 Hi, this is your host Aptin Bhatia and welcome to another episode of TFI Newsroom. And today we have with us once again, Nora Jones, CUN founder of Jelly.io. Nora, it's great to have you on the show again. Thanks for having me, Swap. Excited to be here. It's great to have you back on the show and, you know, you folks are, you know, making announcements, leveraging, you know, Genitive AI or chat GPT. When we talk about incident management response and we have had so many discussions in the past, just talk a bit about how you folks have been leveraging just AI in general and then we will talk about Genitive AI. Everything that's happening around AI right now is very different than kind of what we've seen in the past. And we're taking, we're using AI for, for what it's good for, which is summarizing data. And so that's a lot of what I want to go into today is how we're using it to kind of summarize incident data and allow folks to learn from and improve from that. Can you just, you know, talk about the role that AI plays in building incident response not only as we talked last time, but to remember that it's also about culture. It's also processes, not just the tools, but talk a bit about the role AI plays in building the whole incident response strategies and culture. Yeah. So to talk about the role AI plays in building incident response strategies, I think a lot of it depends on the size of the team you have. So I do see a lot of teams out there that are having incidents but do have very few engineers and along with that very few time and resources. And so I think AI can be leveraged at that point to help give them shoulders to stand on. Like they're still going to need an incident response strategy. They're still going to need a learning from incident strategy. How can you leverage AI to give you a leg up on creating some of that? Now, if you look at this incident response, can you talk about the process, the way it happens? Like incident, something happens and suddenly there is too much data and you're trying to figure out, you're going through the data. When and how, you know, chat GPD or AI comes in to help teams because there's too much information, you can overlook something or it's too much important to go through it. So talk about, I mean, how you folks are going to leverage chat GPD and AI. I'm really excited about that question swap because there is a lot of data and incidents, right? And part of Jelly's thesis is we think there's a lot to be learned and how we converse with each other during incidents, who we bring in, what we say, how long it takes us to do a particular thing, which to your point leads to a lot of data to sift through. And so I think what AI can do here is AI is very good at summarizing data. We can use AI to summarize the data of our conversations of how we talk to each other, of how the incident unfolded and not use that as a final answer, but use that as a means to get the person that's responding or investigating the incident curious to learn more. And so I think of it as a nice starting point. It's allowing you to get rid of the writer's block you might have or the overwhelm you might have looking at this incident data. I think a few weeks ago, we recorded a demo from your team and it was also how Jelly itself, you folks, you know, leverage doc pudding years. And it was more focused on kind of story building or narrative building that what happened, how it happened, and then, you know, what you can do about it. So can you just talk about, like, quickly, though, that the process is how after chat, how the teams will look at this incident response or even your own teams will look at it. If we look at incidents in two areas, right, we have the incident response, which is during the incident, and then we have the post incident, the incident analysis, which is afterwards. So we're rolling things out in both of those categories. And I think how they'll look at things differently with incident response, and that was the question you asked, right, how people will look at it differently. I think how they'll look at it differently with incident response is they can use it for long-winded incidents to help understand what's going on. We have a feature coming out called Catch Me Up, and that allows the user to see a summary of what's happened so far in the incident so that they can get caught up and get curious about certain things that are happening without having to pull people to the side, DM them, things like that. And then on the post incident side, the incident analysis side, we are also using a summary feature. And again, that kind of is meant to help get the investigator curious about what happened in the incident so that you can find out more things about what happened rather than kind of starting with a blank page or with a blank template. Can you talk about some of the key features of this announcement? Some of our favorite features about this announcement go hand-in-hand with our narrative builder. So our narrative builder is one of our best products. It allows you to create a story backwards from your incident. And now we're making that story even more powerful. So the way narrative builder works today is you pick markers. You pick a marker for detection, diagnosis, repair, and any key moments. And you do that through looking through the conversation data and dragging and dropping it into particular markers, which creates this nice visual for you. Now with AI, we're actually allowing you to do some of that automatically and have like a kickstart, so to say. So it's not a completed post-mortem for you, it's allowing you to figure out where to look, to figure out where detection was happening, where diagnosis was happening, and get really curious about it, like I was saying earlier. So that's one thing I'm really excited about, but we have lots more features to play with too. When we look at incident response