>>Okay, welcome back everyone to the Cube's live coverage here in Las Vegas for Amazon re Mars two days of coverage, we're getting down to wrapping up day one. I'm John furrier host of the cube space is a big topic here. You got machine learning, you got automation, robotics, all spells Mars. The two great guests here to really get into the whole geo scene. What's going on with the data. We've got Marcus Norren business development and geo data. Sogeti part of cap Gemini group, and Yoki well kissed portfolio lead data and AI with Sogeti part of cap, Gemini gentlemen, thanks for coming on the queue. Appreciate it. Thanks >>For having us. >>Let me so coming all the way from Sweden to check out the scene here and get into the weeds and the show. A lot of great technology being space is the top line here, but software drives it. Um, you got robotics. Lot of satellite, you got the aerospace industry colliding with hardcore industrial. I say IOT, robotics, one, whatever you want, but space kind of highlights the IOT opportunity. There is no edge in space, right? So the edge, the intelligent edge, a lot going on in space. And satellite's one of 'em you guys are in the middle of that. What are you guys working on? What's the, the focus here for cap gem and I Sogeti part of cap >>Gemini. I would say we focus a lot of creating business value, real business value for our clients, with the satellites available, actually a free available satellite images, working five years now with this, uh, solutioning and, uh, mostly invitation management and forestry. That's our main focus. >>So what's the product value you guys are offering. >>We basically, for now the, the most value we created is working with a forest client to find park Beal infests, uh, in spruce forest. It's a big problem in European union and, uh, Northern region Sweden, where we live now with the climate change, it's getting warmer, the bark beetle bases warm more times during the summer, which makes it spread exponentially. Uh, so we help with the satellite images to get with data science and AI to find these infestations in time when they are small, before it's spread. >>So satellite imagery combined with data, this is the intersection of the data piece, the geo data, right? >>Yeah. You can say that you have, uh, a lot of open satellite data, uh, and uh, you want to analyze that, that you also need to know what you're looking for and you need data to understand in our case, a certain type of damage. So we have large data sets that we have to sort of clean and train ML models from to try to run that on that open data, to detect these models. And, and when we're saying satellite data and open data, it's basically one pixel is 10 by 10 meters. So it's not that you will see the trees, but we're looking at the spectral information in the image and finding patterns. So we can actually detect attacks that are like four or five trees, big, uh, using that type. And we can do that throughout the season so we can see how you start seeing one, two attacks and it's just growing. And then you have this big area of just damage. So >>How, how long does that take? Give me some scope to scale because it sounds easy. Oh, the satellites are looking down on us. It's not, it's a lot of data there. What's the complexity. What are the challenges that you guys are overcoming scope to scale? >>It's so much complexity in this first, you have clouds, so it's, uh, open data set, you download it and you figure out here, we have a satellite scene, which is cloudy. We need to have some analytics doing that, taking that image away basically, or the section of the image with it cloudy. Then we have a cloud free image. We can't see anything because it's blurry. It's too low resolution. So we need to stack them on top of each other. And then we have the next problem to correlate them. So they are pixel perfect overlapping. Yeah. So we can compare them in time. And then they have the histogram adjustment to make them like, uh, the sensitivity is the same on all the images, because you have solar storms, you have shady clouds, which, uh, could be used still that image. So we need to compare that. Then we have the ground proof data coming from, uh, a harvester. For instance, we got 200,000 data points from the harvester real data points where they had found bark Beal trees, and they pulled them down. The GPS is drifting 50 meters. So you have an uncertainty where the actually harvest it was. And then we had the crane on 20 meters. So, you know, the GPS is on the home actually of the home actual machine and the crane were somewhere. So you don't really know you have this uncertainty, >>It's a data integration problem. Yeah. Massive, >>A lot of, of, uh, interesting, uh, things to adjust for. And then you could combine this into one deep learning model and build. >>But on top of that, I don't know if you said that, but you also get the data in the winter and you have the problem during the summer. So we actually have to move back in time to find the problem, label the data, and then we can start identifying. >>So once you get all that heavy lifting done or, or write the code, or I don't know if something's going on there, you get the layering, the pixel X see all the, how complex that is when the deep learning takes over. What happens next? Is it scale? Is it is all the heavy lifting up front? Is the work done front or yeah. Is its scale on the back end? >>So first the coding is heavy work, right? To gets hands on and try different things. Figure out in math, how to work with this uncertainty and get everything sold. Then you put it into a deep learning model to train that it actually run for 10 days before it was accurate, or first, first ation, it wasn't accurate enough. So we scrap that, did some changes. Then we run it again for 10 days. Then we have a model which we could use and interfere new images. Like every day, pretty quickly, every day it comes a new image. We run it. We have a new outcome and we could deliver that to clients. >>Yeah. I can almost imagine. I mean, the, the cloud computing comes in handy here. Oh yeah. So take me through the benefits because it sounds like the old, the old expression, the juice is not worth the squeeze here. It is. It's worth the squeeze. If you can get it right. Because the alternative is what more expensive gear, different windows, just more expensive monolithic solutions. Right? >>Think about the data here. So it's satellite scene. Every satellite scene is hundred by a hundred kilometers. That pretty much right. And then you need a lot of these satellite scene over multiple years to combine it. So if you should do this over the whole Northern Europe, over the whole globe, it's a lot of data just to store that it's a problem. You, you cannot do it on prem and then you should compute it with deep learning models. It's a hard problem >>If you don't have, so you guys got a lot going on. So, so talk about spaghetti, part of cap, Gemini, explain that relationship, cuz you're here at a show that, you know, you got, I can see the CAPI angle. This is like a little division. Is it a group? Are you guys like lone wolves? Like, what's it like, is this dedicated purpose built focus around aerospace? >>No, it's actually SOI was the, the name of the CAPI company from the beginning. And they relaunched the brand, uh, 2001, I think roughly 10, 20 years ago. So we actually celebrate some anniversary now. Uh, and it's a brand which is more local close to clients out in different cities. And we also tech companies, we are very close to the new technology, trying things out. And this is a perfect example of this. It was a crazy ID five years ago, 2017. And we started to bring in some clients explore, really? Open-minded see, can we do something on these satellite data? And then we took it step by step together of our clients. Yeah. And it's a small team where like 12 >>People. Yeah. And you guys are doing business development. So you have to go out there and identify the kinds of problems that match the scope of the scale. >>So what we're doing is we interact with our clients, do some simple workshops or something and try to identify like the really valuable problems like this Bruce Park people that that's one of those. Yep. And then we have to sort of look at, do we think we can do something? Is it realistic? And we will not be able to answer that to 100% because then there's no innovation in this at all. But we say, well, we think we can do it. This will be a hard problem, but we do think we can do it. And then we basically just go for it. And this one we did in 11 to 12 weeks, a tightly focused team, uh, and just went at it, uh, super slim process and got the job done and uh, the >>Results. Well, it's interesting. You have a lot of use cases. We gotta go down, do that face to face belly to belly, you know, body to body sales, BI dev scoping out, have workshops. Now this market here, Remar, they're all basically saying a call to arms more money's coming in. The problems are putting on the table. The workshop could be a lunch meeting, right. I mean, because Artis and there's a big set of problems to tackle. Yes. So I mean, I'm