00:25:15 · Guillaume Moubeche, Matthew Fitzpatrick
[00:25:15] S1: And do you believe, like, customization can be done by AI down the line? Meaning that you would have, like, software that kind of, like, fork themselves into a unique version for each person using it? Or do you feel like this is a... This... There must be, like, a human working and a human in the loop to actually create what fits more of a group of people rather than an individual? [00:25:42] S0: I do think there will be variants of that that will that will be AI enabled for sure. Like, I think that the reality is if I'm using a piece of software every day and it's able to see, like, my usage patterns and my flows, then it should adapt to what I'm doing. I think the the challenge is... And people have asked, can that happen today? You have a bit of a kind of a threshold that has to be crossed first, which is you need to get people using the technology first, and and you need to get adoption. And so what I'd say to you, you take an example, let's say there are a 100 people at a company, and you wanna develop a piece of software for them. Even if AI could observe a lot of it, the the process of bringing those people along and getting their feedback and the kind of process of of building something in the way they want it is is incredibly important for adoption. And so I think there still has to be a very material human element to get people excited about using much of this new tech. But I do think down the line, more of it, you will be able to have AI kind of evolve evolve the software dynamically.
00:26:41 · Guillaume Moubeche
[00:26:41] S1: And right now in the in the way you work, because I think at some point you mentioned that if you let your IT department, you know, like, have the the control over AI in in building, you know, like this prototype, Basically, everything gets stuck in pilots. So how, you know, like, would you go about building like an AI project within a company? Like, who should be involved? Like, how how should that be structured? Like, and how do you, like, build everything you've done, you know, so far?
00:27:16 · Matthew Fitzpatrick
[00:27:16] S0: Yeah. So the the MIT study I mentioned earlier that talks about, you know, 5% of project making... AI projects making into production, they they cite in there that the... They look at the companies that make... That bring AI all the way to production less consistently. And one of the main characteristics they cite is line ownership of AI issues. And alongside that, very clearly defined KPIs of performance tied to that. And so, you know, what I what I say about that is it makes a lot of sense. Right? Like, if if you took any... Like, take commercial credit. Let's say you wanted to change the way you do credit underwriting in a bank. You do need the person who's responsible for the underwriting process. You need the teams to actually say, okay. I would move forward with this technology. And so the process by which you get a team mobilized around using technology defines the success criteria, decide... Define the measurement criteria, you do need line ownership of that. And so I think that the organizations I've seen that are doing this really well have a mix... Have a kind of a cross functional Agile pod. Very different... Very similar to actually how, like, Agile worked, you know, eight, ten years ago in the early days of digital, but you want a technology representative, kind of a line owner, usually somebody from, like, the legal department. You want, like, three to four different cross functional owners who can tell you what are things you can do, you can't do. Legal is important to have in there, right, to understand, like, you don't wanna build something that has a ton of regulatory risk or compliance risk. And so having those folks all involved from the beginning, but most importantly in that is a line owner who's gonna lead it day to day as well as somebody from the operations team to make sure that the adoption is is clearly measured.
00:29:02 · Guillaume Moubeche
[00:29:02] S1: Okay. Really interesting. And, you know, like, whenever you were talking about the implementation for the the the e commerce websites of whether or not you wanted to... I mean, how you could predict actually, like, stocks. And and I was I was wondering, like, you leverage their existing data to actually build predictive model. But to do this, do you take, like, your own model or do you also leverage, you know, existing public model or open source model that are basically, like, on the shelf?
00:29:41 · Matthew Fitzpatrick
[00:29:41] S0: We use all we use all of the kind of exist... Main models. So the way I would think about we're we're doing there is data does tend to be the biggest impediment, particularly in in large enterprises, to building anything with AI. So, you know, let's say you're a business and you've got four ERP systems, a CRM system, an ecommerce platform. You've got five, six, seven, eight different systems that don't all reconcile each other. So you wanna look at, like, the definition of a customer. It's... You have overlap or kind of components of that that don't tie across the various systems. A lot of what our tech does, Neuron brings together data from those different systems until we call ontologies or data objects. And then from that, you can use some combination of machine learning, one of the LMs, etcetera, to actually build out the models. So I I would say we use all the available tech. We we do... You know, we leverage every model out there. We will leverage a lot of tools out there, like Databricks, Snowflake, etcetera. So a lot of what we're delivering is a working AI system that starts with data, builds out a workflow, uses various AI models, etcetera, to actually kind of do automation and and predict outcomes. And then we also have kind of quality assurance and testing on the back end of that, which is equally important. So the observability and telemetry to say the model's working. Here's where errors arise. And that's Synapse is the mod... Is the is the platform we use to do that, which is another commodity of our text
00:31:11 · Guillaume Moubeche
[00:31:11] S1: there. And the the reason I'm asking this is when you see how models are evolving, there is a... You know, like, if you look at the story of of LLMs and also, like, if you look at some companies, if you take... Not sure if you've heard of them, Jasper, they grew extremely fast at some point, you know, they were like an OpenAI rapper. And what they were doing is like a lot of prompt engineering on top, you know, of of this LLM that at the time, you know, was was not that great, but with all the prompting could give, like, results that people had never seen before and that this distraction. However, as the models, you know, got better, basically, what they were building started to kind of shrink because the model could actually produce it. And where I'm saying, you know, the most value is whenever you can produce, you know, like a software or whatever model that whenever there's gonna be an update in the original model, you actually get a better, like, experience, you know, for for the customers. So from your perspective, how exactly do you create this kind of of experience for the customers so that whenever there is an update, you make sure that everything you've built is actually getting better and not kind of worse. You know what I mean?