The Claude Code Moment for Factories? Cognyx + Oplit

Speaker A: Foreign. And we're live, as I like to say. Um, hi everybody. This is Michael Finnicaro. Um, very happy to be joined today by two amazing French founders. Um, Matthias, I won't even try to pronounce your last name. Uh, and Thibaut Wilhelm, um, of oplit and just, uh, want to say welcome, you guys. Thanks for joining us. Um, maybe, uh, Matthias, you can pronounce your name correctly so I don't mess it up and you can tell us what Cognix is doing.

Speaker B: Yeah. Matthias Berael Lazarus. So I'm the CEO and co founder of Cognix, which I define, um, as an AI engineering platform for hardware. So we're essentially building the cloud code for industrial products, if I'm putting things simply.

Speaker A: Awesome. And how about you, Thibaut with Appleton?

Speaker C: Hi, Fino. Very, uh, very happy to be here. So, yeah, um, I'm Thibault Willem. I'm the CEO and founder of oplit, which is an AI supply chain platform for industrial companies. Um, we maximize factory performance with, uh, agency, uh, supply chains. So basically our conviction is, uh, supply chain is full of data, super complex and super repetitive. And it's the perfect playground for AI, uh, to make your supply chain more performance, uh, with less people.

Speaker A: I was just talking to one of the big, the big four today about that specific subject. So we'll have plenty to say about that later. Um, I wanted to ask you guys, I always start this podcast with a question. Um, November 2022, the world changes with this crazy OpenAI thing. Ah, chatgpt. Some people are skeptical, some people are a bit bullish on it. What about you guys? Are you guys, like, super, this is going to change everything. You were like, whoa, elephants. When I ask for something that's really not what I want. How did you guys, uh, live that moment?

Speaker B: Well, I'll start if you want. For me, it was definitely, uh, a defining moment of my entrepreneurial life. So I thought, all right, this is going to be extremely big. I could sense this from the very beginning because I saw tech people around me were extremely enthusiastic. But the real aha moment came, I think, a year later when we started seeing the first use cases in assisted coding. So N23, early 24. So that was the early days of Copilot, helping you, uh, complete your code, essentially. And I thought, well, that sounds like a really powerful use case. And it seems that software engineering is greatly accelerating. And that was, uh, what actually inspired a vision for Cognix. So, so we thought, well, if that works for software engineering, well, why Wouldn't it work in some way for hardware engineering? So let's start building in that direction. So that's what happened for me two to three years ago.

Speaker C: And how about you sense, uh, align with you, uh, Matias, the haha moments clearly was uh, you know, the happening of uh, Cursor, uh, and Novabel, uh, you know, a couple of years ago now where we were like, okay, so it can really replace some people, it can really make some real work. Right? Uh, and that's when we were like, okay, we are maybe going to go from a tool for supply chain professionals to AI agents that will replace uh, some parts of the job.

Speaker A: And um, when, um, since you guys ah, so just talked about codes, how did it change the way, uh, you thought about code and the way you manage your developers? Right, because you're no longer managing just people, you're managing agents and coding agents and so forth. How did that change the way as a manager, as a founder, how did that change the way you manage a development organization?

Speaker B: Um, for us, I mean we've witnessed a, uh, dramatic increase in productivity as everyone else. So I think my rough estimate is that we ship probably 3 to 5x faster in code. Um, so that's uh, of course a dramatic increase. I think the impact on the company and how we build is that the bottleneck has shifted from pure tech, meaning writing code, which was essentially a manual job. Uh, and the bottleneck shifted to product thinking. So what do we build? How do we build it? Is it even a good idea to build it, uh, in the first place? Because once you have the power to build pretty much everything in a matter of minutes or hours, uh, then you can pretty much get carried away quite quick and build a product that makes no sense from a business standpoint. So you need to be very, especially

Speaker A: when cloud says that's a great idea.

Speaker B: Exactly, exactly. So you need to be extremely well thought around. Uh, how the product is actually making an impact. Forecast customers, is that a good idea? Does that move the needle for them? Um, and I think the bottleneck has shifted to this area.

Speaker A: How about for you, uh, Thibaut?

Speaker C: Yeah, on our side, um, I'll align with ah, Matthias. And also what's really changed for us it's two things. Uh, the first thing is what we are able to solve. So now in terms of supply chain and scheduling, you know, it's tough mathematical problems, uh, that you need to solve. Um, and in 2022 we're able to make, with operational research, we were able to solve simple problems. I'll say and now we can solve 10x, uh more difficult uh problems and we can optimize what we could not optimize before. Uh, and so yeah, the bottleneck changed to understanding the complexity of the problem we're trying to solve and then delivering it uh, is uh, now quite easy. And we you know, we can deliver in two months what we could deliver in two years, uh, you know, a couple of years ago. So that's a dramatic change. And internally also, so of course your velocity increases so you know, developer go faster and so on. Uh, and I think what we're still working on, everybody in the tech scene is working on is what is the right organization. Right? Uh, so who are good developers? Should I have only one very senior guy uh handling uh, 25 agents or can we still have some juniors? Uh what about product designers and product managers? Should we have still two categories or should we have only one job that both, that does both product design and product management. Um, so we've done uh, already some uh, you know, organizational, organizational changes uh, in our team. But uh, it's a working process. Uh and uh, I think uh, yeah, it's a fun, fun thing to, to to see evolve every day.