just oversimplifying, but that being said, there's a lot going on opportunity wise here. Yeah. That's not as slow maybe as the, the biz dev at, you know, coming in, this is a huge demand. It will be >>Explode. >>What's your take on the demand here, the problems that need to be solved and what you guys are gonna bring to bear for the problem. >>So now we have been focus mainly in vegetation management and forestry, but vegetation management can be applicable in utility as well. And we actually went there first had some struggle because it's quite detailed information that's needed. So we backed out a bit into vegetation in forestry again, but still it's a lot of application in, in, uh, utility and vegetation management in utility. Then we have a whole sustainability angle think about auditing of, uh, rogue harvesting or carbon offsetting in the future, even biodiversity, offsetting that could be used. >>And, and just to point out and give it a little extra context, all the keynotes, talk about space as a global climate solution, potentially the discoveries and or also the imagery they're gonna get. So you kind of got, you know, top down, bottoms up. If you wanna look at the world's bottom and space, kind of coming together, this is gonna open up new kinds of opportunities for you guys. What's the conversation like when you, when this is going on, you're like, oh yeah, let's go in. Like, what are you guys gonna do? What's the plan, uh, gonna hang around and ride that wave. >>I think it's all boils down to finding that use case that need to be sold because now we understand the satellite scene, they are there. We could, there is so many new satellites coming up already available. They can come up the cloud platform, AWS, it's great. We have all the capabilities needed. We have AI and ML models needed data science skills. Now it's finding the use cases together with clients and actually deliver on them one by >>One. It's interesting. I'd like to get your reaction to this Marcus two as well. What you guys are kind of, you have a lot bigger and, and, and bigger than some of the startups out there, but a startup world, they find their niches and they, the workflows become the intellectual property. So this, your techniques of layering almost see is an advantage out there. What's your guys view of that on intellectual property of the future, uh, open source is gonna run all the software. We know that. So software's no going open source scale and integration. And then new kinds of ways are new methods. I won't say for just patents, but like just for intellectual property, defen differentiation. How do you guys see this? As you look at this new frontier of intellectual property? >>That's, it's a difficult question. I think it's, uh, there's a lot of potential. If you look at open innovation and how you can build some IP, which you can out license, and some you utilize yourself, then you can build like a layer business model on top. So you can find different channels. Some markets we will not go for. Maybe some of our models actually could be used by others where we won't go. Uh, so we want to build some IP, but I think we also want to be able to release some of the things we do >>Open >>Works. Yeah. Because it's also builds presence. It it's >>Community. >>Yeah, exactly. Because this, this problem is really hard because it's a global thing. And, and it's imagine if, if you have a couple of million acres of forest and you just don't go out walking and trying to check what's going on because it's, you know, >>That's manuals hard. Yeah. It's impossible. >>So you need this to scale. Uh, and, and it's a hard problem. So I think you need to build a community. Yeah. Because this is, it's a living organism that we're trying to monitor. If you talk about visitation of forest, it's, it's changing throughout the year. So if you look at spring and then you look at summer and you look at winter, it's completely different. What you see. Yeah. Yeah. So >>It's, it's interesting. And so, you know, I wonder if, you know, you see some of these crowdsourcing models around participation, you know, small little help, but that doesn't solve the big puzzle. Um, but you have open source concepts. Uh, we had Anna on earlier, she's from the Amazon sustainability data project. Yeah, exactly. And then just like open up the data. So the data party for her. So in a way there's more innovation coming, potentially. If you can get that thing going, right. Get the projects going. Exactly. >>And all this, actually our work is started because of that. Yes, exactly. So European space agency, they decided to hand out this compar program and the, the Sentinel satellites central one and two, which we have been working with, they are freely available. It started back in 2016, I think. Yeah. Uh, and because of that, that's why we have this work done during several years, without that data freely available, it wouldn't have happened. Yeah. I'm, I'm >>Pretty sure. Well, what's next for you guys? Tell, tell me what's happening. Here's the update put a plug in for the, for the group. What are you working on now? What's uh, what are you guys looking to accomplish? Take a minute to put a plug in for the opportunity. >>I would say scaling this scaling, moving outside. Sweden. Of course we see our model that they work in in us. We have tried them in Canada. We see that we work, we need to scale and do field validation in different regions. And then I would say go to the sustainability area. This goes there, there is a lot of great >>Potential international too is huge. >>Yeah. One area. I think that is really interesting is the combination of understanding the, like the carbon sink and the sequestration and trying to measure that. Uh, but also on top of that, trying to classify certain Keystone species habitats to understand if they have any space to live and how can we help that to sort of grow back again, uh, understanding the history of the, sort of the force. You have some date online, but trying to map out how much of, of this has been turned into agricultural fields, for example, how much, how much of the real old forest we have left that is really biodiverse? How much is just eight years young to understand that picture? How can we sort of move back towards that blueprint? We probably need to, yeah. And how can we digitize and change forestry and the more business models around that because you, you can do it in a different way, or you can do both some harvesting, but also, yeah, not sort of ruining the >>Whole process. They can be more efficient. You make it more productive, save some capital, reinvest it in better ways >>And you have robotics and that's not maybe something that we are not so active in, but I mean, starting to look at how can autonomy help forestry, uh, inventory damages flying over using drones and satellites. Uh, you have people looking into autonomous harvesting of trees, which is kind of insane as well, because they're pretty big <laugh> but this is also happening. Yeah. So I mean, what we're seeing here is basically, >>I mean, we, I made a story multiple times called on sale drone. One of my favorite stories, the drones that are just like getting Bob around in the ocean and they're getting great telemetry data, cuz they're indestructible, you know, they can just bounce around and then they just transmit data. Exactly. You guys are creating a opportunity. Some will say problem, but by opening up data, you're actually exposing opportunities that never have been seen before because you're like, it's that scene where that movie, Jody frost, a contact where open up one little piece of information. And now you're seeing a bunch of new information. You know, you look at this large scale data, that's gonna open up new opportunities to solve problems that were never seen before. Exactly. You don't, you can't automate what you can't see. No. Right. That's the thing. So no, we >>Haven't even thought that these problems can be solved. It's basically, this is how the world works now. Because before, when you did remote sensing, you need to be out there. You need to fly with a helicopter or you put your boots on out and go out. Now you don't need that anymore. Yeah. Which opened up that you could be, >>You can move your creativity in another problem. Now you open up another problem space. So again, I like the problem solving vibe of the, it's not like, oh, catastrophic. Well, well, well the earth is on a catastrophic trajectory. It's like, oh, we'll agree to that. But it's not done deal yet. <laugh> I got plenty of time. Right. So like the let's get these problems on the table. Yeah. Yeah. And I think this is, this is the new method. Well, thanks so much for coming on the queue. Really appreciate the conversation. Thanks a lot. Love it. Opening up new world opportunities, challenges. There's always opportunities. When you have challenges, you guys are in the middle of it. Thanks for coming on. I appreciate it. Thank you. Thanks guys. Okay. Cap Gemini in the cube part of cap Gemini. Um, so Getty part of cap Gemini here in the cube. I'm John furrier, the host we're right back with more after this short break.

