>>from our studios in the heart of Silicon Valley, Palo Alto, California It is a cute conversation. >>Hey, welcome back already, Jeffrey. Here with the cue, we're in our pal Amato Studios for acute conversation or excited, have ah, many Time Cube alone. I has been at all types of companies. He's moving around. We like to keep him close because he's got a great feel for what's going on. And now he's starting a new adventure. Eso really happy to welcome Alan Cohen back to the studio. Only great to see you. >>Hey, Draft, how are you >>in your new adventure? Let's get it right. It's the D C v c your partner. So this is ah, on the venture side. I'm gonna dark. You've gone to the dark side of the money side That is not a new firm, dark side. You know what's special about this town of money adventure right now, but you guys kind of have a special thesis. So tell us about yeah, and I think you've spoken >>to Matt and Zack. You know my partners in the past, So D. C. V. C is been in the venture business for about a decade and, um, you know, the 1st 5 years, the fund was very much focused on building, ah, lot of the infrastructure that we kind of take for granted. No things have gone into V m wear and into Citrix, and it's AWS, and hence the data collect of the D. C out of D. C. V. C. Really, the focus of the firm in the last five years and going forward is an area we call deep tech, which think about more about the intersection of science and engineering so less about. How do you improve the IittIe infrastructure? But how do you take all this computational power and put it to work in in specific industries, whether it's addressing supply chains, new forms of manufacturing, new forms of agriculture. So we're starting to see all that all the stuff that we've built our last 20 years and really apply it against kind of industrial transformation. So and we're excited. We just raise the $725 million fund. So we I got a little bit of ammunition to work with, >>Congratulate says, It's fun. Five. That's your eighth fund. Yeah, and really, it's consistent with where we're seeing all the time about applied a I and applied machine. Exactly. Right in New York, a company that's gonna build a I itt s'more the where you applying a i within an application, Where you applying machine, learning within what you do. And then you can just see the applications grow exactly right. Or are you targeting specific companies that are attacking a particular industrial focus and just using a eyes, their secret sauce or using deep taxes or secret uh, all of the above? Right. So, like I >>did when I think about D c v c like it's like so don't think about, um, I ops or throughput Orban with think about, um uh, rockets, robots, microbes, building blocks of effectively of human life and and of materials and then playing computational power and a I against those areas. So a little bit, you know, different focus. So, you know, it's the intersection of compute really smart computer science, but I'll give you a great example of something. It would be a little bit different. So we are investors and very active in a company called Pivot Bio, which is not exactly a household name. Pivot bio is a company that is replacing chemical fertilizer with microbes. And what I mean by that is they create microbes they used. So they've used all this big data and a I and computational power to construct microbes that when you plant corn, you insert the microbe into the planting cycle and it continuously produces nitrogen, which means you don't have to apply fertilizer. Right? Which fertilizer? Today in the U. S. A. $212 billion industry and two things happen. One you don't have. All of the runoff doesn't leech into the ground. The nitrous does. Nitrogen doesn't go into the air, and the crop yield has been a being been between about 12 and 15% higher. Right? >>Is it getting put? You know, the food industry is such a great place, and there's so many opportunities, both in food production. This is like beyond a chemical fertilizer instead of me. But it's great, but it's funny because you think of GMO, right? So all food is genetically modified. It's just It took a long time in the past because you had to get trees together, and yet you replant the pretty apples and throw the old apple trees away. Because if you look at an apple today versus an apple 50 years, 100 years, right, very, very different. And yet when we apply a man made kind of acceleration of that process than people, you know, kind of pushed back Well, this is this is not this is not nature, So I'm just curious in, in, in in, Well, this is like a microbe, you know? You know, they actually it is nature, right? So nature. But there'll be some crazy persons that wait, This is not, you know, you're introducing some foreign element into Well, you could take >>potash and pour it on corn. Or you could create a use, a microbe that creates nitrogen. So which one is the chemical on which one is nature, >>right, That that's why they get out. It's a funny part of that conversation, but but it's a different area. So >>you guys look, you guys spent a lot of time on the road. You talked a lot of startups. You talked a lot of companies. You actually talked to venture capitalists and most of the time where you know, we're working on the $4 trillion I t