00:01:12 · Matthew Fitzpatrick
[00:01:12] S0: Yeah. Look. I think, first and foremost, I think I was just very excited about Invisible. I think the company had some really unique expertise and experience, having trained all the large language models for the Frontier Labs and the large language model builders. I thought that was a... You know, the the paradigm in the market today, back then, Elise, of how do you ensure that a kind of a... An AI model is working in production in an enterprise context? Was... There was not really a... And there's still, to some degree, not a perfectly well understood paradigm for how to do that. And I thought Invisible had really unique experience in how to do that. I thought the the kind of caliber of the team that had been built around that as well was exceptional. I think those are the two things really attracted me to it. And then I also think there was a unique point in the market where, you know, I'd spent a a decade, twelve years at McKinsey at that point, and I I did feel like I wanted to enter a world where I could raise capital to pursue the ideas I wanted to pursue. And I also think, you know, Invisible is a platform software company where where we've... You know, the work I did at McKinsey and at QuantumBlack, folks around four deployed engineering, was something I love doing, but I also am a believer that the next iteration and generation of this will be anchored in kind of having a coherent set of platforms that allow you to get AI working in production, and that was really what I excited to build at Invisible.
00:02:33 · Guillaume Moubeche
[00:02:33] S1: If you had to pitch, like, for people who don't know Invisible, like, if you had to pitch it to a a two year old, what what would that sound like?
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.