(upbeat instrumental jazz music) >> Announcer: From our studios in the heart of Silicon Valley, Palo Alto, California, this is a CUBE conversation. >> Hello and welcome to theCUBE Studios in Palo Alto, California for another CUBE Conversation where we go in-depth with thought leaders driving innovation across the tech industry. I'm your host, Peter Burris. Everybody's talking about digital business and the transformation to digital business, and the promise that it heralds for better customer experience, new levels of business productivity, and, quite frankly, the types of changes that are going to save humanity in certain respects. But in those conversations, we too often focus on the technology, the applications, what we're going to do with A.I., what we're going to do with machine learning, and not enough about the people. In fact, often it's presumed that we're going to dislocate or displace a whole lot of people, but the simple reality is every system features work by people using systems to improve their productivity. And it's time that we focused more attention on how we're going to improve the productivity of people as they use technology to undertake more complex work that is uniquely required, or that uniquely requires human capacities. Now, big topic, but we've got a great conversation. We're joined by Dejan Deklich, who's a Chief Product Officer of 8x8 to talk about this. Dejan, welcome to the cube. >> Thank you, Peter, pleasure. I'm delighted to see your amazing studio over here. >> Well thank you, Dejan. So let's start, 8x8, tell us a little bit about 8x8. >> So 8x8 has been in business for a really long time. We have been public for 30 years now, give or take. We are in the communication and collaboration business. We are now close to 2000 people worldwide. And I have to tell you, I have offices all over the world, and I have remote employees all over the world, and I can tell you a lot about learning how to deal with people, at scale, in remote locations with different languages and different laws surrounding them. >> Well let's jump into it. So I mentioned up front this notion that we have to move more attention to the people side of the equation. Certainly in the U.S., despite all these significant improvements in technology, we still face an employment issue. We are at full employment. So clearly, we're using people to do things, but the question, especially to use your scenario, 2000 people company, offices all around the world, serious people doing serious work. What can we do, ultimately, to improve the productivity of how those people work together from a variety of different perspectives? >> Right, so that's a problem that I struggle with on the daily basis. Imagine, 20 years ago, you had everybody sitting in the cube next to each other, talking to each other, going for lunch together. Then maybe 10-15 years ago, we moved to remote teams, everybody spun up a team in Asia, spun up a team in Europe. Now you are in the world where you have remote individuals working literally all over the world. How do you get them all together is really difficult. One thing that we tried to do at 8x8 was literally fly in all the newbies that we hire every month into San Jose, and introduce them to the company. Works great, but the cost is enormous. So we have now shifted to a much more video-based, enabling of individuals and teams. So what we did at 8x8, is we started this new product line, 8x8 Video Meetings, with exactly this goal in mind. I wanted to have a way to reach out any individual, wherever in the world they might be, with minimum amount of drama, with minimum amount of impact on their day-to-day work. I just want the teams to collaborate and communicate. And I think we have seen in the past that there were plenty of research studies, Frost and Sullivan is one of them, Hanover Group is another one, which say that the impact of video communications on the teams, on the decision making, on simplification of day-to-day business, is huge. And, to me, that is the key for the next ten to twenty years in our industry. >> So, Dejan, I've also had some experience with large teams on a global basis. And each time that some new technology came out, folks flocked to that new technology, whether it was email, and then text, or then, you know, collaboration, some sort of video collaboration. But it always seemed to me, as though, those silos, those became silos. They became independent channels for how you work with people, and the choice that you made about how to set the meeting up really constrained what you could do in the meeting. It seems as though it's time to think about how all these different communication mechanisms can come together in a common platform so that you can choose what you need to use at the time that you're trying to effect the communication. Have I got that right? >> I think you stole my words, they are perfect. Look, think of it also, it's more complex than that. You also have people from lots of different generations. You have millennials, who first thing they will do is whip their phone and start texting somebody. You have, you and me, who are not necessarily millennials anymore, who will likely start the conversation with a phone call. And you go, my job as a Chief Products Officer of 8x8 is to figure out, how do I get all these people working together? And we have seen that there is an enormous value in having a unified platform which allows everybody to choose whichever mode of communication they want to engage in. So, if you know, you are a bit older millennial like myself, I will start with a phone call, and then I will hop into a video meeting. For the younger kids in the company, they will probably start it with chat, text, and then go possibly to the phone if the conversation becomes too long, and then eventually into a meeting. To me, the key for every enterprise and mid-market customer out there is how do you put all of this information together so you really know what your employees are doing, and what your customers are doing. >> Yeah, I'll give you a great example of that, some of the customers that I'm working with these days, is this whole notion of evidence-based management, which is in many respects, the manifestation of A.I., M.L., and some of this other stuff, analytics into how business managers actually operate. It's very difficult to communicate findings from some of these models using text, or even using voice. You need images, you need pictures, but you don't want to just send a static file. You would rather be explaining something to, you know, the finding of the model, the outcome of the model, to your executive. Observe whether or not they look confused. How do you envision some of these new application styles that we're building for some of these new digital approaches, pulling video into the conversation that much more? >> Exactly, look, I think what you said is the key. We are humans, and we evolved through interactions with each other. If I look at you and I see you are smiling, probably my presentation is going to go slightly differently than if I see you go really upset at whatever I'm showing. Putting a solution together that allows you to share the screen, talk in really high definition audio and video, as well as see the face of the person you are talking with is the key to me. And then, as you think about it going forward, starting to actually record the conversation, start to extract the knowledge out of this conversation. A lot of times in the meeting, somebody will say something really, really smart. Mostly, by the end of the meeting, it's 45 minutes later, you forgot about it. If you have a recording, if you have a transcript, you can actually do something with that information. So to me, it's all about, remove the barriers, extract as much information from this conversation as possible, and then, if possible, provide the enterprise with the API where they can get all the information in some form of digital download. So, I personally, I'm a huge believer of M.L. and A.I. that you opened up with, and I believe that getting a lot of this information together will really change how we think about operations, and how we think about running remote teams and local teams. >> I think that one of the important things, and I think you mentioned, I can kind of pull this together, is that video by itself is often difficult to search. But when you combine video and text through transcriptions, translations, et cetera, now you've got something that's searchable, but you still are able to retain the power of the video. Is 8x8 looking at this as part of a unified platform? And if you are, it suggests that these are not things that you regard as wholly distinct, but as part of the fundamental challenge of, how do you improve communication inside a business. >> Exactly, so when I started two and a half years ago, the first thing I did is I started the journey on the fully unified analytics platform. I want to have all the text messages, all the phone calls, all the transcriptions of all the meetings, all the contact center information, I want everything in one place, so I can then start deploying my M.L. and A.I. models across the data. I tend to believe we are uniquely positioned to do that, because not only do we have the actual product lines, but we also have the captive audience, in form of a customer on the phone, or in the meeting, or calling a support team in the contact center. Putting all of that together, and getting the insights that drive human behavior, to me, is the absolute key for the industry. If I can know what problems you are facing, and if I have the context of your problem, I can probably solve your issue much, much faster than if it is the first conversation of a type, please give me your mother's maiden name, and last four digits of your Social Security Number. >> Well, and I want to build on that 'cause, here at theCUBE, we obviously use video pretty