sector, not an insignificant sector, right? So that's globally. It's that's about the size of the economy. You know, manufacturing, agriculture and health care is more like 20 to $40 billion of the economy. So what we've also done is open the aperture to areas that have not gone through the technical disruption that we've seen an I t. Right now in these industries. And that's what's that mean? That's why I joined the firm. That's why I'm really excited, because on one hand you're right. There is a lot of cab you mentioned we were talking before. There is a lot of capital in venture, but there's not a CZ much targeted at the's area. So you have a larger part of global economy and then a much more of specific focus on it. >>Yeah, I think it's It's such a you know, it's kind of the future's here kind of the concept because no one knows, you know, the rate of which tech is advancing across all industries currently. And so that's where you wake up one day and you're like, Oh, my goodness, you know, look at the impacts on transportation. Look at the impacts on construction of the impacts on health care. Look at the impacts on on agriculture. So the opportunity is fantastic and still following the basic ideas of democratizing data. Not using a sample of old data but using, you know, real time analytics on hold data sets. You know, all these kind of concepts that come over really, really well to a more commercial application in a nightie application. Yeah. So, Jeff, I'm kind of like >>looking over your shoulder. And I'm looking at Tom Friedman's book The world is flat. And you know, if we think about all of us have been kind of working on the Internet for the last 20 years, we've done some amazing things like we've democratized information, right? Google's fairly powerful part of our lives. We've been able to allow people to buy things from all over the world and ship it. So we've done a lot of amazing things in the economy, but it hasn't been free. So if I need a 2032 c r. 20 to 32 battery for my key fob for my phone, and I buy it from Amazon and it comes in a big box. Well, there's a little bit of a carbon footprint issue that goes with that. So one of our key focus is in D. C V. C, which I think is very unique, is we think two things can happen is that weaken deal with some of the excess is over the economy that we built and as well as you know, unlock really large profit pulls. At the end of the day, you know, it has the word Venture Patrol says the word capital, right? And so we have limited partners. They expect returns. We're doing this obviously, to build large franchises. So this is not like this kind of political social thing is that we have large parts of the economy. They were not sustainable. And I'll give you some examples. Actually, you know, Jeff Bezos put out a pledge last week to try to figure out how to turn Amazon carbon neutral. >>Pretty amazing thing >>right with you from the was the richest person Now that half this richest person in the world, right? But somebody who has completely transformed the consumer economy as well as computing a comedy >>and soon transportation, right? So people like us are saying, Hey, >>how can we help Jeff meet his pledge? Right? And like, you know, there are things that we work on, like, you know, next generation of nuclear plants. Like, you know, we need renewables. We need solar, but there's no way to replace electricity. The men electricity, we're gonna need to run our economy and move off of coal and natural gas, Right? So, you know, being able to deal with the climate impacts, the social impacts are going to be actually some of the largest economic opportunities. But you can look at it and say, Hey, this is a terrible problem. It's ripping people across. I got caught in a traffic jam in San Francisco yesterday upon the top of the hill because there was climate protest, right? And you know, so I'm not kind of judging the politics of that. We could have a long conversation about that. The question is, how do you deal with these real issues, right and obviously and heady deal with them profitably and ethically, and I think that something is very unique about you know, D. C. V. C's focus and the ability to raise probably the largest deep tech fund ever to go after. It means that you know, a lot of people who back us also see the economic opportunity. And at the end of day there, you know, a lot of our our limited partners, our pension funds, you know, in universities, like, you know, there was a professor who has a pension fund who's gotta retire, right? So a little bit of that money goes into D C V C. So we have a responsibility to provide a return to them as well as go after these very interesting opportunities. >>So is there any very specific kind of investment thesis or industry focus Or, you know, kind of a subset within, you know, heavy lifting technology and science and math. That's a real loaded question in front of that little. So we like problems >>that can be solved through massive computational capability. And so and that reflects our heritage and where we all came from, right, you and I, and folks in the industry. So, you know, we're not working at the intersection of lab science at at a university, but we would take something like that and invest in it. So we like you know we have a