Inspired with Alexa von Tobel
00:13:24 · Cameron, Daniel First
[00:13:24] S3: So, Daniel, when we first met back in 2022, you had just signed your first contract. And fast forwarding to today, you now serve many of the largest manufacturers in the world. And Alexa and I and our team have had a front row to your growth. But in your words, what has surprised you about the journey so far as you've scaled? What has changed, and what have you learned? [00:13:45] S0: I think that one of the the biggest things that's been surprising to us is that when we started off, we thought quality was really for for one department, for the quality department, and that it was really about defense. You know, you release a product, you're suddenly, you know, attacked with all this warranty costs, and you're trying to, like, defend it away. But, you know, at Harley Davidson, for example, within the first couple of months of us working with them, they had 12 different departments on the platform. Everybody from marketing to CX to supply chain to the software engineering team and product strategy. And when when we picked at it, it turned out that people were using quality to learn more about what their customers wanted. And a quality issue is just a a situation where a customer's needs are a mismatch with what your product is. And so they were actually using this to to inform not just the messaging and not just the CX of the existing product, but actually to inform the next generation of products. So I think that was a very big shift that we saw.
00:13:45 · Daniel First, Alexa von Tobel
[00:13:45] S0: I think that one of the the biggest things that's been surprising to us is that when we started off, we thought quality was really for for one department, for the quality department, and that it was really about defense. You know, you release a product, you're suddenly, you know, attacked with all this warranty costs, and you're trying to, like, defend it away. But, you know, at Harley Davidson, for example, within the first couple of months of us working with them, they had 12 different departments on the platform. Everybody from marketing to CX to supply chain to the software engineering team and product strategy. And when when we picked at it, it turned out that people were using quality to learn more about what their customers wanted. And a quality issue is just a a situation where a customer's needs are a mismatch with what your product is. And so they were actually using this to to inform not just the messaging and not just the CX of the existing product, but actually to inform the next generation of products. So I think that was a very big shift that we saw. [00:14:43] S2: What else has surprised you? Just like being a CEO, you know, building this journey, what else has been harder than you expected? And then what's been easier than you expected?
00:14:43 · Alexa von Tobel, Daniel First
[00:14:43] S2: What else has surprised you? Just like being a CEO, you know, building this journey, what else has been harder than you expected? And then what's been easier than you expected? [00:14:54] S0: I think definitely what's been easier than expected is I think that when I started out, everybody said that there's no way that manufacturers would ever buy, you know, software. And I think that actually people are pretty rational and, you know, we're we're in a... We're an exciting time that people are willing to try out technologies. And if they actually work, then then they're pretty happy to scale it. I think that something that I didn't realize would be so difficult is that I think that Axion... We're actually a very different type of company from many other startups, that have been created over the last, you know, over the last twenty years. Axion really is about going from from data to, you know, detecting a signal in the data to trend to insight to root cause, you know, down to action, and how you can do that at scale as as these companies will solve tens of thousands of problems every year on their products. And I think that there actually aren't a lot of companies, that are trying to to drive automation in that that loop. And I think it's required us to build our organization in a very different way, than most organizations are built. [00:15:58] S2: I wanted an example or a story. Can you think of a story that stands out when you saw firsthand just how effective and impactful Axion has been for your customers?
00:15:58 · Alexa von Tobel, Daniel First, Cameron
[00:15:58] S2: I wanted an example or a story. Can you think of a story that stands out when you saw firsthand just how effective and impactful Axion has been for your customers? [00:16:08] S0: We actually had a medical device manufacturer that had surgery equipment. And the surgery equipment was malfunctioning during surgery. And I I would I would actually read reports about people losing lots of blood, when these, devices would malfunction. And the manufacturer, they had actually thought that they had solved the problem, but there were actually two distinct root causes that the AI picked up. And as a result, people were still being impacted by this. And so we were able to to flag that, and I think that that was an example where we, you know, very clearly impacted people's lives. [00:16:39] S2: By the way, that's incredible. What, like, an amazing practical example of AI playing offense. That's wonderful. [00:16:46] S3: Daniel, you mentioned, just now how your organization looks different based on what you've learned. Can you tell us a bit about what that means and then what the organization looks like?
00:16:46 · Cameron, Daniel First, Alexa von Tobel
[00:16:46] S3: Daniel, you mentioned, just now how your organization looks different based on what you've learned. Can you tell us a bit about what that means and then what the organization looks like? [00:16:56] S0: Yeah. I think that it looks different in a lot of ways from a traditional enterprise SaaS startup. So one of the ways is that, you know, again, we're trying to to automate... You know, if a company has to solve 10,000 problems every year, and maybe currently they're solving only a thousand of them, we're trying to automate going from data to trend to, you know, issue to root cause to insight to to insight to root cause to action. So to do this, we actually have, many, many, aerospace engineers, medical device engineers that both work at Axion and obviously at our clients. But what we'll do is we'll map out how these people think, how they think about scoping an issue. They'll actually go and, you know, they work, behind the scenes on the platform and try to scope issues or root cause them. And, and then our product team, they look at what steps these engineers are taking when they're root causing an issue. And then one by one, we, you know, automate the the least complex, most manual steps of those... Of that problem solving. But I actually see very few companies, that are actually built that way, but it's a key part about how we're built. [00:18:02] S2: Talk about the future. What what do you see ahead of Axion for the next twenty four months? What are you excited about right now, and how are you thinking about investing those dollars?
00:18:11 · Daniel First
[00:18:11] S0: One of the big ways that our customers have been pulling us is that, you know, today, a lot of the way that the platform is being used is, you know, after products are released, how do I learn about the issues as rapidly as possible so I can solve them? But there's actually many different functions that want to use axi... Axion for lots of different workflows, and people increasingly wanna pull the insights that are coming out of the platform earlier and earlier into the product development life cycle. And so a lot of the funding, that, you know, that were... Is gonna be used actually for creating more of a ecosystem of products all around these product issues. I'll give you an example. You know, SharkNinja, they released a, you know, an ice cream machine. And, there were a lot of, initially, there were... It was received very, very well, but then there were a lot of issues back in 2023 where suddenly people were were complaining about it. And when when they looked into it, it turned out that one of the biggest use cases for an at home ice cream machine was, protein ice cream. So fitness influencers were were talking about, you know, how to how to make high protein ice cream, but the machine wasn't made for that. It was really it was really, you know, not made to kind of take very, very thick protein powder. So on the one hand, that's a quality issue with existing model, but on the other hand, that obviously can inform the next generation of innovation. And so the product strategy team can then use that insight and in... And and they actually made, you know, a creamier machine that's actually dedicated for protein. So I think that really building out an ecosystem that can be used across the entire product life cycle is gonna be a huge part of our product going forward.