extensively. And how we turn the conversations we're having into concepts, or knowledge, or artifacts that users can use to make decisions. We've found, and this is what I want to test with you, that something really interesting happens, there's a lot of research to support this. You, as you mentioned, we are humans with bicameral vision. Most of the information we get, we come in through our eyes. It just is that way. We're tuned for that. And so, when you're looking at a face or you're having a conversation with someone, and that face is available to you, as part of the interaction, you just listen better, you retain better, you focus harder, you pay more attention. And it seems as though video is an absolute essential feature, or it must be an essential feature, of how we improve communication, especially if we're going to ask people to take on more challenging tasks, perform more challenging work that feature higher risks. What do you think about that? >> I agree, and I think there is one more little point before all of that. The usage of the product has to be super simple, and it has to be incredibly intuitive. You do not, my regular example is I'm always two minutes late to a start of the meeting, and then if I get asked, "Oh, now please download some plugin so you can start the meeting, blah, blah, blah," the time has gone by, now I'm fifteen minutes late to a a meeting. Then people yell at me, generally, because I'm late. Well, with 8x8 meetings there is no need to download any plugins, and you remove this barrier to entry into the conversation. To me, that is almost the key to the whole thing. Just like the phone is, by now, intuitive for everybody, just like texting, video has to become exactly the same, where, we need to communicate, well let's just hop in it, let's talk it through, let's see how we each react to it, and then we go move forward. >> You know, I think it's a great point. If the technology generates stress in the conversation, you've diminished the productivity of the conversation. One of the biggest challenges that CIOs face today is the business is applying, is going after all these new opportunities with technology in mind, but if you don't get the enterprise to adopt the technology, it fails. And so you really have a challenge of abandonment. It's not just that that individual phone call loses productivity, but the entire approach to how you conduct business gets abandoned, and you don't want that. So by doing it more simply, you get better results. So what kind of experience have your customers been enjoying as they use 8x8, advance some of these new technologies, and what do you anticipate for their use of video? >> So, the way I see it, there are almost two categories of customers that videollate at 8x8. There is the relatively simple customer, the small mid-market customer, and then when you enter the enterprise, all hell breaks loose. The complexity starts exploding. We have customers that have deployed us at 4000 locations worldwide. Imagine operating a system at that scale. And you go, you are not only talking different locations, you are talking different legal jurisdictions, you are talking different geographies, different continents. Putting all of that together, and simplifying this communication is the key for the customers. And I have seen again and again, CIOs try to force their workforce onto a platform of choice, right? And, one of my friends who is a CIO here in the valley, says the easiest way for a CIO to get fired is to force sales and engineering on the same text messaging or video meeting solutions. One group will get you fired. So you go, if you go with 8x8, suddenly you can have everybody on the same platform. The firing concept goes away, which is always good, and you enable massive gains in scale and in performance. You reduce the barriers to entry for all these people. >> But, let me explore why that is because I think it's an interesting concept. And I think what you're saying is that sales people typically use different workflows, require different classes of information, that can be rendered in different mechanisms, text, or whatever else it might be. Engineering is showing different workflows, different classes of people, different kinds of information. So trying to make engineering give up some of what they need, or sales give up some of what they need to try to make both happy, that's the prescription for failure, and you're saying that by being able to support all of those workflows, roles, and information forms, you get a more complete system? >> Exactly, you get a more complete system and, for you as a CIO who is deploying 8x8 or a similar tool, you suddenly get to see how your employees are actually interacting with each other, as well as how they're interacting with the end customer. To me, it is fascinating how much the computer science is changing the way people communicate with each other. I know who you are, I have a lot of information from the web around you, maybe I can tailor this communication specifically for you. To me, that is the path forward to the future. Using all of this data about you as a person in the context of the enterprise is the key. >> So the right tool for the right conversation and the right roles, but, still with the opportunity to do derivative analysis as you bring all that information together later. >> Exactly, the analysis is the key. So we have seen all sorts of really interesting things happen at 8x8 as we are putting more and more of our internal employees on these tools. You start seeing inefficiencies in support. You start seeing inefficiencies on the sales side. And you go, "Well, before I had no idea, I did not know that my sales people are not calling, following people in sales force." Well, now I can see it. I can actually do something about it, and I don't need analysts who will write me reports and build Tableau data sheets and whatnot. I can see day-to-day what is going on with my labor force and employees. >> Excellent! Dejan, thanks very much for being on theCUBE. >> Thank you so much! This was a lot of fun. >> So, Dejan Deklich from 8x8, Chief Product Officer, thanks again for joining us, for another CUBE conversation. I'm Peter Burris, see you next time. (upbeat instrumental jazz music)

(energetic music) >> Announcer: From our studios in the heart of Silicon Valley, Palo Alto, California, this is a Cube Conversation. >> Hi, welcome to our Palo Alto, California studios for another Cube Conversation, where we go in depth with thought leaders about some of the most pressing topics of the day in business and technology. I'm your host Peter Burris. One of the biggest challenges that any company faces is how to get more out of their people, even though we are increasingly distributed, we are increasingly utilizing digital means to interact and work together, and we are increasingly trying to do this with customers and with other third parties that are crucial to making business work, profitable, and grow revenue. A number of things have occurred in the last few years that are actually making it possible to envision how we can be more distributed and yet be more productive. And one of the most important ones is the use of video as a basis for connecting people. How're we going to to do that? Well, to have that conversation, we're here with David Maldow who's the CEO of, Let's Do Video. David, welcome to theCUBE. >> Hey, thanks for having me Peter, appreciate it. >> So, tell us a little bit about, very quickly, about, Let's Do Video, and then let's jump into it. >> Sure. Let's Do Video's, a boutique analyst blog on www.letsdovideo.com. We cover everything having to do with remote technology, anything that allows teams to be more productive whether they're working together or working across the country. >> All right, so in your name is, "video." Let's identify some of the key trends. What really is making it possible to utilize video in this way today where it really was nothing more than a promise made, put forward by a lot of companies 10 years ago. >> I think, well, there's been a lot of factors, but big part of that has been the cloud. A few years ago we had the big cloud software revolution in video conferencing. Before then you had to buy these expensive video appliances to have them at your workplace, and you really needed a team of experts to run them. By running the video in the cloud, all we need is our apps on our phones, and apps in our meeting rooms. And it makes it a lot easier, and it made it a lot more affordable. So, now it's available for everyone, and it was just a matter of whether we were ready for it, and appears that we are. >> So, we're getting the service that we need without having to worry about the technology that's required, the formats that are being employed, the operational complexities associated with video. Have I got that right? >> Yeah, actually there was a long list of reasons we weren't using video. Analyst like myself looked at the video conferencing industry and said, "Guys, you need to fix all of these things "or no one's going to use it. "It needs to be easier, one click to join. "It needs to be more affordable." The stuff was expensive. Needs to be reliable. Balls were dropping. It needs to use less bandwidth. It was taking over our networks. All of these things it needed to be, and they fixed all of that. And we promised if they fixed all of that, people would start to use it. Now we are seeing an absolute explosion in the market of people taking these apps into the workplace and actually using them. >> It seems to me, David, I want to get your take on this. That some of the early suppliers of some of these video related services were treating it largely as a means to an end, and typically that end was, what type of things can we put in the marketplace that's going to increase the amount of network bandwidth that's required so we can sell more networking equipment, or sell more networking