lot of lessons in agriculture and health care were, surprisingly, one of the largest investors in space. We have investments and rocket labs, which is the preferred launch vehicle for any small satellite under two and 1/2 kilograms. We are large investors and planet labs, which is a constellation of 200 small satellites over investors and compel a space. So, uh, well, you know, we like space, and, you know, it's not space for the sake of space. It's like it's about geospatial intelligence, right? So Planet Labs is effectively the search engine for the planet Earth, right? They've been effectively Google for the planet, right? Right. And all that information could be fed to deal with housing with transportation with climate change. Um, it could be used with economic activity with shipping. So, you know, we like those kinds of areas where that technology can really impact and in the street so and so we're not limited. But, you know, we also have a bio fund, so we have, you know, we're like, you know, we like agriculture and said It's a synthetic biology types of investments and, you know, we've still invest in things like cyber we invest in physical security were investors and evolve, which is the lead system for dealing with active shooters and venues. Israel's Fordham, which is a drone security company. So, um, but they're all built on a Iot and massive >>mess. Educational power. I'm just curious. Have you private investment it if I'm tree of a point of view because you got a point of view. Most everything on the way. Just hear all this little buzz about Quantum. Um, you know, a censure opened up their new innovation hub in the Salesforce tower of San Francisco, and they've got this little dedicated kind of quantum computer quanta computer space. And regardless of how close it is, you know there's some really interesting computational opportunities last challenges that we think will come with some period of time so we don't want them in encryption and leather. We have lost their quantum >>investments were in literally investors and Righetti computing. Okay, on control, cue down in Australia, so no, we like quantum. Now, Quantum is a emerging area like it's we're not quite at the X 86 level of quantum. We have a little bit of work to get there, but it offers some amazing, you know, capabilities. >>One thing >>that also I think differentiates us. And I was listening to What you're saying is we're not afraid. The gold long, I mean a lot of our investments. They're gonna be between seven and 15 years, and I think that's also it's very different if you follow the basic economics adventure. Most funds are expected to be about 10 years old, right? And in the 1st 3 or four years, you do the bulk of the preliminary investing, and then you have reserves traditional, you know, you know, the big winners emerged that you can continue to support the companies, some of ours, they're going to go longer because of what we do. And I think that's something very special. I'm not. Look, we'd like to return in life of the fun. Of course, I mean, that's our do share a responsibility. But I think things like Quantum some of these things in the environment. They're going to take a while, and our limited partners want to be in that long ride. Now we have a thesis that they will actually be bigger economic opportunities. They'll take longer. So by having a dedicated team dedicated focus in those areas, um, that gives us, I think, a unique advantage, one of one of things when we were launching the fund that we realized is way have more people that have published scientific papers and started companies than NBA's, um, in the firm. So we are a little bit, you know, we're a little G here. That >>that's good. I said a party one time when I was talking to this guy. You were not the best people at parties we don't, but it is funny. The guy was He was a VC in medical medical tech, and I didn't ask him like So. Are you like a doctor? Did you work in a hospital where you worked at A at a university that doesn't even know I was investment banker on Wall Street and Michael, that's that's how to make money move. But do you have? Do you have the real world experience of being in the trenches? Were Some of these applications are being used, but I'm also curious. Where do you guys like to come in? ABC? What's your well, sweets? Traditionally >>we are have been a seed in Siri's. A investor would like to be early. >>Okay, Leader, follow on. Uh, everybody likes the lead, right? Right, right, right. You know what? Your term feet, you >>know? Yeah, right. And you have to learn howto something lead. Sometimes you follow. So we you know, we do both. Okay, Uh, there are increasing as because of the size of the fund. We will have the opportunity to be a little bit more multi stage than we traditionally are known for doings. Like, for example, we were seed investors in little companies, like conflict an elastic that worked out. Okay, But we were not. Later stage right. Investors and company likes companies like that with the new fund will more likely to also be in the later stages as well for some of the big banks. But we love seed we love. Precede. We'd like three guys in in a dog, right? If they have a brilliant >>tough the 7 50 to work when you're investing in the three guys in a dog