(energetic string music) >> Hi, I'm Peter Burris and welcome to another Cube Conversation from our beautiful studios in Palo Alto. As we do with every Cube Conversation, we want to find a great topic and a smart person to talk about it, and that's what we've got today. What's the topic? We're going to be talking about new classes of AI, that are capable of addressing some of the more complex white-collar worker work that gets done. And to have that conversation, we've got Amy Guarino, who's the COO of Kyndi, here on the Cube with us today. Amy, welcome to the Cube. >> Thank you very much Peter. >> So, tell us a little bit about yourself first. >> Sure, so I grew up at IBM in sales and sales management, and then started doin' startups. Most recently, I spent eight years at Marketo, and then just after the Vista acquisition, I joined Kyndi. So that was two years ago. It was a nine person science and research kind of an organization and we've done a few things to get the group in order and we now have 31 folks and really focus on explainable AI. >> Okay, so explainable AI, what is that? >> So what's really interesting is that AI has had a lot of success, specifically around deep learning, neural nets. And one of the challenges with that approach is that it is a black box. You can't understand what the outcome was, or is. And what's really interesting, I was with a customer yesterday, and they were telling me that they were using deep learning around water treatment plants. But they got a lot of feedback that if I'm going to be drinking water, you need to explain to me what it is that you're doing to it and why. And they were like, well holy cow, we can't. And they said, that's a problem. And that's why they came to us, cause they wanted to learn about how you could do explainable type of AI. And the approach that we take really focuses on language. And how do analyze that language, but doin' it in a way where you're able to trace back to the actual raw data source to make sure that it really is correct. So we think about it as more augmenting humans versus replacing humans. >> Well let me see if I can break that down, cause I think of AI, at least things that are pertinent to AI, in a couple of different ways, kind of a mix. To what degree is something programatic, and therefore you can discover patterns in how the program operates so that you can improve it. But there's also social elements to any system that has to happen. >> Yes. >> And it's, and the black box is good for very programatic, relatively structured, where the problem space is relatively well defined, relatively well articulated and has a very specific role in a broader context of things. But when we start talking about activities that have a significant social component, where human beings are a major participant or a major source of value in the activity set that's being performed, you can't count on a black box because humans won't adopt it. So is it, when you say discoverable AI, was that it? >> Explainable AI. >> Explainable AI, is it really AI for those use cases where human beings are and essential part of the value, creation value chain? >> I think that's a great way to think about it. We initially thought it was going to be most applicable in regulated industries, where you had a requirement to explain it. But what we found is it absolutely works there, but it also is very relevant for any kind of decisions where humans are allocating resources or doing something and they have to explain why. >> So the explainable AI means that the AI can be more easily adopted by human centered activities. >> Absolutely. >> Okay, so how, so we think about AI, we think about deep learning, we think about machine learning, I mean, text automatically introduces natural language processing. What of, what elements are you combining to make the explainable AI of Kyndi work? >> So what we do is we actually ingest documents, PDF's, word documents, any kind of text, we then apply natural language processing to that to be able to parse out the entities, the terms, all of the concepts. We apply machine learning so that we can extract what we call proto-ontology, or structure, from that. So you don't have to do a lot of work upfront building out a taxonomy, and therefore we have benefit of being able to go from one domain to another very quickly and then we take all-- >> Which, by the way, blackbox AI does not do well. >> That's correct, that's absolutely correct. We addressed that deficiency as well. And then we take that output and we put it in what we call cognitive memory, which is a knowledge graph. It's a proprietary knowledge graph that allows us then to be able to search the information on there from a context perspective, so a cognitive type of search. We can also apply certain preset, sort of a filters, for different applications. So, one of the areas where we focus on is around pharmaceutical, and they're very interested in understanding and analyzing a lot of the texts associated with reports around drug discovery. And to be able to understand where there's data integrity and where's there's not-- >> And whether the process had been followed right, you got to believe. >> Yes, absolutely. And to be able to apply those preset filters against that across a really large data set and be able to highlight and get to a smaller subset that the scientists can dig into and really understand where there are potential issues and figure out how to mitigate those issues is critical. >> So let me see if I can generalize. A explainable AI being applied in a domain, like pharmaceutical-- >> Yes. >> that has a common set of audit features to it, in terms of the methods used-- >> Yes. >> for drug discovery, drug authorization, and okay. And utilizing that with the drug discovery people who are responsible for actually validating that the process is being followed appropriately to limit the amount of manual work that goes into the audit process, have I got that right? >> Yes, absolutely, by a huge factor. >> How huge? >> It's like 100 times. >> Oh, okay, well that works. >> Yes, it does work. >> So we're talking about being able to, you said 100 times, to reduce the number of people or to increase the volume of possible candidates for drug commercialization. >> Absolutely right, absolutely right. >> So what other domains