services? Let me ask you a question. Because that has been fixed by utilizing the cloud. Does it now mean that we are getting a whole bunch of new technology companies that are stepping into the market place to provide video services as the end itself? And that's leading to better engineering, better innovation, and better customer experience? >> That's exactly what happened. We went from a top-down adoption model, to a ground-up adoption model. And what I mean by that is. It used to be a top-down thing, where these video conferencing companies would go talk to the CEO or CTO of a big company and do an amazing demo in the meeting room, and say, "look at this amazing video quality that you get." And they would show these studies that people like me help write (laughs), showing that if you do use video you'll be more productive. If you do use video you'll have more impact, and if you do use video you'll get all these benefits. So, buy this expensive stuff and then force your people to use them. And that didn't work 'cause they bought the stuff, and they tried to force people to use them. But, like we talked about, it was complicated. it was inconvenient. Now what's happening is, instead of the top-down we're getting the bottom-up. We're getting people walking into the workplace saying, "I'm using this app. I'm using this app. "I need video to talk to my teammates." And the boss CEO has to say, "Okay, okay, we'll accommodate that. "Don't use the consumer apps, though. "Let us find a nice business app that's secure for you." So instead of having, "You should use this "'cause we were sold on it." We're having a great new cloud video industry that's saying, "oh, let's give you what you want." >> So, when adoption happens from a bottom-up stand point, it means that the benefits have to be that much more obvious to everybody, otherwise, you don't get the adoption. So, what are some of the key productivity measures that this rank and file, this ground swell of interest in these technologies, are utilizing to evaluate and to judge how they want to use video within their business lives, workflows, engaging the customers, etc. >> For a long time it was just anecdotal. It just seemed obvious, if you, we all know that when you have a face-to-face meeting you get the work done. If it's a phone call, "oh, I'll explain to them why it's not done." We all know things get done more effectively in meetings. We all know a face-to-face meeting can last 20 minutes and get the work done. While a phone call can go on for hours. But now that we are starting to use it, instead of anecdotal, we're actually getting real data. Companies are reporting that they use to have a... Their web app development team used to take eight weeks before every release. Now they're doing it every six weeks. We're seeing real results. Frost & Sullivan, a big analyst firm in the space recently came out with some statistics. A survey of CEOs, CTOs, and they reported that using video among their team accelerated decision making. 86% of them agreed with that, 83% that agree, that it improves productivity, that's massive. 79% said it boosts innovation. So not only people getting more work done, more leading work, getting ahead of the competition, coming up with new things. And this is a huge one, 79%, this is self-reporting, believe that it improved their customer experience. We know, you know, the customer relationship is everything in sales. >> Why? >> Now we're actually measuring the results. >> Why is that, what is it about video that is so important to allowing us to not only accelerate workflows and achieve the outcomes, but also as we take on more complex workflows, even as we distribute work greater, what is it about video that makes the difference? >> There's a lot to it. I think a lot of it is that human connection. It's really hard to focus on a phone call. You lose track, I mean, you know, one of the reasons that my I named my company "Let's Do Video" is 'cause I'd be on the phone with a partner, a colleague, a teammate, and I'm like, "is he or she checking her email? "Did you hear, do I have to repeat what I just said?" We need to get work done, let's do video. And I think teams across all industries are finding that out now. Once they get on video, the work just gets done. >> But it's not just that they're on video, it's that they're utilizing video as a way of connecting with each other. That you can see whether or not somebody's paying attention to you at the most simple level. You can also register whether or not someone is a little bit agitated with what you're saying, even though you may not hear that on the phone. But video is being utilized as a way of adding to how other work gets done. It's not like we're suddenly, you know, putting a whole bunch of presentations up in the video. We're looking at faces, we're listening to people. We're having a connection as we work in other medium. Have I got that right? >> Exactly, yeah. I used to... When video conferencing first hit the scene 20 years ago, we were marketing it as a replacement for travel. Instead of flying across the country for that big meeting, you do it over a video. And what we realized is you still need to travel for that really, really big meeting once or twice a year, you still get on a plane. Video conferencing isn't getting rid of that niche meeting. It's not fixing that one big meeting, It's not cutting your travel costs. It's upgrading the phone call. It's upgrading the text message, the imChat. It's upgrading the e-mail. It's becoming, like you're saying, a part of how we're normally working. And it's changing the way remote workers see their teams. Let's Do Video, my team is completely remote. I've never met one of my teammates in person till we were two or three years in. We met up at an airport and said, "oh my God, I actually get to see you in three dimensions! "It's amazing!" And if we had started this company 10 years ago, I would say, I don't really have a team. I'm a sole guy, it's all me, I have some contractors. I send them an email, and a month later, they send me the result. But with video, I have a team, there's accountability. We're friends, we know what's going on with each other's lives. And there's a lot more motivation there, because instead of just, "Hey, you're my graphics person, "get this graphics for me. "You're my web person, fix the thing on the site." My colleagues, they're part of the team, and they want the company to succeed, 'cause they look at me in the face and they say, "I got this project done!" They feel good about it. It's a lot more of an investment, and it sounds like happy fluffy stuff, but it affects your bottom line. I don't think my... I know my company would not be as successful if I did not regularly meet with my team over video. >> Well, who doesn't want (laughing) a little bit of happy fluffy stuff every now and then? It's nice to bring a smile to your job. Let's pivot a little bit and just talk about the difference between internally to now externally. Because one of the other things that a lot of these video conferencing solutions offered, was they offered the opportunity to connect with video on a single network, your company's network with specialized end points. Now we're talking about trying to find new ways to enhance the experience that sales people have, service people have. Utilizing video to engage customers, to drive new types of experience, to drive new forms of revenue. How is video starting to alter the way we engage not just internally but also externally? >> That's more starting to happen than already happening. I think video in the workplace is becoming just a normal thing. I meet with my team over video. We're still finding ways to engage our externals. But the drive is definitely there, because we're seeing the results from working with our teams, and we know the impact. I think anyone in sales, they'll do anything to get that face-to-face meeting. They'll do anything to get you to come into their office or let you into their office to sit down. If you give a salesperson a choice between face-to-face or a phone call. That salesperson wants to be face-to-face. So, as we're getting the technology to make it easier for customers to get face-to-face with us, and partners, and externals. The demand will be there, and what's great is that the cloud enables that. The real problem is, like you said, they were on our own network. So, if I wanted to talk to a customer or a partner, I had to open a hole in my firewall, and let someone else into my network, and my IT people would go crazy. Now, the call's hosted up on whatever video conferencing company's cloud, it's safe. So, we're ready for that sort of thing. >> Lot of changes, lot of opportunities, tremendous potential. The types of changes we see in five years are going to dwarf the changes we've seen in the last five years. Again, as folks get used to using video internally, they're going to start demanding it as they engage each other externally as well. David Maldow, CEO of, Let's Do Video. Thanks for being on theCUBE. >> Thanks so much, this was fun. >> And once again, I'm Peter Burris. Until next time, thanks for watching. (upbeat music)