and listen well and that runs and runs and you know you >>we do things we call experiments. Just you know, uh, we >>also have >>a very unique asset. We don't talk about publicly. We have a lot of really brilliant people around the firm that we call equity partners. So there's about 60 leaning scientists and executives around the world who were also attached to the firm. They actually are, have a financial stake in the firm who work with us. That gives us the ability to be early Now. Clearly, if you put in a $250,000 seed investment you don't put is the same amount of time necessarily as if you just wrote a $12 million check. What? That's the traditional wisdom I found. We actually work. Address this hard on. >>Do you have any? Do you have any formal relationships within the academic institutions? How's that >>work? Well, well, I mean, we work like everybody else with Stanford in M I t. I mean, we have many universities who are limited partners in the fund. You know, I'll give you an example of So we helped put together a company in Canada called Element A I, which actually just raised $150 million they, the founder of that company is Ah, cofounder is a fellow named Joshua Benji. Oh, he was Jeff Hinton's phD student. Him in the Vatican. These guys invented neural networks ing an a I and this company was built at a Yasha his position at the University of Montreal. There, 125 PhDs and a I that work at this firm. And so we're obviously deeply involved. Now, the Montreal A icing, my child is one of the best day I scenes in the world and cool food didn't and oh, yeah, And well, because of you, Joshua, because everybody came out of his leg, right? So I think, Yes, I think so. You know, we've worked with Carnegie Mellon, so we do work with a lot of universities. I would, I would say his university's worked with multiple venture firm Ah, >>such an important pipeline for really smart, heavy duty, totally math and tech tech guys. All right, May, that's for sure. Yeah, you always one that you never want to be the smartest guy in the room, right, or you're in the wrong room is what they say you said is probably >>an equivalent adventure. They always say you should buy the smallest house in the best neighborhood. Exactly. I was able to squeeze its PCB sees. I'm like, the least smart technical guy in the smartest technical. There >>you go. That's the way to go. All right, Alan. Well, thanks for stopping by and we look forward. Thio, you bring in some of these exciting new investment companies inside the key, right? Thanks for the time. Alright. He's Alan. I'm Jeff. You're watching the Cube. We're Interpol about the studios. Thanks for watching. We'll see you next time.

(dramatic music) >> Welcome back, everyone to theCUBE's headquarters in Palo Alto, I'm John Furrier, the host of theCUBE, founder of SiliconANGLE Media, we're here for Cube Conversation with Ryan Welsh, who's the founder of CEO of Kyndi. It's a hot startup, it's a growing startup, doing really well in a hot area, it's in AI, it's where cloud computing, AI, data, all intersect around IoT, RPA's been a hot trend everyone's on, they're in that as well, but really an interesting startup we want to profile here, Ryan, thanks for spending the time to come in and talk about the startup. >> Yeah, thanks for having me. >> So I love getting the startups in, because we get the real scoop, you know, what's real, what's not real, and also, practitioners also tell us the truth too, so we love to have especially founders in. So first, before we get started, tell 'em about the company, how old is your company, what's the core value proposition, what do you guys do? >> Yeah, we're four years old, we were founded in June 2014. The first two, three years were really fundamental research and developing some new AI algorithms. What we focus on is, we focused on building explainable AI products for government customers, pharmaceutical customers and financial services customers. So our-- >> Let's explain the AI, what does that mean, like how do you explain AI? AI works, especially machine learning, well AI doesn't really exist, 'cause it's really machine learning, and what is AI? So what is explainable AI? >> Yeah, for us, it's the ability of a machine to communicate with the user in natural language. So there's kind of two aspects to explainability. Some of the deep learning folks are grabbing onto it, and really what they're talking about with explainability is algorithmic transparency, but where they tell you how the algorithm works, they tell you the parameters that are being used. So I explain to you the algorithm, you can actually interrogate the system. For us, if our system's going to make a recommendation to you, you would want to know why it's making the recommendation, right? So for us, we're able to communicate with users in natural language, like it's another person, of why we make a recommendation, why we bring back a search result, why we do whatever it is as part of the business process. >> And you mentioned deep learning AI is obviously the buzzword everybody's talking about, I mean I'm a big fan of AI in the sense that hyping it up means my kids know what it is, and everybody say, hey Dad, love machine learning. They love AI 'cause it's got a futuristic sound to it, but deep learning is