 theCUBE presents UiPath Forward 5, brought to you by UiPath. Hi everybody, we're back in Las Vegas. We're live with theCUBE's coverage of Forward 5, 2022. Dave Vellante with Dave Nicholson, Ted Kumer in the series. The executive vice president, product and engineering at UiPath brought on to do a lot of the integration and bring on new capabilities for the platform and we've seen that over the last several years. And he's joined by Dr. Edward Chalice who's the co-founder of the recent acquisition that UiPath made, company called Re-Infer. We're going to learn about those guys. Gents, welcome to theCUBE. Ted, good to see you again. Ted, welcome first time. Thank you. Great to be here with you. Yeah, so we have seen, as I said, this platform expanding. I think you can use the term business automation platform. It's kind of a new term. You guys introduced the conference. Where'd that come from? What is that? What are the characteristics that are salient to the platform? Well, I see the evolution of our platform in three chapters. You understand the first chapter, we call that the RPA chapter. And that's where we saw the power of Ui Automation applied to the old problems of how do I integrate apps? How do I automate processes? That was chapter one. Chapter two gets us to forward three in 2019 and the definition of this end-to-end automation platform. The capabilities from discover to measure and building out that core platform. And as the platforms progressed, what we've seen happen with our customers is the use of it goes from being very heavy in automating the repetitive and routine to being more balanced to now where they're implementing new business process, new capability for their organization. So that's where the name business automation platform came from, reflecting now that it's got this central role as a strategic tool sitting between their application landscape, their processes, their people helping that move forward at the rate that it needs to. And process mining and task mining, that was sort of the enabler of chapter two, is that right? Well, I'd say chapter two was, first the robots got bigger in terms of what they could cover into API integration, long-running workflows, AI and ML skills, integrated document processing, citizen development and professional development, engaging end users with things like user interfaces built with UiPath apps. And then the discovery, just an expansion of the whole surface area, which opened up a lot of things for our customers to do that went much broader than where core RPA started. And the other thing about this progression to the business automation platform is we see customers now talking more about outcomes. Early on, they talk a lot about our safety, and that's great. But then what about the business outcomes that's enabling the transformations in their business? And the other thing we're doing in the platform is thinking about, well, where can we land with solutions capabilities that more directly land on business measurable business outcomes? And so we had started, for example, offering an email automation solution, big business problem for a lot of our customers last year. And we'd started encountering this company re-infer as we were working with customers. And we encountered re-infer being used with our platform together. And we saw why we can accelerate this. And what that is giving us now is a solution now that elands with a very defined business outcome. And this way, we can help you process communications and do it efficiently and provide better service for your customers. And that's beginning of another important progression for us in our platform. So that's a nice segue. So tell us about re-infer. Why did you start the company? Right, yeah. So my whole career has been in machine learning and AI. And I finished my PhD around 2013. It was a very exciting time in AI. And me and my co-founders come from UCL, this university in London, and DeepMind, this company, which Google acquired a few years later, came from our same university. So very exciting time amongst the people that really knew about machine learning and AI. And everyone was thinking, you know, how do we, this is just a really big breakthroughs. And you could just see that there was going to be a whole bunch of subsequent breakthroughs. And we thought NLP would be the next breakthrough. So we were really focused on machine reading problems. But we also knew as people that had like built machine learning production systems, because I'd also worked in industry that journey from having a hypothesis that machine learning can solve a problem to getting machine learning into production, that journey is a painful, painful journey. And that, you know, you could see that you've got these advances, but getting into prod is just way too hard. So where do you fit in the platform? Yeah, so I think when you look in the enterprise, just so many processes start with a message, start with a note, start with a case ticket, or you know, some other kind of request from a colleague or a customer. And so it's super exciting to be able to, you know, take automation one step higher in that process chain. So you could automatically read that request, interpret it, get all the structured data you need to drive that process forward. So it's about bringing automation into these human channels. So I want to give the audience a sense here. So we do a lot of events at the Venetian Conference Center and it's usually very booth heavy, you know, brands and big, giant booths. And here, the booths are all very small. They're like kiosks and all pretty much the same size. So it's not like one vendor trying to compete with the other. And there are all these elements, you know, that feel like there's clouds and there's, you know, of course orange is the color here. And one of the spots is it has this really kind of cool sitting area around customer stories. And I was in there last night reading about Deutsche Bank. Deutsche Bank was also up on stage. Deutsche Bank, you guys were talking about a re-infer. So share with our audience what Deutsche Bank are doing with UiPath and re-infer. Yeah, so I mean, you know, beyond like before we automate something, we often like to do what we call communications mining, which is really understanding what all of these messages you're about that might be hitting a part of the business. And at Deutsche Bank and in many, you know, like many large financial services businesses, huge volumes of messages coming in from the clients, we analyze those, interpret the high volume query types, and then it's about automating against those to free up capacity, which ultimately means you can provide faster, higher