>> From our studios in the heart of Silicon Valley. HOLLOWAY ALTO, California It is a cube conversation. >> Hello and welcome to Cube conversations. I'm James Kabila's lead analyst at Wicked Bond. Today we've got an excellent guest. Who's a Cube alumnus? Par excellence. It's your own Haviv who is the founder and CEO of a guajillo. Hello. You're wrong. Welcome in. I think you're you're coming in from Tel Aviv. If I'm not mistaken, >> right? Really? Close the deal of any thanks from my seeing you again. >> Yeah. Nice to see you again. So I'm here in our Palo Alto studios. And so I'm always excited when I can hear your own and meet with your room because he always has something interesting in new to share. But what they're doing in the areas of cloud and serve earless and really time streaming analytics And now, data science. I wasn't aware of how deeply they're involved in the whole data Science pipelines, so ah, your own. This is great to have you. So my first question really is. Can you sketch out? What are the emerging marketplace requirements that USA gua Si are seeing in the convergence of all these spaces? Especially riel time streaming analytics edge computing server lis and data science and A I can you give us a sort of ah broad perspective and outlook on the convergence and really the new opportunities or possibilities that the convergence of those technologies enable for enterprises that are making deep investments. >> Yeah, so I think we were serving dissipated. What's happening now? We just call them different names will probably get into into this discussion in a minute. I think what you see is the traditional analytics and even data scientist Science was starting at sort of a research labs, people exploring cancer, expressing, you know, impact. Whether on, you know, people's moved its era. And now people are trying to make real or a Y from a guy in their assigned, so they have to plug it within business applications. Okay, so it's not just a veil. A scientist Inning the silo, you know, with a bunch of large that he got from his friends, the data engineer in the scan them and Derrickson Namesake runs to the boss and says, You know what? You know, we could have made some money in a year ago. We've done something so that doesn't make a lot of impact on the business, where the impact on the business is happening is when you actually integrate a I in jackpot in recommendation engines in doing predictive analytics on analyzing failures and saving saving failures on, you know, saving people's life. Those kind of use cases. Doctors are the ones that record a tighter integration between the application and the data and algorithms that come from the day I. And that's where we started to think about our platform. Way worked on a real time data, which is where you know, when you're going into more production environment of not fatal accident. Very good, very fast integration with data. And we have this sort of fast computation layer, which was a one micro services, and now everyone talks about micro services. We sort of started with this area, and that is allowing people to build those intelligent application that are integrated into the business applications. And the biggest challenges they see today for organizations is moving from this process of books on research, on data in a historical data and translating that into a visit supplication or into impact on business application. This is where people can spend the year. You know, I've seen the tweet saying with build a machine learning model in, like, a few weeks. And now we've waited eleven months for the product ization. So that artifact, >> Yes, that's what we're seeing it wicked bomb. Which is that A. I is the heart of modern applications in business and the new generation of application developers, in many ways, our data scientists, or have you know, lovers the skills and tools for data science. Now, looking at a glass zeros portfolio, you evolve so rapidly and to address a broader range of use cases I've seen. And you've explained it over the years that in position to go, as well as being a continuous data platform and intelligent edge platform, a surveillance platform. And now I see that you're a bit of a data science workbench or pipeline tooling. Clever. Could you connect these dots here on explain what is a guajillo fully >> role, Earl? Nice mark things for this in technology that we've built, OK, just over the years, you know, people, four years when we started, So we have to call it something else. Well, that I thought that analytic sort of the corporate state of science. And when we said continued analytics, we meant essentially feeding data and running, some of them speaking some results. This is the service opposed to the trend of truth which was dating the lady Throw data in and then you run the batch that analytic and they're like, Do you have some insight? So continue statistics was served a term that we've came up with a B, not the basket. You know, describe that you're essentially thinking, needing from different forces crunching it, Prue algorithms and generating triggers and actions are responsible user requests. Okay on that will serve a pretty unique and serve the fireman here in this industry even before they called it streaming or in a real time, data science or whatever. Now, if you look at our architecture are architecture, as I explained before, is comprised of three components. The first event is a real time, full time model database. You know, you know about it really exceptional in his performance and its other capabilities. The second thing is a pursue miss engine that allows us to essentially inject applications. Various guys, initially we started with application. I sense you do analytics, you know, grouping joining, you know, correlating. And then we start just adding more functions and other things like inference, saying humans recognitions and analysis. It's Arab is we have dysfunction engine. It allows us a lot of flexibility and find the really fast for the engine on a really fast data there endure it, remarkable results and then this return calling this turn this micro assume it's finger serve Ellis who certainly even where have the game of this or service gang. And the third element of our platform is a sense she having a fully manage, passed a platform where a ll those micro services our data and it threw a self service into face surfing over there is a mini cloud. You know, we've recently the last two years we've shifted to working with coronaries versus using our own A proprietary micro spurs does or frustration originally. So we went into all those three major technologies. Now, those pit into different application when they're interesting application. If you think about edge in the engine in serving many clouds, you need variety of data, sources and databases. With you, no problem arose streaming files. Terra. We'LL support all of them when our integrated the platform and then you need to go micro services that developed in the cloud and then just sort of shift into the enforcement point in the edge. And you need for an orchestration there because you want to do suffer upgrades, you need to protect security. So having all the integrated separated an opportunity for us to work with providers of agin, you may have noticed our joint announcement with Google around solution for hedge around retailers and an i O. T. We've made some announcement with Microsoft in the fast. We're going to do some very interesting announcement very soon. We've made some joint that nonsense with Samsung and in video, all around those errands, we continue. It's not that we're limited to EJ just what happens because we have extremely high density data platform, very power of fish and very well integrated. It has a great feat in the India, but it's also the same platform that we sell in. The cloud is a service or we sell two on from customers s so they can run. The same things is in the clouds, which happens to be the fastest, most real time platform on the Advantage service. An essential feature cannot just ignore. >> So you're wrong. Europe. Yeah, Iguazu is a complete cloud, native development and run time platform. Now serve earless in many ways. Seems to be the core of your capability in your platform. New Cleo, which is your technology you've open sourced. It's bill for Prem bays to private clouds. But also it has is extensible to be usable in broader hybrid cloud scenarios. Now, give us a sense for how nuclear and civilised functions become valuable or useful for data science off or for executing services or functions of data of the data science pipeline kick you connect the dots of nuclear and data science and a I from the development standpoint >> church. So So I think you know, the two pillars that we have technology that the most important ones are the data. You know, we have things like twelve batons on our data engine is very high performance and nuclear functions, and also they're very well integrated because usually services stateless. So you know, you you end up. If you want to practice that they have some challenges with service with No, no, you can't. You stay for use cases. You can mount files. You have real time connections to data, so that makes it a lot more interesting than just along the functions. The other thing, with no clothes that is extremely high performance has about two hundred times faster than land. So that means that you can actually go and build things like the stream processing and joins in real time all over practice, their base activities. You can just go and do collectors. We call them those like things. Go fetch information from whether services from routers for the X cybersecurity analysis for all sorts of sensors. So those functions are becoming like, you know, those nanobots technology of off the movies is that you just send them over to go and do things for you, whether it's the daily collection and crunching, whether it's the influencing engines, those things that, for example, get a picture of very put the model, decide what's in the picture, and that this is where we're really comes into play. They nothing important you see now an emergence off a service patterns in data science. So there are many companies that do like mother influencing as a service city what they do, they launch an end point of your eleven point and serve runs the model inside you send the Vector America values and get back in the Americans and their conversion. It's not really different and service it just wait more limited because I don't just want to send a vector off numbers because usually I understand really like a geo location of my cellphone, which are user I D. And I need dysfunction