real, deep learning is about learning systems that learn, which means they need to know what's going on, right? So this learning loop, how does that work? Is that kind of where explainable AI needs to go? Is that where it's going, where if you can explain it and it's explainable, you can interrogate it, does it have a learning mechanism to it? >> I think there's two major aspects of intelligence. There's the learning aspect, then there's the reasoning aspect. So if you look back through the history of AI, current machine learning is phenomenal at learning from data, like you're saying, learning the patterns in the data, but its reasoning is actually pretty weak. It can do statistical inferencing, but in the field of symbolic AI, where there's inductive, deductive, abductive, analogical reasoning, kind of advanced reasoning, it's terrible at reasoning. Whereas the symbolic approaches are phenomenal at reasoning but can't learn from data. So what is AI? A sub-group of that is machine learning that can learn from data. Another sub-group of that, it's knowledge-based approaches, which can't learn from data, they are phenomenal at reasoning, and really the trend that we're seeing at the edge in AI, or kind of the cutting edge, is actually fusing those two paradigms together, which is effectively what we've done. You've seen DeepMind and Google Brain publish a paper on that earlier this year, you've seen Gary Marcus start to talk about that, so for us, explainability is kind of bringing together these two paradigms of AI, that can both learn from data, reason about data, and answer questions like, why are you giving me this recommendation. >> Great explanation. And I want to just ask you, what' the impact of that, because we've always talked in the old search world, meta-reasoning, you type in a misspelling on Google, and it says, there's the misspelling, okay, I get that, but what if is misspell the word all the time, can't Google figure out that I really want that word? So reasoning has been a hard nut to crack, big time. >> Well you have to acquire the knowledge first to combine bits of knowledge to then reason, right? But the challenge is acquiring the knowledge. So you have all these systems or knowledge-based approaches, and you have human beings on-site, professional services, building and managing your knowledge base. So that's been one of the hurdles for knowledge-based approaches. Now you have machine learning that can learn from data, one of the problems with that is, that you need a bunch of labeled data. So you're kind of trading off between handcrafted knowledge systems, handcrafted labeled systems which you can then learn from data. So the benefits of fusing the two together is you can use machine learning approaches to acquire the knowledge, as opposed to hand engineering it, and then you can put that in a form or a data model that you can then reason about. So the benefit is really it all comes down to customer. >> Awesome, great area, great concepts, we can go for an hour on this, I love this topic, I think it's super relevant, especially as cloud and automation become the key accelerant to a lot of new value. But let's get back to the company. So four years old, you've done some R and D, give me the stats, where are you guys in the product side, product shipping, what's the value proposition, how do people engage with you, just go down looking on the list. >> Yeah, yeah, shipping product to customers in pharmaceutical, and government use cases. How people engage with us-- >> It's a software product? >> It's a software product. Yeah, yeah. So we can deliver it, surprisingly a lot of customers still want it on-prem. (both laugh) But we can deploy in the cloud as well. Typically, how we work with customers is we'll have close engagements for specific use cases within pharma or government or financial services, because it's a very broad platform an can be applied to any text-based use case. So we work with them closely, develop a use case, they're able to sell that internally to champions >> And what problems are they solving, what specifically is the answer? >> So for pharmaceutical companies, a lot of their internal, historical clinical trial data, they'll develop memos, emails, notes as they bring a drug to market. How do you leverage that data now? Instead of just storing it, how do I find new and innovative ways to use existing drugs that someone in another part of the organization could have developed? How do I manage the risks within that historical clinical trial data? Are there people that are doing research incorrectly? Are they reporting things incorrectly? You know, this entire process of both getting drugs through the pipeline and managing drugs as they move through the pipeline, is a very manual process that revolves around text-based data sources. So how do you develop systems that amplify the productivity of the people that are developing the drugs, then also the people that are managing the process. >> And so what are you guys actually delivering as value? What's the value proposition for them? >> Yeah, so >> Is it time? >> It's saving time, but ultimately increasing their productivity of getting that work done. It's not replacing individuals, because there's so much work to do. >> So all the... The loose stuff like