The Exchanges

Q the causal ML work background that you guys have. So can, can you maybe now show the forks? So there's kind of like the way that a lot of companies does it, which is like, Hey, you, you build the context. Now you ask the LLM to figure out why this was happening. What are, like, some of the things that you're doing differently, um, that you can talk about?

A So I'll note a few things I'll let Roz also, I guess. Uh, there's Building the context itself is where a huge amount of the, the, the pain is, right? Because you're dealing with, let's say petabytes of data, like let's say a billion possible symptoms, and you have to get it down to a small number of symptoms. And that entire, so it's this like massive data processing pipeline where you're reducing, we're getting more and more relevant context over time. And there's LLMs throughout that process. And each of those LLMs, there's basically AI agents throughout the process helping to, to, um, winnow down the The relevant amount of information and the way it's windowing down that information is by running these kinds of statistical tests that we have built in a proprietary way, because most of the data you're looking at is like time series data. And these LLMs are really bad at, at processing time series data, right? And that's really where like good statistics comes in. So those are basically like statistical tests of the toolkit that the AI agent has access to. And like, almost like a data scientist is deciding, you know, uh, dynamically what is the next best Collection of statistical tests to run to filter down the search space and also use at the same time, the semantic information associated with the logs, the metrics, the traces, and so on and so forth. It's like a, it's like a…

AI assessment note: “the way it's windowing down that information is by running these kinds of statistical tests”

Q it's like, what do you think is really unique about the approach you took that really resonated with people, especially when you don't really have a background that is like in this space, right? Like, I think like, you know, in a black box, you would imagine, okay, the founder of Traversal is probably somebody who ran DevOps or something at one of these big clouds or something like that.

A Yeah, I think it was two things. As Sean said, like fundamentally, this is a space of show not tell. And I think, um, we just had good references of, of customers actually using the product at scale and dealing with complex incidents. I think that the, the long term, uh, technical edge will be when you deal with complex incidents. And I'd say most of the companies that have been in the space definitely put a product out that works and people are using it, um, All the time. And also their use case has typically been like smaller, easy runbook automation for alerts versus like actually dealing with incidents where no one has any answer. And so I think the biggest thing was just showing and having, you know, being able to point to customers that are actually using it. And so I think that's probably the number one thing. And then I think the second part of it is explaining why this is such a hard AI problem, where it's not just like, oh, let's throw a chat GPT wrapper around your telemetry and, and something magical happens and why that's not possible. And so I think explaining that really carefully, and we can talk about what those key AI challenges are. So I think, I think it was a mix of those two things, which is the, you know, actually having it work. And I don't think we've seen any other company in our space having something actually work at production. And then second, a ve…

AI assessment note: “I think it was two things. As Sean said, like fundamentally, this is a space”

Q said, I used the century MCP, right? But century has a century seer product, but I don't pay for it because I use the MCP for free. How do the data sources, like, are they your friends? Like, are they happy that you're building this? You know, like, will the, does, you know, does Elastic want to build something here? Like what are like the competitive dynamics in the market?

A Yeah, it's a good question. I think at least what we have seen is that as you start hitting, you know, even midsize and large organizations, it's incredibly fragmented. Like they're using like seven or eight different tools. They're using, I mean, like one company worked with using Datadog, ThousandEyes, Splunk, Dynatrace, Datadog. ServiceNow. And I think, you know, and if you want to troubleshoot, you have to kind of traverse through all of these different systems, pun intended, right? And so that's the, and that's really the pain, right? Is, is the fragmentation of the system and fragmentation of teams. Now, the problem is that all of these different companies, the typically the way they work is, is they price based on the amount of data they're storing, right? And so, you know, they have very little incentive for, for company A To provide you any insight on data being stored on, on company, observability company B, right? Because that's just not, it doesn't fit with your pricing model as it stands. And so as, but the problem is to troubleshoot well, you have to go talk to both of them in an iterative way almost. And that's why I think there's a chance for a company, a new, a new company such as ourselves to, um, to have value because we're not trying to sell you data storage. We're trying to sell you an outcome, which is like the investigation itself. And we're, Reasonally i…

AI assessment note: “they have very little incentive for, for company A To provide you any insight”

Q And so when people are creating this, are they being notified of the issue somewhere else and then they come to you or are you automatically? Pre-filling this so that they can start the investigation.

A Typically I'd say it auto triggers off an incident channel or an alert channel. And so that's typically the flow that happens. Sometimes people will come to our UI for, well, two reasons. One is If you want to do a deep dive into an investigation, because on Slack, you just get a small piece of information, basically like the leads. If you want to do a deep dive as to why we believe that is the answer, giving all of the associated evidence, doing follow-ups, all that kind of stuff can happen on our UI. The reason people would come to this page is a few reasons. One is sometimes they may not be an incident, but they just kind of want to get a sense of what's going on with their system. So sometimes on, you know, let's say people will go to a, Like Grafana or Datadog or something, even when something isn't wrong to just get like a health check of like, is everything okay? Is everything cool? And so people kind of do that too. It's like, oh, this, you know, this part of my systems feels kind of slow. Can you just figure out what's going on for this, like this general health check type thing? So I'd say that's a typical use case for an But coming on the UI and beginning a manual investigation.

AI assessment note: “Typically I'd say it auto triggers off an incident channel or an alert channel.”