00:02:41 · Matthew Fitzpatrick
[00:02:41] S0: Yeah. So we're a modular AI software platform. We do two things. We train all the large language models for... Via reinforced learning. So think of that as effectively any topic of world, anything anything you... Any model you might build, we'll actually bring human expertise to then evaluate if that output is good. And then on the enterprise side, we we serve 10 different sectors, but I see the the most active ones being food and beverage, health care, asset management, and consumer retail. And we use kind of... And we use the same suite of of of platforms across both. So we have Neuron, which is data platform that brings together structured and unstructured data, Atomic, which is a process builder that can build out any operational process. AppsON, which is an agentic platform that can orchestrate agents. And then Meridian, which is our expert marketplace, and that's... We have 1,100,000 experts at one time. That's what we use to do a lot of the reinforced learning training. So we use that suite of four platforms, the enterprise, to build effectively hyper personalized software using that common suite of platforms. And so everything from, like, inventory management to pricing, we're using those same suite of platforms to deliver. And I was gonna say... And we wrap it all with four deployed engineering as kind of the core delivery motion. So think of that as we use a lot of the really impressive kind of innovative Gen AI coding tools to actually deliver a lot of this much faster in that era.
00:04:04 · Guillaume Moubeche, Matthew Fitzpatrick
[00:04:04] S1: And can you share, like, a specific use case of something that that has been developed inside one of these industries? [00:04:11] S0: Yeah. Sure. So I think one example I can cite recently is SwissGear. So SwissGear is an example. Know, one of the challenges that they had was inventory forecasting on a six month lead time is quite challenging. And actually how... Knowing how you should forecast inventory. And, obviously, that has huge implications both on the amount of inventory you have to carry if you don't sell it, or if you don't carry enough inventory, you miss out on revenue as a result. And so, you know, we we map together about a 100... 750 different data tables very, very rapidly to do that kind of forecasting transformation, and the tool we built actually doubled the number of SKUs that they could forecast effectively. Just as one example. [00:04:52] S1: Wow. That's nice. And do you like... What's what's the business model like? Like, do you build all these kind of, like, tools and models inside the company, and then you have, like, a fee to maintain it, or is it, like, a one off?
00:04:52 · Guillaume Moubeche, Matthew Fitzpatrick
[00:04:52] S1: Wow. That's nice. And do you like... What's what's the business model like? Like, do you build all these kind of, like, tools and models inside the company, and then you have, like, a fee to maintain it, or is it, like, a one off? [00:05:06] S0: Everything is a managed platform. So, yeah, we can either host or or deploy as a container in the company's environment, and there's no big upfront services fee. What we're doing is deploying something on an ongoing basis that works. And I think that's actually a really important part of this. The the model of, like, charge an enormous amount upfront and then hand it over has really not worked in the general world partly because just have... Like, I'll give an example. Let's say you're a engineering construction player and you build a model that should handle permitting, but then permit... Permitting regulations all change in three months. Like, you actually need a way to an ongoing basis to make sure the model is right and maintained. And so, yeah, our... You know, if I take the the SwissGear example, we're using our data platform to bring the data together, then we're using Axon or agented platform to actually build out a lot of logic, and then that is maintained in an ongoing basis. And so it's it's a managed platform. It's a structure. [00:05:58] S1: Nice. And the the business model, would it be close to, like, SaaS? So it's like a a monthly fee or yearly fee. Or
00:05:58 · Guillaume Moubeche, Matthew Fitzpatrick
[00:05:58] S1: Nice. And the the business model, would it be close to, like, SaaS? So it's like a a monthly fee or yearly fee. Or [00:06:07] S0: know, SaaS is a is a controversial topic at the moment. What I would say is we we have more different pricing models than than we do have some SaaS pricing. So, generally, I would think of our pricing on the enterprise side as a mix of three components, a platform fee, some often kind of consumption based fee. So that can be things like price per call in a contact center, for example. So it's tied to the variabilized usage. And then the third is actually tied to outcomes. So we do tie quite a bit of our fee structures to shared outcomes with our customers. [00:06:39] S1: Okay. Super interesting. I think [00:06:40] S0: it's important. I think that in a world where you just have a SaaS price per seat, it's it's actually pretty hard to to make the ROI clear to folks. So it's actually been, I think, a core part of what we do. [00:06:51] S1: Nice. And, you know, we we just touched base a little bit about SaaS and and evaluation and the the kind of, you know, like a lot of people, and I think it's probably helped the industry think, you know, we're in a AI bubble with valuation, with the capital concentration. I'm curious to hear, like, your take on on that.
00:07:13 · Matthew Fitzpatrick
[00:07:13] S0: Yeah. I I think the the interesting thing about the AI funding market is it's something like three companies are 30% of the total funding, and then it's about a 100 companies are about 70% of the total funding. So you could argue... You you can debate whether it's about whether or not. I think there's all sorts of different questions about where else one would put money to get... Generate better returns. But I think the main thing I would highlight is it is a a focus on a small number of companies where there is scarcity. Meaning, there's a lot more investors that wanna put money into those 100 companies than is available space. And so I think that's the interesting dynamic of the current AI era is there's not a thousand companies doing this. It's a small number. It's where a lot of the market wants to invest, and so you have a scarcity dynamic. And that's what's causing, you know, whatever valuations we have now. The other thing I'd highlight, though, is it's not a question of... This era of companies is not like, you know, people bring up the .com era. It's not a bunch of eyeball companies. I mean, these companies have hundreds of millions, if not billions of dollars of revenue at a much faster pace than we've really ever seen scaling loss demonstrate before. I think it was like... The number I saw recently, for example, is like this class of... The most recent class of Y Combinator from a year ago has the highest aggregate revenue ever for its cohort a year in. And so you you actually have, like, several different factors at work. You have scarcity in not that many companies that people can invest in, really unique scaling laws from a revenue standpoint in the whole space. And then I do think the macro is... Makes this whole thing complicated as well, which is there are not that many areas right now where the returns are good. You know, areas like real estate that have traditionally been really reliable have been more complicated. And so you have a lot of capital trying to move into this space. And so you can debate what the right fair valuation is, but I think that's the fairly logical set of reasons why we are who we are.
00:09:06 · Guillaume Moubeche
[00:09:06] S1: And from your perspective, because you mentioned the the .com bubble, like, what do you think is gonna be the the impact of AI versus, like, what we had with with the Internet?