the paper, they can discover it faster, so they have more access to the data. >> That's right. >> Using your tools >> That's right >> and your software. >> You can classify things in certain ways, saying there's data integrity issues, you need to look at this closer, but ultimately managing that data. >> And that's where machine learning and some of these AI techniques matter, because you want to essentially throw software at that problem, accelerate that process of getting the data, bringing it in, assessing it. >> Yeah, I mean we spend most of our time looking for the information to then analyze. I mean we spend 80% of our time doing it, right? Where it's like are there ways to automate that process, so we can spend 80% of our time actually doing our job? >> So Ryan, who's the customer out there? So is it someone, someone's watching this video, and what's their pain point, when do they call you, why do they call you? What's some of the signals that might tell someone, hey I want to give these guys a call, I need this solution? >> Yeah, a lot of it comes down to the amount of manual labor that you're doing. So we see a lot of big expenses around people, because you haven't traditionally been able to automate that process, or to use software in that process. So if you actually look at your income statement and you say where am I spending my most money, on tons of people, and I'm just throwing people at the problem, that's typically where people engage with us and say, how do I amplify the productivity of these people so I can get more out of them, hopefully make them more efficient? >> And it's not just so much to reduce the head count issue, it's more of increasing the automation for saying value in top-line revenue, because if you have to reproduce people all the time, why not replicate that in software? So I think what I'm seeing is, get that right? >> That's exactly right. And the job consistently changes too, so it's not like this robotic process that you can just automate away. They're looking for certain things one day, then they're looking for certain things the next day, but you need a capability that kind of matches their expertise. >> You know, I was talking to a CIO the other day and we were talking about some of the things around reproducing things, replicating, and the notion of how things get scaled or moved along, growth, is, and the expression was "Throw a body at that". That's been IT. Outsource it. So throwing a body, or throw bodies at it, you know, throw that problem at me, that doesn't really end well. With software automation you can say, you don't just throw a body at it, you can say, if it can be automated, automate it. >> Yeah, here's what I think most people miss, is that we are the bottleneck in the modern production process because we can't read and understand information any faster than our parents or grandparents. And there's not enough people on the planet to increase our capacity, to push things through. So if we were to compare the modern knowledge economy, it's interesting, to the manufacturing process, you have raw materials, manufacture it, and end product. All these technologies that we have effectively stack information and raw materials at the front of it. We haven't actually automated that process. >> You nailed it, and in fact one of the things I would say that would support that is, in interviewed Dave Redskin, who's a site reliable engineer at Google, and we were talking about the history of how Google scaled, and they have this whole new program around how to operate large data centers. He said years and years ago at Google, they looked up the growth and said, we're going to need a thousand people per data center, at least, if not, per data center, so that means we need 15,000 people just to manage the servers. 'Cause what they did was they just did the operating cycle on provisioning servers, and essentially, they automated it all away, and they created a lot of the tools that became now Google Cloud. His point was, is that, they now have one person, site reliability engineer, who overlooks the entire automation piece. This is where the action is. That concept of not, to scale down the people focus, scale up the machine base model. Is that kind of the trend that you guys are riding? >> Absolutely. And I think that's why AI is hot right now. I mean, AI's been around since the late 40s, early 50s, but why this time I think it's different is, one, that it's starting to work, given the computational resources and the data that we have, but then also the economic need for it. Businesses are looking, and saying, how I historically address these problems, I can no longer address them that way, I can't hire 15,000 people to run my data center. I need to now automate-- >> You got to get out front on it. >> Yeah, I got to augment those people with better technologies to make them do the work better. >> All right, so how much does the product cost, how do people engage with you guys, what's the engagement cost, is it consulting you come in, POC you ship 'em software, to appliances in the cloud, you mention on-premise. >> Yeah, yeah. >> So what's, how's the product look, how much does it cost? >> Yeah, it costs a good chunk for folks, so typically north of 500K. We do provide a lot of ROI around that, hence