to cross correlated with other information about myself with the location. Then came commendation of which a product they need to buy. So and then those functions also have all sorts of dependency exam on different packages. Different software environment, horribles, build structures, all those. This is really where service technologies are much more suitable now. It's interesting that if you'LL go to Amazon, they have a product called Sage Maker. I'm sure yes, which is dinner, then a science block. Okay, now sage mint for although you would say that's a deal use case for after Onda functions actually don't use Amazon London functions in sage maker, and you ask yourself, Why aren't they using Lambda Stage Maker just telling you, you know you could use Lambda is a blue logic around sage maker. And that's because because London doesn't feed the use case. Okay, because lambda doesn't it is not capable of storing large content and she learning miles could be hundreds of megabytes or Landa is extremely slow. So you cannot do hi concurrency influencing with will land the function so essentially had to create another surveillance and college with a different name. Although if they just would have approved Landa, maybe it was one or a Swiss are So we're looking, We've took it, were taken the other approach We don't have the resources that I have so we created a monster virus Engine one servant attention does batch Frost is saying scream processing, consort, lots of data, even rocketeer services to all the different computation pattern with a single engine. And that's when you started taking all this trend because that's about yeah, we need two version our code. We need to, you know, record all our back into dependencies. And although yes, service doesn't so if we just had to go and tied more into the existing frameworks and you've looked at our frantically product called Tokyo Jupiter, which is essentially a scientist, right, some code in his data's passport book and then in clicks. One command called nuclear Deploy, it automatically compiles, is their science artifact in notebooks, that server and converted into a real hand function that can listen in on your next city. People can listen on streams and keep the scheduled on various timing. It could do magic. So many other things. So, and the interesting point is that if you think about their scientists there, not the farmers, because they should be a scientist on this's means that they actually have a bigger barrier to write in code. So if you serve in this framework that also automates the law daughter scaling the security provisioning of data, the versions of everything in fact fantasies, they just need to focus on writing other them's. It's actually a bigger back for the book. Now, if you just take service into them, Epstein's and they will tell you, Yeah, you know, we know how to explain, Doctor. We know all those things, so they're very their eyes is smaller than the value in the eyes of their scientists. So that's why we're actually seeing this appeal that those those people that essentially focus in life trying math and algorithms and all sorts of those sophisticated things they don't want to deal with. Coding and maintenance are refreshed. And by also doing so by oppression analyzing their cool for service, you can come back to market. You can address calle ability to avoid rewriting of code. All those big challenges the organizations are facing. >> You're gonna have to ask you, that's great. You have the tools to build, uh, help customers build serve Ellis functions for and so forth inside of Jupiter notebooks. And you mentioned Sage Maker, which is in a WS solution, which is up in coming in terms of supporting a full data science tool chain for pipeline development. You know, among teams you have a high profile partnerships with Microsoft and Google and Silver. Do you incorporate or integrator support either of these cloud providers own data science workbench offerings or third party offerings from? There's dozens of others in this space. What are you doing in terms of partnerships in that area? >> Yeah, obviously we don't want to lock us out from any of those, and, you know, if someone already has his work bench that I don't know my customers say they were locking me into your world back in our work when things are really cool because like our Jupiter is connected for real time connections to the database. And yes, serve other cool features that sentir getting like a huge speed boost we have. But that's on A with an within vigna of round Heads and Integration, which reviews are creating a pool of abuse from each of one of the data scientist running on African essentially launch clubs on this full of civilians whose off owning the abuse, which are extremely expensive, is you? No. But what we've done is because of her. The technology beside the actual debate engine is open source. We can accept it essentially just going any sold packages. And we demonstrate that to Google in danger. The others we can essentially got just go and load a bunch of packages into their work match and make it very proposed to what we provide in our manage platform. You know, not with the same performance levels. Well, functionality wise, the same function. >> So how can you name some reference customers that air using a guajillo inside a high performance data science work flows is ah, are you Are there you just testing the waters in that market for your technology? Your technology's already fairly mature. >> That says, I told you before, although you know, sort of changed messaging along the lines. We always did the same thing. So when we were continuous analytics and we've spoken like a year or two ago both some news cases that we Iran like, you know, tell cooperators and running really time, you know, health, a predictive health, monitoring their networks and or killing birds and those kind of things they all use algorithms. You control those those positions. We worked with Brian nailing customers so we can feed a lot of there there in real time maps and do from detection. And another applications are on all those things that we've noticed that all of the use cases that we're working with involved in a science in some cases, by the way, because of sort of politics that with once we've said, we have analytics for continuous analytics, we were serving send into sent into the analytic schools with the organization, which more focused on survey data warehouse because I know the case is still serve. They were saying, and I do. And after the people that build up can serve those data science applications and serve real time. Aye, aye. OK, Ianto. Business applications or more, the development and business people. This is also why we sort of change are our name, because we wanted to make it very clear that we're aren't the carnage is about building a new applications. It's not about the warehousing or faster queries. On a day of Eros is about generating value to the business, if you ask it a specific amplification. And we just announced two weeks in the investment off Samsung in Iguazu, former that essentially has two pillars beyond getting a few million dollars, It says. One thing is that they're adopted. No cure. Is there a service for the internal clouds on the second one is, we're working with them on a bunch of us, Della sighs. Well, use case is one of them was even quoted in enough would make would be There are no I can not say, but says she knows our real business application is really a history of those that involves, you know, in in intercepting data from your sister's customers, doing real time on analytics and responding really quickly. One thing that we've announced it because of youse off nuclear sub picture. We're done with inferior we actually what were pulled their performance. >> You're onto you see if you see a fair number of customers embedding machine learning inside of Realtor time Streaming stream computing back ones. This is the week of Flink forward here in San San Francisco. I I was at the event earlier this week and I I saw the least. They're presenting a fair amount of uptake of ml in sight of stream computing. Do you see that as being a coming meet Mainstream best practice. >> Streaming is still the analytics bucket. OK, because what we're looking for is a weakness which are more interactive, you know, think about like, uh, like a chatterbox or like doing a predictive analytic. It's all about streaming. Streaming is still, you know, it's faster flow data, but it's still, sir has delay the social. It's not responses, you know. It's not the aspect of legacy. Is that pickle in streaming? Okay, the aspect of throughput is is higher on streaming, but not necessarily the response that I think about sparks streaming. You know, it's good at crossing a lot of data. It's definitely not good at three to one on would put spark as a way to respond to user request on the Internet S O. We're doing screaming, and we see that growth. But think where we see the real growth is panic to reel of inches. The ones with the customer logs in and sends a request or working with telcos on scenarios where conditions of LA car, if the on the tracks and they settled all sorts of information are a real time invent train. Then the customer closer says, I need a second box and they could say No, this guy needs to go away to that customer because how many times you've gotten technician coming to your house and said I don't have that more exactly. You know, they have to send a different guy. So they were. How do you impact the business on three pillars of business? Okay, the three pillars are one is essentially improving your china Reducing the risk is essentially reducing your calls. Ask him. The other one is essentially audio, rap or customer from a more successful. So this is around front and application and whether it's box or are doing, you know our thing or those kind of us kisses. And also under you grow your