00:11:32 · Neil C. Hughes, Stephen Deasy
[00:11:32] S0: Love it. And there's also a lot of excitement around AI agents, what they will eventually be able to do, but businesses still need to justify that tech spending today. Anything else a company could achieve with Streamhouse right now? What would it struggle to achieve with its existing data architecture? Maybe just to help people listening and watching understand what that before and after picture might look like. [00:11:54] S1: Yeah. I I... Maybe a good example of this in in... That can make it real would be another great customer of ours, Citizens Bank, for example, one of the, you know, loan fulfillment processing, for You know, they were able to move to real time processing and real time stream on what is now the Streamhouse, and in there are able to reduce loan fulfillment times, increase customer satisfaction, decrease loan reprocessing, and things like that. And so what we see is that by bringing these capabilities together, you can have very real meaningful impact for your business, for your customers. And by having all of that business context current and available, it also unlocks the business... The use cases maybe you haven't thought of. Yeah. So now, it's available in a catalog where somebody comes up with a great idea, wants to connect into some AI use cases, you already have that governed fresh data available to you, which reduces the time from idea to production very significantly.
00:11:54 · Stephen Deasy, Neil C. Hughes
[00:11:54] S1: Yeah. I I... Maybe a good example of this in in... That can make it real would be another great customer of ours, Citizens Bank, for example, one of the, you know, loan fulfillment processing, for You know, they were able to move to real time processing and real time stream on what is now the Streamhouse, and in there are able to reduce loan fulfillment times, increase customer satisfaction, decrease loan reprocessing, and things like that. And so what we see is that by bringing these capabilities together, you can have very real meaningful impact for your business, for your customers. And by having all of that business context current and available, it also unlocks the business... The use cases maybe you haven't thought of. Yeah. So now, it's available in a catalog where somebody comes up with a great idea, wants to connect into some AI use cases, you already have that governed fresh data available to you, which reduces the time from idea to production very significantly. [00:12:57] S0: And when we're talking about AI agents making decisions in real time, anything you can add on what human oversight sit in that process, particularly when an automated decision could have financial or even regulatory consequences? We...
00:12:57 · Neil C. Hughes, Stephen Deasy
[00:12:57] S0: And when we're talking about AI agents making decisions in real time, anything you can add on what human oversight sit in that process, particularly when an automated decision could have financial or even regulatory consequences? We... [00:13:12] S1: I think in in the agentic world, it's... We we think of it as a new type of model of operating. Yeah. We're moving from worlds where we built software and we would test in regression, which we're still doing, to more probabilistic models where you have to continuously evaluate and look at the data, and it might be that a new model comes out. It might be that you have some additional data points. So if you think about the combination of your models and your tools and your context, Bringing these together allows you to continuously evaluate the quality and the outputs. And so something like the Streamhouse allows you to do that both in real time, but then also reprocess some of your historical data with maybe some of this newer context or tools, and so they're very common use cases that we see in a Streamhouse that allow you to have confidence that you can make those business decisions in real time. [00:14:08] S0: And again, going back to the return on investment, sorry to go back to that. I know it's such a big topic right now. When people are measuring it, I think it's very difficult to know what metrics to measure. So is it faster decision making, lower operating costs, better better customer experience, all of these things or something else? How do you measure that ROI?
00:14:08 · Neil C. Hughes, Stephen Deasy
[00:14:08] S0: And again, going back to the return on investment, sorry to go back to that. I know it's such a big topic right now. When people are measuring it, I think it's very difficult to know what metrics to measure. So is it faster decision making, lower operating costs, better better customer experience, all of these things or something else? How do you measure that ROI? [00:14:28] S1: I think it's... It is it is all of those things, and it it varies. So what we see with our customers with Streamhouse architectures is operational use cases like observability. We see financial use cases like fraud detection. So another exciting announcement today was the managed model inference. So now on Confluent Cloud, you can connect up to models that we will run for you. We introduced Google and IBM time series for things like anomaly detection. [00:14:59] S0: Yeah. [00:15:00] S1: So whether it's an observability use case or a fraud detection use case, things like that, we're now able to provide the plug ins for those in addition to your own proprietary capabilities within that Streamhouse architecture, that goes back then to unlocking the business value quickly so that you're not spending time standing up and building infrastructure. Instead, you're solving real business problems with the data you have in a secure, governed environment.
00:15:00 · Stephen Deasy, Neil C. Hughes
[00:15:00] S1: So whether it's an observability use case or a fraud detection use case, things like that, we're now able to provide the plug ins for those in addition to your own proprietary capabilities within that Streamhouse architecture, that goes back then to unlocking the business value quickly so that you're not spending time standing up and building infrastructure. Instead, you're solving real business problems with the data you have in a secure, governed environment. [00:15:29] S0: And one of the things I always try and do on this podcast every day is bust a few myths and misconceptions. Now as someone that will spend a lot of time, not only in the technical environment, but also doom scrolling down your phone, you probably see a lot of information on LinkedIn, etc. Are there any misconceptions that enterprises have about what they need to change to make what we're talking about work today? It's a good question. Yeah. Yeah. There can be so many different frustrations, and I just wondered if there are any trends in the kind of questions that people are asking all the kind of things that they have in their head.
00:16:00 · Stephen Deasy
[00:16:00] S1: Well, one... I I think one of the interesting conversations that I'm having at Current today and and and with customers is that... I don't think it's a misconception. I think it's actually what we hear from customers is almost an affirmation that what we're describing in Streamhouse and how it works in the various components actually resonate not because it's new or somehow revolutionary. It's actually putting a name on things that people have already been doing. Streaming the data, governing the data, processing it, and now querying that in real time is something that people have been doing and are feeling even more opportunity or challenge to do because AI is pushing everything so real time that maybe it's that Streamhouse finally gives a name to what people have been feeling and helps then categorize those different pieces that you need. So the nice part about Streamhouse is that this is something we launched as an industry category. This was not a Confluent proprietary. So streamhouse.com is a number of vendors and players in the space that came together and described it, and then we believe we have the best data streaming platform that provides a Streamhouse to you. And so when we talk to customers about that, there's a lot of, oh, that helps me frame a thing I've been seeing and feeling, and now I can use.