the ability to charge such a high price. Typically how we push people through the cycle and how we actually engage with folks is, we do what we demonstration of value. So there's a lot of different, or typically there's about 15 use cases that any given Fortune 500 customer wants to address. We find the ones with the highest ROI, the ones with accessible data >> And they point at it, >> The ones with budget >> They think, that's my problem, they point to it, right? >> Yeah. >> It's not hard to find. >> We have to walk 'em through it a little bit. Hopefully they've engaged with other vendors in the market that have been pushing AI solutions for the last few years, and have had some problems. So they're coached up on that, but we engage with demonstration of value, we typically demonstrate that ROI, and then we transition that into a full operational deployment for them. If they have a private cloud, we can deploy on a private cloud. Typically we provide an appliance to government customers and other folk. >> So is that a pre-sale activity, and you throw bodies at it, on your team. What's the engagement required kind of like a... Then during that workshop if you will, call it workshop. You come in and you show some value. Kind of throw some people at it, right? >> Yeah, you got-- >> You have SE, and sales all that. >> Exactly right. Exactly right. So we'll have our sales person managing the relationship, an SE also interacting with the data, working with the system, working closely with a contact on the customer's side. >> And they typically go, this is amazing, let's get started. Do they break it up, or-- >> They break it up. It's an iterative process, 'cause a lot of times, people don't fully grasp the power of these capabilities, so they'll come through and say, hey can you just help us with this small aspect of it, and once you show 'em that I can manage all of your unstructured text data, I can turn it into this giant knowledge graph, on top of which I can build apps. Then the light kind of goes off and they go, they go, all right, I can see this being used in HR, marketing, I mean legal, everywhere. >> Yeah, I mean you open up a whole new insight engine basically for 'em. >> That's exactly right. >> So, okay, so competition. Who are you competing with? I mean, we've been covering UiPath, they just had an event in Miami. This is the hot area, who's competing with you, who are you up against, and how are you guys winning, why are you winning? >> Yeah, we don't compete with the RPA folks. You know there's interesting aspects there, and I think we'll chat about that. Mainly there are incumbents like IBM Watson that are out there, we think IBM has done phenomenal research over the last 60 years in the field of AI. But we do run into the IBMs, big consulting companies, a lot of the AI deployments that we see, candidly are from all the big consulting shops. >> And they're weak, or... They're weaker than yours. >> Yeah, I would argue yes. (both laugh) >> It's okay, get that out of your sleigh. >> I think one of the big challenges-- >> Is it because they just don't have the chops, or they're just recycling old tech into a-- >> We do have new novel algorithms. I mean, what's interesting is, and this has actually been quite hard for us, is coming out saying, we've taken a step beyond deep learning. We've take a step beyond existing approaches. And really it's fusing those two paradigms of AI together, 'cause what I want to do is to be able to acquire the knowledge from the data, build a giant knowledge graph, and use that knowledge graph for different applications. So yeah, we deploy our systems way faster than everyone else out there, and our system's fully explainable. >> Well I mean it's a good position to be in. At least from a marketing standpoint, you can have a leadership strategy, you don't need to differentiate in anyway 'cause you're different, right, so... >> Yeah, yeah >> Looks like you're in good shape. So easy marketing playbook there, just got to pound the pavement. RPA, you brought that up and I think that's certainly been an area. You mentioned you guys kind of dip into that. How do you, I mean that's not an area you would, you would fit well in there, so, I want to get you, well you're not positioning yourself as an RPA solution, but you can solve RPA challenges or those kinds of... Explain why you're not an RPA but you will play in it. >> Here's what's so fascinating about this market is, a lot of people in AI will knock the RPA guys as not being sophisticated approaches. Those guys are solving real business problems, providing real value to enterprises, and they are automating processes. Then you have sophisticated AI companies like ours, that are solving really really high-level white-collar worker tasks, and it's interesting, I feel like the AI community needs to kind of come down a step of sophistication, and the RPA companies are starting to come up a level of sophistication, and that's where you're starting to see that overlap. RPA companies moving from RPA to intelligence process automation, where AI companies can actually add value in the analysis of unstructured text data. So around natural language processing, natural language understanding. RPA companies no longer need to look at specific structured aspects