Natasha, it's great to have you here.
Thank you so much. A real pleasure working with Vatusa all these years and we're looking forward to doing tremendous things together.
Absolutely. It's wonderful to be here and thank you so much for hosting me. I mean, like, Verusa and Pega have a relationship which is more than 25 years now. So we are extremely proud of that relationship all the way from engineering to implementation to now joint go-to-market. So really, really great to be here and to talk to you and to, you know, listen to your perspectives in terms of what you are seeing and what we are seeing in the marketplace.
Well, I think we're all seeing a lot of AI and agentic talk going on, and I noted that Vatusa recently won an award for your AI work. And I think that's terrific because AI is so central to what is going to change the business, I think, in ways unlike we've ever seen before, to tell you the truth.
No, totally, totally. I think, look, our strategy has been very simple, right? We are not a platform player and we don't intend to be, because that's probably Pega's job, right, to create the next set of platforms. But the way we have approached our AI strategy is really to set up a set of productized solutions, right, which kind of sit and can get orchestrated on multiple different platforms, right? Whether it is on SDLC, whether it is on enterprise functions, whether it is on line of business use cases. So that's really the approach that we've taken and we are seeing a lot of resonance in the marketplace. Yeah, so I think that's probably why we get rated so highly right now through the analysts.
And we want a platform that you can use to achieve that, both with better quality, because I think a lot of time people focus on the speed of AI, but if you use the AI right, you get extremely good learnings and knowledge and understanding. And at the same time, you also get it fast, and that's part of what can really make, I think, businesses more responsive to their customers, able to save money in ways that they didn't think was going to be possible, and be able to grow.
I think, Alan, that's a great point and I would like to get your perspectives. Look, we work in a lot of heavily regulated industries, whether it's financial services or healthcare or life sciences and so on. And there is this huge debate about, you know, autonomous agents versus directed agents and how do you go from being assistive to prescriptive to directive. How are you seeing that? And, you know, there's a lot of kind of nervousness, I would say, you know, while speed and quality are important, but so is control. So how are you seeing about it and how is Pega handling this kind of a...
Well, we're actually handling it very differently than we see the other players handle it. There are a lot of companies that say, 'Hey, use the miracle of LLMs, set them free, create these enormous libraries of maybe even thousands of prompts that will agentically drive parts of your business. And maybe they can tell you what they've done after they do it, but they actually don't tell you what they're going to do before they do it.' And when we take a look at not just regulated industries, but any industry in which you'd kind of like to know what you were doing, you'd kind of like to perfect a set of best practices, we think that's really backwards. And, you know, in 2022 when ChatGPT really hit and we got super deeply involved in it, we made a couple of really important strategic decisions. One of them was that we were going to take vector database technology, which is an LLM-style technology, to codify our knowledge and to make it so that our best practices could be incorporated in how we further use AI. And then we created this thing called Blueprint, which is able to use those best practices and that knowledge with the information from the client and put those together to be able to really define highly precise and specific workflows that you can look at, that know how to integrate with backend systems, that know how to actually do things, and use those workflows that are created at design time as a way to manage the agents at runtime. And so this bifurcation of a really, really highly LLM-driven agentic design time, coupled with at runtime using the LLM for things that it does well but reliably, things like translation or things like reading a document, I think has turned out perfectly for our customers in terms of being able to blend the reasoning and the power but also intrinsically control and constrain it. So it's almost like you're configuring agents in design time and actually getting them to a point where they are really checked, balanced, you know, directed, the context coming in clearly. And, you know, I think businesses in general are really a collection of processes and decisions. And what we say is, use the AI to enumerate and to create the library of these processes and decisions, and then make it easy at runtime for the AI to just kind of add whatever little tweaks are needed.
No, I totally agree. And I think, Alan, it will also be good to understand, because I think with Blueprint, I feel like there are two other distinct advantages. One is that, you know, Pega is now able to provide clients context to get merged with your own best practices. Right? That's number one. And the second piece, at least to me what I feel, is that there is a degree of separation that you could still use Blueprint in cases where even Pega underlying platform does not exist. So I think both of those, I feel, are kind of accelerators, if you will, towards the, you know, business transformation or legacy modernization or any of the archetypes, or even new product development, which, you know, clients would like to go for.
And I think that's right on target, and especially true now that we've created partner-branded blueprints where Vertusa themselves has created a vector database where you're able to put your IP, we can't even see it. This is really proprietary and private to you. And if a Vertusa employee uses Blueprint, it will incorporate the intellectual property that you value and that is private to you and that would be used just with you and that client. And I think that lets our partners really bring their richness of history and, as you said, the specialization you may have in certain areas and feather it in to Pega's experience, the knowledge from the internet, and all the other things that Blueprint brings together.
No, absolutely. And I think we are seeing the advantage of that already in several of the workshops that we are doing with our clients, right? Because now we are able to almost on real-time sit down with the client, look at a problem, solve it, or create the solution with them, co-create the solution with them, because all that context, all that knowledge is there. And we don't need tons and tons of people, you know, to bring in and to create documentation and so on, but it's really about jointly sitting together, creating a solution, and then off you go. So I think that's great.
I'd like to show you some things we released just this week on the ability to do really a vibe-ish experience with Blueprint. Would you like to go build an application?
Yes, absolutely. Let's do that.
Hey, great. Thank you. So, come around and check this out. Let me explain what we're seeing here. This is pega.com. So, this is our standard Pega site. Anybody can come. And because this has been logged on with a Vertusa ID, you get to see the Vatusa intellectual property incorporated in this as well. And I call what you do a domain encapsulation for your transformation here as well. And so this is something that you wouldn't see with any other partner or with Pega. This is private to you. And here you've got the Blueprint dashboard. Allows you to see blueprints that you and your team have been making and allows you to share them between them and edit them. But let's create a new one. I can show you some of the new features that we just added.
We would love to see that.
So you hit create a blueprint. And of course, Blueprint is free. So you're able to use this and take advantage of it to really help use generative AI to prompt best practices and good thinking in conjunction with your IP and Pega's IP.
And this is quite similar to the point that you were mentioning, that this is the design time where we are actually kind of, you know, setting up those...
That's exactly right. You're able to really, really design it. What do you want to do? Banking?
Sure. Yeah. Let's go ahead. It's been a popular one for us always here as well, and maybe corporate banking.
And what function you want to do?
Maybe we can do onboarding, maybe.
No, onboarding is always a good one. Yeah. And say next. What's the purpose of this application? Is it, you know, client onboarding for financial services? Sure. It's going to propose a set of descriptions. All of this is live. All of this is being driven by the LLM. Yeah. And in doing this, what it does is it lets you change it. And if you've got bank-specific documents or things particular to an organization, it will allow you to incorporate that. And then let's just pick an organization. It's only going to access public information for this sort of thing. Here we'll pick Citibank.
Yeah, that's fine. One of my favorites.
And we'll go and hit generate. With just that information, it's able to envision what an optimized onboarding process will be. Bring your IP, bring our IP together, and it goes through and analyzes the requirements. It researches best practices. She goes out to the internet to see what can I know about this from industry standard places, creates a data model, and it's identified that these are the things that you'd want to do if you were in the business of client boarding for financial services.