in general and when we look at the whole cloud native word, I mean, we can talk about Kubernetes word, we do talk about a lot of cultural changes, we talk about practices, we talk about a lot of design, we talk about SREs, we talk about platform engineering, we talk about chaos engineering, we talk about observability. So talk about where do you see incident response with, and if you just look at C and C of landscape and they look at observability in general, they not only talk about logging, matrices, tracing, they now also add chaos engineering there. So talk about how you're seeing the whole field is evolving and the reason I'm asking is that when people build their whole cloud native or Kubernetes strategy, they should also think about incident response as a critical piece of their strategies. As folks are deciding their observability strategy or their cloud native strategy, I think one thing to keep in mind is Jelly is tech agnostic, so we can build on top of anything you're deciding there. But I think as you're making those decisions and as you're even developing those strategies, it goes hand in hand with developing an incident response strategy. Like there is a reason you are focusing on cloud native. There is a reason you're focusing on which tools you're using for observability. And that reason is because your company is scaling and you probably have customers and you probably have customers that care if your things are going down. And so along with developing these technical strategies, it is incredibly important to develop a incident response strategy which involves both tech and culture within your teams. And what I end up seeing a lot of companies do is invest in the former without taking time to invest in the latter. Because they kind of figure it will just happen as it unfolds. But talking about it beforehand, practicing it, having a particular strategy in mind, and one that goes with your tech stack, regardless of what it is, is incredibly important. Since we are talking about this kind of platform or tech agnostic, can you also talk about with the inclusion of chat, GPT, Genetic AI, how easy or how kind of seamless it will be for Jelly Solutions to integrate with their existing audio, how you integrate with Slack and the pipeline they have when something goes wrong, the processes they have in place. We try to make it incredibly easy to do. We meet you where you are today is what we say. And I do think of the tech industry as being a large spectrum of maturity on incident response and incident analysis. And depending on where you are, is depending on also how well your business is doing, right? You're investing in these strategies. You're investing in the time behind them because of what your business needs, because of what your customers expect, because of what's in your contracts. With all that in mind, it is incredibly seamless to integrate Jelly, and it will be with each new feature we add, regardless of if it's AI, regardless of if it's some other thing. And so this goes along with our whole ethos. And so it is very easy to meet folks where they are today. You can play around with it. You can play around with it in some of our Game Day incidents. You can play around with it in a real incident. And just using that catch up feature to figure out what's going on in the incident. I think a lot of what Jelly does is play with the socio-technical systems, play with the humans and the machines and how they're working together. And that doesn't deviate just because we're using AI now. We're taking that same philosophy and applying it towards how we integrated AI. And so we're really thinking about the responder when we developed this AI solution, which what's hard for the responder, it's hard to sift through all that data as you talked about earlier, Swap. And so that's what we're really making it easier to do with this. When you look at Genitive AI, when you look at GenGBD, do you think it's just a hype or you think, no, this is more than that. This is moving in production. This is not just another phase of technologies. What do you think of Genitive AI? I don't think it's just a hype or just another phase of technology. I think the excitement will dwindle down a little bit, but I do see a lot of power in this if we use it for the things we're good at. These machines are also just machines. They will hit a wall. They will have limitations. They will have limitations that human beings won't have. And so it's about how much we use it to our advantage, how much we use it to help us save time and work on other things that require our human brains. And so I don't think it's hype, but I do think the learning about the limitations of it will have to catch up to the excitement of it. And while we are still kind of celebrating and enjoying this announcement, and of course, there are a lot of things you cannot talk about, but you know you folks are working on. So I just want a little bit of kind of, not really just a little bit, but what are the, what next we can expect from Jelly? What are the things that you folks are working on that you can share at this moment? Yeah, I mean, I think you're going to see a lot in how we come up with incident patterns and how we help you learn from your incidents. You know, the more incidents, the more you're sharing with Jelly how these incidents are unfolding, the more we can tell you about areas to improve and areas to double down on. So that's just a little teaser, but very excited about that. Thank you so much for taking time out today and not only talk about this announcement, but also give us a wider picture of, you know, how incident response is kind of emerging and also the role of these generative AI technologies. Thanks for all those insights and as you said, there are a lot of things in the pipeline. So I'm looking forward to our next discussion. Thank you. Thank you so much for having me.