and forms, but can actually move into more sophisticated extraction of things from text data and other-- >> Well I think it's not a mutually exclusive scenario anymore, as you mentioned earlier, there's a blending of the two machine learning and symbolics coming together in this new reasoning model. If you look at RPA, my view is it's kind of a dogmatic view of certain things. They're there to replace people, right (laughs) >> Yeah, totally. >> We got robotics, we don't need people on the manufacturing line, we just put some robotics on as an example. And AI's always been about getting the best out of the software and the data, so if you look at the new RPA that we see that's relevant is to your point, let's use machines to augment humans. A different, that's a cultural thing. So I think you're right, I think it's coming together in new ground where most people who are succeeding in data, if you will, data driven or AI, really have the philosophy that humans have to be getting the value. Like that SRE example, Google, so that's a fundamental thing. >> Absolutely. >> And okay, so what's next for you guys? Business is good? >> Business is good. >> Hiring, I'm imagining with your kind of community >> Always hiring phenomenal AI and ML expertise, if you have it, >> Good luck competing with Google >> Shoot us an email. >> And Google will think that you're hiring 'em all. How do you handle that, I mean... >> Yeah I mean they actually get to work on novel algorithms. I mean what's fascinating is a lot of the AI out there, I mean you can date it all the way back to Rumelhart and Hinton's paper from 1986. So I mean, we've had backprop for a while. If you want to come work on new, novel algorithms, that are really pushing the limit of what's possible, >> Yeah, if you're bored at Google or Facebook, check these guys out. >> Check us out. >> Okay, so funding, you got plenty of money in the bank, strategic partners, what's the vision, what's your goal for the next 12 months or so, what's your objective? >> Yeah, focusing big on the customers that we have now. I'm always big on having customers, get a viral factor within the B2B enterprise software space, get customers that are screaming from the mountaintop that this is the best stuff ever, then you can kind of take care of it. >> How about biz dev, partnerships, are you guys looking at an ecosystem? Obviously rising tide floats all boats, I mean I can almost imagine might salivate for some of the software you're talking about, like we have all this data, here inside theCUBE, we have all kinds of processes that are, we're trying to streamline, I mean, we need more software, I mean, can I buy your stuff? I mean we don't have half a million bucks, can I get a discount? I mean how do I >> We'll see. We'll see how we end up. >> I mean is there like a biz dev partner program? >> No, not... >> Forgetting about theCUBE, we'd love if that's so, but if it's to partner, do you guys partner? >> So not yet in exposing APIs to third parties. So I mean I would love if I had the balance sheet to go to market horizontally, but I don't. So it's go to market vertically, focus on specific solutions. >> Industries. >> Industries, pharma >> So you're sort of, you're industry-focused >> government, financial services. >> That's the ones you've got right now. >> They're the three. >> For now. >> Yep. >> Okay, so once you nail an industry, you move onto the next one. >> Yeah, then I would love expose APIs for tab partners to work on this stuff. I mean we see that every day someone wants to use certain engines that we have, or to embed them within applications. >> Well I mean you've got a nice vertical strategy. You've knocked down maybe one or two verticals. Then you kind of lay down a foundational... >> Yeah. >> Yeah, development platform. >> Yeah, that's right. >> That's your strategy. >> And we can be, I mean at Kyndi I think we can be embedded in every application out there that's looking at unstructured data >> Which is also the mark of maturity, you got to go where the customers are, and you know the vision of having this global platform could be a great vision, but you've got to meet the customers where they are, and where they are now is, solve my vertical problem. (laughs) >> Yeah, and for us, with new technologies, well, show me that they're better than other approaches. I can't go to market horizontally and just say, I have better AI than Google. Who's going to come beyond the Kyndi person? >> Well IBM's been trying to do it with Watson, and that's hard. >> It's very hard. >> And they end up specializing in industries. Well Ryan, thanks for coming on theCUBE, appreciate it. Kyndi, great company, check 'em out, they're hiring. We're going to keep an eye on these guys 'cause they're really hitting a part of the market that we think, here at theCUBE, is going to be super-powerful, it's really the intersection of a lot of major markets, cloud, AIs, soon to be blockchain, supply chain, data center of course, storage networking, this is IoT security and data at the center of all the action. New models can emerge, with you guys in the center, so thanks for coming and sharing your story, appreciate it. >> Thank you very much. >> I'm John Furrier, here in theCUBE studios in Palo Alto. Thanks for watching. (dramatic music)