do you expect Kyndi to be applied to? >> It's a very broad capability. It's any kind of work where you're reading lots of text. Today we focus in terms of the pharma opportunities. We have a lot of manufacturing folks that are looking at ways to be able to look at and review, sort of tribal knowledge that exists within a manufacturing environment. As people retire, there's a lot of information that doesn't quite get passed down and they're trying to figure out ways to get that information and also make it more easily searchable. >> Can you look at COBOL code? >> Uh, we've talked about it, we've talked about it. We do that and also in the government, we do a lot of work. >> Alright, so, you know it's interesting that you started talking about pharmaceutical. Most firms like yours work their way up to pharmaceutical. >> Yes. >> Because pharmaceutical is, you know the FDA is governed by rules where liabilities actually are associated with software. >> Yes. >> Most domains doesn't have to worry about that. So you guys are starting with the hardest problems with the greatest potential commercial risk and you're working your way into others. >> Well I think it's because it's explainable. I think that's the advantage that we have. And so we are able, then, to go back and provide that provenance to be able to support how we got there. And so it makes a big difference. >> Okay, so what's going to happen with Kyndi in 2019? >> We're going to continue to grow and really expand, particularly on the commercial side of the business, and go beyond pharmaceutical into manufacturing, maybe even a little for the financial services. But really make our customers successful, show how successful we can be. And that's going to be our marketing capability, to be able to help share this with the rest of the world. >> Yeah, if you're around COBOL, you can help my CIO guys. >> Okay. (laughter) >> There's a lot of people, like me, retiring. Alright, Amy Guarino, COO of Kyndi, talking about explainable AI and the need for new classes of tools that can augment human activity, make 'em more productive. Amy, thanks very much for being on The Cube. >> Thanks Peter, it's been great. >> Once again, I'm Peter Burris, thanks very much for watching this Cube Conversation. Until next time. (energetic string music)

(light orchestral music) >> Hi, I'm Peter Burris and welcome to another Cube Conversation from our wonderful studios here in Palo Alto, California. One of the most challenging things that any business has to navigate, especially B2B business, is that crucial relationship between sales and marketing and customer engagement. How to make customer engagement as high quality, high value, to a customer but also as productive to the business as possible. And to have that conversation, we've got Amy Guarino, who's a COO of Kyndi here on theCUBE with us today. Now Amy, welcome to theCUBE. >> Thank you very much Peter. >> So we're going to start with something that recently happened. You recently attended a Women in Sales conference, tell us a little bit about yourself and then we can talk about that conference. >> Sure, I'm the COO of Kyndi which is an explainable AI company, and that means I have responsibility for everything customer-facing. So from sales, marketing, services, support, and anything else to help make sure that we run the business in a good way. I recently came from Marketo, I had eight years there, where sales and marketing was really definitely a critical piece, and hopefully we helped change a little bit in terms of the way people think about sales and marketing. >> Well that's a small job that you have so, but nonetheless you had time to go to this Women in Sales conference, tell us a little bit about it. >> Sure yeah, so it's a group that started out in New York city, and then they've been having some events across the country, but this was the first West Coast event. So myself and there were two other women that have sales leadership roles out here and we participated and there were about 120 mostly women, I think there were three fellas there that joined. So what I couldn't figure out is why more fellas didn't come, it seems like a great place to meet a bunch of pretty interesting women. So, it really was a fun event and a lot of the questions focused on women in sales careers and how best to develop a sales career. >> Well certainly I'm sure it would have been an opportunity for some men to discover something about how women envision the role that sales plays, the role of engagement. There have been a number of studies over the years that women actually seem to demonstrate an even stronger affinity for making some of those connections necessary to traverse a very highly complex, high-value sales relationship. What were some of the highlights that you took away from the conference? >> Well I think some of things that were pretty interesting was understanding how do women look at sales differently and really what are some of the unique aspects of how women approach things, and a lot of it focused on listening skills and a woman's ability to, and it's not to mean that fellas aren't good listeners. >> What, what, what, what? I'm sorry, what? >> But I think it is something that women do have a natural affinity to be able to listen and to really pull out when someone is speaking, whether it's a prospect or a customer, what really is important to them? >> So listening is one, any others that just pop to mind? >> I think the other was in terms of sales management is really interesting is the ability for women sales managers and leaders to be able to understand what are the strengths and weaknesses of folks on the team, how to be able to coach them, and then how to pull together a team that really takes advantage of all of the different skills across the whole sales team. >> So here's one of the questions that I have about looking at women in sales as a thing. You have to on the one hand be very careful about generalizing, but on the other hand you really do want to discover what attributes of a person tend to create value for business, create value for the customers, et cetera. Was there any conversation about how far we should take some of these generalizations like I once had someone tell me, "Well men are very transactional, "women are very relationship-orientated." Which