quality service because you've got more time to do it. And you're not dealing with all of those mandate. So it's that whole journey of mining to automation of the comms that come into the corporate. So how do I invoke the service? So is it as a mother module, or what's the customer onboarding experience like? Well, so I think the first thing that we do is we generate some understanding of actually the communications data they want to observe, right? And we call it mining, but you know, what we're trying to understand is like, what are these communications about? What's the intent? What are they trying to accomplish? Tone can be interesting, like what's the sentiment of this customer? And once you understand that, you essentially then understand categories of conversations you're having, and then you apply automations to that. And so then essentially those individual automations can be pointed to sets of emails for them to automate the processing of. And so what we've seen is customers go from things they're handling 100% manual to now 90% for 5% of them are handled basically with completely automated processing. The other thing I think is super interesting here and why communications mining and automation are so powerful together is communications about your business can be very, very dynamic. So new conversations can emerge. Something happens right in your business. You have an outage, whatever. And the automation platform being a very rapid development platform can help you adapt quickly to that in an automated way. Which is another reason why this is such a powerful thing to put the two things together. So you can build that event into the automation very quickly yourself. Yeah, that's totally cool. So add on the subject of natural language processing and machine learning versus machine teaching. If I text my wife and ask her, would you like to go to an Italian restaurant tonight? Yeah. And she replies, fine. Yeah. Yeah. Okay. How smart is your machine? And of course context usually literally denotes things within the text and a short response like that's very difficult to do this. But how do you go through this process? Let's say you're implementing this for a given customer. And we were just talking about the specific customer requirements that they might have. What does that process look like? Do you have an auditor that goes through and do you get like 20% accuracy? And then you do a pass and now you're at 80% accuracy and you do a pass. What does that look like? Yeah, so I mean, when I was talking about the pain of getting a machine learning model into production, one of the principal drivers of that is this process of training the machine learning model. Right. And so what we use as a technique called Active Learning which is effectively where the AI and ML model queries the user to say, teach me about this data point, teach me about this sentence. And that's a dynamic iterative process. And by doing it in that way, you make that training process much, much faster. But critically that means that the user has, when you train the model, the user defines how you want to encode that interpretation. So when you were training it, you would say fine from my wife is not good, right? So it might be fine. Do you have a better suggestion, right? But that's actually a very serious point because one of the things we do is track the quality of service that our customers use us to track the quality of service they deliver to their clients, right? And in many industries, people don't use flowery language, like thank you so much or I'm upset with you. What they might say is fine. And you know as the person that manages that client, that is not good, right? Well, they might say, I'd like to remind you that we've been late the last three times, you know? It's like- This is urgent. No, okay, alright. So it's important that the client, our client, the user of RIMF can encode what their notions of good and bad are. So a quick follow up on that. Differences between British English and American English. In the UK, if you're thinking about becoming an elected politician, you stand for office, right? Here in the US, you run for office. That's just the beginning of the vagaries and differences. Well, you know, I've now got a lot more American colleagues and I realize my English phrasing as often goes amiss, so I'm really aware of the problem. We have customers that have contact centers, some of them are in the UK, some of them are in America, and they see big differences in the way that the customers get treated based on where the customer is based. So we've actually done analysis in re-invert to look at how agents and customers interact and how you should route customers to the contact centers to be culturally matched, because sometimes there can be a little bit of friction just to that cultural mapping. So what's the general philosophy when you make an acquisition like this and you bring in new features? Do you just wake up one day and all of a sudden there's this new capability? Is it a separate sort of four-pay module? Does it depend? I think it depends. You know, in this case, we were really led here by customers. We saw a very high value opportunity in the beginnings of a strategy and really being able to mine all forms of communication and drive automated processing of all forms of communication. And in this case, we found a fantastic team and a fantastic piece of software that we can move very quickly to get in the hands of our customers via UiPath. We're in private preview now. We're going to be GA in the cloud right after the first of the year and it's going to continue forward from there. But it's definitely not one size fits all. Every single one of them is different and it's important to approach them that way. Right, right. So some announcements. Studio Web was one that you could try. I think it came out today. Can't remember what it was today. I talked about it yesterday on the keynote anyway, on the keynotes. Why is that important? What is it all about? Well, we talked, you know, at a very top level, you know, I think every development platform thinks about two things for developers. They think, how do I make it more expressive so you can do other things, richer scenarios? And how do I make it simpler? Because fast is always better and lower learning curves is always better and those sorts of things. And so, re-infers a great example of, look, the runtime is becoming more and more expressive and now you combine communications data as part of your automation, which is super cool. And then, you know, Studio Web is about kind of that second point and Studio X are already low-code, visual, but they're desktop. And