market, which is a together on a recommendation in at this time. So all those fit you if you want, have hey, I incorporated in your business applications. In few years you're probably gonna be dead. I don't see any bits of sustained competition without incorporating so ability to integrate really real data with some customer data and essentially go and react >> changes. Something slightly you mentioned in video as a partner recently, Of course, he announced that few weeks ago. At their event on, they have recently acquired Melon ox, and I believe you used to be with Melon Axe, so I'd like to get your commentary on that acquisition or merger. >> Right? Yes, yes, I was VP Data Center man Ox. Like my last job, I feel good friends off off the Guider, including the CEO and the rest of the team with medicines. And last week I was in Israel's with talk to the media. Kansas. Well, I think it's a great merger if you think about men in Ox Head as sort of the best that breaking and storage technology answer Silicon Side and the video has the best view technologies, man. It's also acquired some compute cheap technologies, and they also very, very nice. Photonics technologies and men are today's being by all the club providers. Remiss Troll was essentially only those technical engagement would like the seizures and you know the rest of the gas. So now VP running with the computation engine in and minerals coming, we serve the rest of the pieces were our storage and make them a very strong player. And I think it's our threatens intel because think about it until they haven't really managed to high speed networking recently. They haven't really managed to come with Jiffy use at your combat and big technology, and so I think that makes a video, sort of Ah, pretty. You know, vendor and suspect. >> And another question is not related to that. But you're in Tel Aviv, Israel. And of course, Israel is famous for the start ups in the areas of machine learning. And so, especially with a focus on cyber security of the Israel, is like near the top of the world in terms of just the amount of brainpower focused on cyber security there. What are the hot ML machine? Learning related developments or innovations you see, coming out of Israel recently related to cybersecurity and distributed cloud environments, anything in terms of just basic are indeed technology that we should all be aware of that will be finding its way into mainstream Cloud and Cooper Netease and civilised environments. Going forward, your thoughts. >> Yes, I think there are different areas, you know, The guys in Israel also look at what happens in sort of the U. S. And their place in all the different things. I think with what's unique about us is a small country is always trying to think outside of the box because we know we cannot compete in a very large market. It would not have innovation. So that's what triggers this ten of innovation part because of all this tippy expects in the country. And also there's a lot of cyber, you know, it's time. I think I've seen one cool startup. There's also backed by our VC selling. Serve, uh, think about like face un recognition, critical technology off sent you a picture and make it such that you machine learning will not be able to recognize Recognize that, you know, sort of out of the cyber attack for image recognition. So that's something pretty unique that I've heard. But there are other starts working on all the aspects on their ops and information in our animal and also cyber automated cyber security and hope. Curious aspect. >> Right, Right. Thank you very much. Your own. This has been an excellent conversation, and we've really enjoyed hearing your comments. And Iguazu. It was a great company. Quite quite an innovator is always a pleasure to have you on the Cube. With that, I'm going to sign off. This is James Kabila's with wicked bond with your own haviv on dh er we bid You all have a good day. >> Thank you.

>> Narrator: Live from Nassau in the Bahamas. It's The Cube. Covering Polycon 18, brought to you by Polymath. >> Welcome back, everyone, this is The Cube exclusive coverage at Polycon 18, put on by Grid Capital and Polymath, of course The Cube is independent publishing, digital TV and research. Of course we're covering all the action this year in the crypto-space blockchain, crypto-currency token economics. Big news here with Polymath is announcing a partnership. We've got Trevor Koverko is the CEO of Polymath Amir Kaltak, CEO at Lexit. You guys just came off stage and announced a partnership. Your ecosystem is growing, you guys are enabling platform. Talk about the relationship. >> You want to start? >> Please, I insist. >> Alright, fine. So awhile ago we just realized that what Trevor and his team are doing is just fitting right in what we do. We, the marketplace for M&A, helping to liquidize assets, and a security token, in its purest form, is an asset. So we want to help, we want to work together and create an ecosystem in the future between Polymath and Lexit, that is basically the thing that we want to figure out and how to do it. >> Yeah, no, we're big fans of the project, and more importantly the team behind the project. That's always what we look for when it comes to any investment, or purchase, or partnership. We're really excited. >> This is really a great sign for you guys. Congratulations, Polymath and Lexit, you guys are growing companies. This is the magic of platforms, right? You guys have collaboration, ecosystem partners really become instrumental for you guys, so it's a good sign. You get the leverage, the platform, you get some time to market faster, time to value, this is what it's all about, right? >> I believe that security tokens are going to be a big part of our future revenue on Lexit itself, and I can't miss out on that one, and I'm happy that we meet at that early stage, so to say, where everything happens. Where we set the path into the future. So let's see what happens. >> Amir I want to ask you, as someone who's partnered (inaudible), why Polymath? What was compelling for you? What was the reason? Obviously they have a secure token, so it's a platform, and it's a trend that's your friend right now. So why Polymath? >> There are multiple ones. Trend isn't that right, but the thing is, I'm old school, right? If somebody I know and trust tells me this is a great person I need to talk to, this is a great project, then I do it. So, Tim Frost of Taurus Solutions was the guy who connected us in New York on a brief meeting, and now more and more, and so this is how we started. And I go with my gut feeling. If I see a sincere man, I see a sincere man, and I would like to work with him. >> Great. Platform-wise, API's, how's it going to work? You guys, can you share any details? I missed the announcement because we were doing Cube interviews. What was announced on stage? >> For me, this is kind of what I've been echoing all week. It's all about building the components of this ecosystem. We're trying to, literally, re-imagine Wall Street, and to do that it requires new forms of structure formation of capital. So we have private equity, we have mergers and acquisitions, we have venture capital, and with Polymath we're just trying to be the base layer that other exciting projects like Lexit can build on top of. >> What are some of the most important things in the platform, Amir, that you like? Just get under the hood a little bit. What's, what about Polymath is going to be a good deal for you guys? What's the key? Is it saving time, is it the certain things on the platform? What specifically about these guys- >> Free t-shirts? >> Free t-shirts definitely. And after that, the free t-shirt contest. What contest? I'm kidding. No, to me it's like, look, you want to have a security token, right? And then there are multiple jurisdictions, and there's a lot of legal compliance. It's a mountain of work in front of you. Those guys figured out how to do this simple and reliable for all of us. >> Kind of like what you're doing on the M&A side, except they do it for the security token. >> Sort of. >> Always breaking down barriers, that's the name of the game. That's the definition of an entrepreneur. >> Removing the blockers in front of you is the key, and not to waste time on management cycles, on things that someone else's doing. That, to me, it good partnership. Sounds like that's what you guys are offering, right? >> Absolutely. >> Absolutely. >> Alright, guys, well thank you for sharing the news. A final word, >> John, thank you. >> What do you see as the outlook, partnerships, you guys going to make some money together, you've got to build the product out first? How's the sequence, the order of operations of the partnership? Share the quick overview, then we'll end the segment. >> So we have a lot of work ahead of us. And right now it's about getting Polymath, the demo is out, the alpha is out, it's live, you can use it. And my biggest party right now is getting the application layer to the market, and that simply means a user interface, drag and drop, point and click, and that is my life right now. So once we get that out the door, these guys are ready. >> The thing is we are launching globally, full developed, since two years away in develop, in June. And soon after that, we will hopefully be ready for their platform. But, speaking of that, it's public now, but we will work closely right away to figure out how to optimize everything in between our systems. So it's going to be an ongoing process where we to be careful with resources, of course, but it's going to happen during this year, I hope. >> Amen. >> Well congratulations on the building blocks of success, you've got to start with the core. This is The Cube bringing a live coverage from the Bahamas. Big news here on the partnerships of the two companies, Polymath and Lexit. Look for more coverage. We'll be right back with more coverage after this short break. (techno music)