always seemed to me to be a bromide. But how far should we take the notion of women specifically in sales as we think about business management? >> Well the piece I think we talked about a lot last night was not so much in terms of the generalizations, but the fact that in today's world you want to hire the best of the best, and in order to hire the best of the best, women make up 50% of the population, you want to be able to-- >> And 80% of the best. >> (laughs) Well I appreciate that, but you want to be able to put yourself in a business culture or a sales culture where that's appreciated. I think especially in tech there's so many situations where you walk into a tech sales organization, and it's 80, 90% fellas and it makes it tough for women to want to join that kind of an organization. And as a sales leader, as a sales manager, if you want to hire the best of the best, you want to make sure that you're attracting people, the best, and so therefore you want to make sure your culture really is in a position to be able to attract the best. >> Yeah, 'cause I think one of the things that our Chief Revenue Officer has to do is it has to drive sales productivity which means taking advantage of skills and improving sales enabled them. But at the same time establishing a culture that encourages each person to shine, that doesn't diminish different types of skills. And I got to believe that's one of the things you took away. How are you applying some of the lessons that you learned to your job as a COO and responsible for customer engagement at Kyndi? >> Well I think the one thing is to really be attentive to it. Sometimes your business is growing so fast you're just like, "Oh, I'm just going to hire and get things going." And one of the things, we're not quite at that stage where we're adding tons of people yet, but we know we're going to, is making sure that we're thinking about and being very deliberate in terms of the types of folks that we're recruiting. And one of the things that I've seen most effective, particularly for fast-growing tech companies, is hiring women leadership. I think sometimes, and I don't think it's where the fellas are hiring people 'cause they want to not hire women, but its more they hire people they know. And so all of a sudden you look up and you realize, oh my goodness I've got six first-line sales managers and they're all guys. And when a woman goes in to interview for a first-line role and they look at that and say well, your whole management staff is all men, how is that going to make me feel comfortable? Is this the kind of environment where I'm going to be able to be successful? And so it's really being very deliberate and intense in terms of thinking about how can I make sure that I do have some women in leadership? And I think that can change the dynamic quite a bit in terms of the company culture. >> And are you discovering that at least from a Kyndi standpoint, I mean obviously Kyndi at very, very senior levels is you. So that says something about what constitutes being important at Kyndi. Do you anticipate that having more women is going to improve your ability to engage customers? Improve your ability for customers to take action quicker? What's the expectation? >> I think that the expectation is that you've got different types of perspectives and different types of way to look at customer acquisition and customer engagement and customer support, and we can all help each other. When you have different opinions and different ways of looking at things, as a team then you really get much more productive in terms of being able to do the right things for customers and make sure they're successful. >> So a culture that encourages, or at least liberates and takes advantage of diversity. Talk a little bit about the sales enablement side of that. Because again one of the things I mentioned earlier is that as chief revenue officer, part of your job has to be to accelerate increases in productivity of your field organization as fast as possible. How does what you heard from the conference yesterday, that mission, sales enablement, et cetera, come together, collide? >> Sure, yeah I'm not sure that it's specific to women, but it's any time you bring on a new rep, you want to be able to take that gap from when you hire them to the time their productive, and productive means being able to go out and actually sell something to a customer. You want to make that as quick as possible and as efficient as possible. So it's really understanding that path and understanding what it's going to take to help make that rep successful. Doing that in a systematic approach as opposed to, hey why don't you go out and go on a few calls with somebody and then see how it goes. Because when you actually take that and make it into a process, you can understand where people are picking things up, where they're not picking things up, what you can actually do to enhance that process and make it go faster and make it easier for new to people to come on board and be productive. 'Cause sales people want to sell, they want to get engaged with customers, they're eager to get going and really make an impact, and so the better you can enhance that process I think the better and more successful they'll feel. And then from an organizational standpoint, the quicker you can make your number, because it's all about how do I have as many quota-carrying, productive reps in the territory as quickly as possible. >> Yeah, one last thought, I think other thing is that sales people tend to learn from other sales people. Having a culture that encourages that kind of sharing and that kind of respect and that kind of diversity means that you're going to get a lot more different perspectives on what works. >> Exactly, it's all about figuring out what works and what doesn't work and then sharing that information across the group. >> Alright, fantastic, Amy Guarino, COO of Kyndi, talking about women in sales and how she is COO and to space taking some of the lessons learned and applying it to make Kyndi a more inclusive, better customer serving business. >> Terrific, thanks Peter. >> Thanks Amy, and once again this is Peter Burris, thanks again for listening to this Cube Conversation. Until next time. (light orchestral music)

(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)