part of our strategy here is to elevate all of that experience into the web. Now, we didn't elevate all of Studio there. It's a subset. It is API integration and web-based application automation, which is a great foundation for a lot of apps. It's a complete re-imagining of the Studio user interface. And most importantly, it's our first cross-platform developer strategy. And so that's been another piece of our strategy is to say to the customers, we want to be everywhere you need us to be. We did cross-platform deployment with the Automation Suite. We got cross-platform robots with Linux robots, serverless robots, Mac support, and now we've got a cross-platform devs story. So we're starting out with a subset of capabilities, maybe oriented toward what you would associate with citizen scenarios, but you're going to see more roadmap bringing more and more of that. But it's pretty exciting for us. We've been working on this thing for a couple of years now, and this is a huge milestone for the team to get to this point. I think my first conversation on the queue with a customer was six years ago, maybe, at one of the earlier forwards. I think forward two. And the pattern that I saw was basically people taking existing processes and making them better, taking the mundane away. I remember asking customers, are you kind of paving the cow path? Aren't there sort of new things that you can do with new processes? And they're like, yeah, that's sort of the next wave. So what are you seeing in terms of automating existing processes versus new processes? I would see re-infer is going to open up a whole new vector of new processes. How should we think about that? Yeah, I think, in some ways, RPA is this reputation because there's so much value that's been provided in the automating of the repetitive and routine. But I'd say, in my whole time, I've been at the company now for two and a half years, I've seen lots of new novel stuff stood up. I mean, just in COVID, we saw the platform being used in PPP loan processing. We saw it in new clinical workflows for COVID testing. We see it, and we've just seen more and more progression. And it's been exciting that the conference to see customers now talking about things they built with UiPath apps. So app experiences they've been delivering. I talked about one in healthcare yesterday and basically how they've improved their patient intake processing and that sort of thing. And I think this is just the front end. I truly believe that we are seeing the convergence happen and it's happening already of categories we've talked about separately, iPad, BPM, low code, RPA, it's happening. And it's good for customers because they want one thing to cover more stuff. And I think it just creates more opportunity for developers to do more things. Your background at Microsoft probably well prepared you for a company that was born on-prem and it went all in on the cloud and had multiple code bases to deal with. UiPath has gone through a similar transformation. We talked to Daniel last night about this. You're now cloud first. So how is that going just in terms of managing multiple code bases? Well, it's actually not multiple. It's the same one. It's the deployment models I should say. It's the first thing, yeah. The deployment models. Another thing we did along the way was basically re-platform at an infrastructure level so we now can deploy into a Kubernetes Docker world what you'd call the cloud native platform. And that allows us to have much more of a shared infrastructure layer as we look to deliver to the automation cloud, the same workload to the automation cloud that we now deliver in the automation suite for deployment on-prem or deploying a public cloud for a customer to manage. Interesting enough, that's how re-infer was built which is it was built also in the cloud native platform. So it's going to be pretty easy. Well, pretty easy. There's some work to do but it's going to be pretty easy for us to then bring that into the platform because they're already working on that same platform and provide those same services both on-premises and the cloud. Without having your developers have to think too much about both. Okay, I got to ask you. So I could wrap my stack in a container and put it into AWS or Azure or Google and it'll run great. As well, I could tap some of the underlying primitives of those respective clouds which are different. And I could run them just fine. Or, and, I could create an abstraction layer that I could hide those underlying primitives and then take the best of each and create an automation cloud, my own cloud. Does that resonate? Is that what you're doing architecturally? Is that a roadmap? Certainly going forward. You know, in the automation cloud, the automation cloud we announced a great partnership or a continued partnership with Microsoft just Azure and our platform. We obviously take advantage of anything we can to make that great and native capabilities. And I think you're going to see in the automation suite stewing more and more to be in a deployment model on Azure be more and more optimized to using those infrastructure services. So if you deploy automation suite on-prem, we'll use our embedded distro. Then when we deploy it, say on Azure, we'll use some of their higher level managed services instead of our embedded distro. And that will just give customers a more automation, a more, a better optimized experience. Interesting to see how that'll develop. Last question is, you know, what should we expect going forward? Can you show us a little leg on the future? Well, we've talked about a number of directions. This idea of semantic automation is a place where, you know, you're going to, I think, continue to see things, shoots, green shoots come up in our platform. And, you know, it's somewhat of an abstract idea, but the idea that the platform is just going to become semantically smarter. You know, I had to sort of re-infer as a way we're semantically smarter now about communications data and forms of communications data. We're getting semantically smarter about documents, screens, you know, so developers aren't dealing with like this low level stuff. They can focus on business problem and get out of having to under, you know, deal with all this lower level mechanism. That is one of many areas I'm excited about, but I think that's an area you're going to see a lot from us in the next coming years. All right, guys, hey, thanks so much for coming to theCUBE. Really appreciate you taking us through this awesome platform extension. All right, keep it right there. Everybody, Dave Nicholson, I'll be back right after this short break from UiPath Forward 5 from Las Vegas.