our modern I.T. Operations practice to to ingest that information and then provide remediation services on the back end with the full sort of closed loop incident management experience. So the problems that, that, that we faced are similar to the ones that you described at the beginning of this episode. Right. So it's doing more with less providing a faster and more efficient path to an outcome. Those are the types of reasons why we decided to partner with you and I think in general, right. You know, the partnerships at CDI, we've got hundreds of partners, right, in our ecosystem and the ones that we can sort of check all three boxes. So and sort of we can resell their platform, we can implement and integrate their platform with our services team and then be a customer of that technology to integrate it as part of our managed services offerings with our modern I.T operations group. Those are the ones that tend to be the most fruitful relationships for us, right? So we sort of check all three boxes and that's true of, you know, several of our partners sort of check all, all of those three boxes, not all do, but the ones that are most strategic certainly do. So I appreciate the question. And we're like you mentioned, it's recent. It's we're just getting started and we're very excited about the outlook. So you're a good person to know. I think if we if we have any trouble. I'm calling you! Let me know, you know how to reach me. No, in serious, though, I love that mentality because the more you use the software to solve your own internal problems, you'll be in such a better position to do the other two legs of the stool, the reselling and the delivering services around the product. So I see it a very strong win win win for the customers as well. Yeah, I mean, it's good for everybody all around, right? And in the end, that's what counts. So. Well. Hey, Fred, awesome getting to talk to you. Really appreciate it. I hope you come back sometime soon and enjoy the nice weather out there and. Will do. Well. We'll see you back soon, Will. Thanks so much. Awesome. Thank you very much. Take care. The CDI CTO podcast is brought to you by CDI, hosted by Will Huber and produced by Alyssa Hall and Spencer Grogan. To learn more about CDI, you can visit CDILLC.com. The CDI CTO podcast is a production of CDI Studios.