Speaker A: So um, in terms of the implication of AI and to the way you guys are building uplit and cogni, is it something a. Uh, I mean there was the original thing we already discussed where people doing copilots, where Matias you talked about how at the beginning people were doing copilots and then I think AI has seeped all the way through the stack, bound to even some vendors writing foundational models. I think Thibault you were talking about having two of those. So how is AI actually integrated into OPLID and Cognix respectively?

Speaker B: Um, so yeah, in our case we use both kind of AI. So as Thibault outlined, we solve some tough mathematical problems. Some of them are not solved with statistical AI. So you need good old school symbolic AI to solve some of these things. So optimization and the constraints and so on. So we use both, um, and I would say I'm less interested in uh, the technology than the outcome. So the outcome is shortening the time to market how fast you're able to design product and how fast you're able to optimize bills of materials, cost, design, uh, under constraints. And in order to do this you need to bake AI uh into certain areas of the product. So in our case, uh, it's a lot about how do we extract knowledge from um, uh, engineers and how do we turn this into Code or structures that um, the machine can understand and leverage. So that's where I think AI makes a lot of the difference in Cognix. Um, and it's less about just having a blank screen and a chat that you need to talk to. I think the Cognix interface from many aspects is actually um, quite traditional if I would say so. So you see your products, you see your boms, um, so you're able to interact with it. I mean at the end of the day engineers need to see what they're doing. So it needs a real interface. Uh, but under the hood there's a lot of AI. So that's how we think about it.

Speaker A: And um, Thibaut, when we talked you were saying you actually were using two foundational models.

Speaker C: Yeah. So actually on our side, um, I feel we want to replace at the end of the day the guy uh, who is using our tool. So it's AI can be interesting, you know, like for him to uh, retrieve some data, uh, build some dashboards, uh, and so on. But we did not push so much. Uh, on this uh, topic we have some uh, basic uh, retrieve information, uh, tell me this or this kind, um, of feeders. But at the end we really want to optimize how the world production is done. Uh, at the end of the day we want to optimize resources, we want to, to optimize the schedule. Um, and so for us it's really uh, deep tech. How to uh, put everything the schedulers have in their brain to optimize the factory. And so yeah, we have two foundational models. The first one is you know, optimization. So how to combine AI with operational research, which is uh, you know, a long lasting mathematical branch uh, of mathematics. Um, and also regarding uh, so the industry, uh, knowledge model. So how we capture rules that are in planners head in order to automate and make the agents do uh, the schedule by itself. So for me at the end, uh, my ideal product is no product at all, is only backend and just the factory running by itself. It's going to take some years, uh, of course, uh, but uh, at the end it's really how you capture rules, how you capture knowledge, uh, to run the factory.

Speaker A: So if I rephrase what you said, you really want to have supply chain as code.

Speaker C: Yeah. So yeah, actually like the planners, we uh, want them to write code instead of applying the same rules every day. We want them to define the system, how it should operate, what are the objectives, you know, should I uh, prefer uh, on time delivery or should I prefer the efficiency of my factory. So we want them to define the system and then at the end the system to run by itself. Um, so it's a different uh, uh, uh, shift in terms of how you think products, uh, production organization.

Speaker B: And by the way, pretty similar to what we discussed earlier. So the bottleneck is shifting to what is it that you human want to do? Do you want to optimize for your factory efficiency or your inventory level or you need to make a decision and

Speaker C: your ability to define it, uh, properly. Because like if I look at schedulers, uh, at our clients. So schedulers are usually very smart people. But some will be able to code the system and define and structure the knowledge model that will run the factory. Uh, and so they are the ones who will control the agents and some others will not have the skills to do it. So they will uh, probably uh, do other jobs within the company. Uh, because they are always people that very smart people with a lot of knowledge, uh, on how the factory works, uh, that you want to keep. Um, but they're just doing other jobs afterwards.

Speaker A: Um, that's uh, really cool. Actually it wasn't on the script, but I would just be interested because I found that um, both of you guys were created with um, a French venture company called OSS Ventures. And I find the thesis behind OSS really interesting that they look after problems to solve and then they find brilliant people like you guys to go out and solve them. It would be, I would just like to know the story. Like did John Philippe find you, uh, sitting in a bar or he already knew you guys. How did that actually work? Because obviously he found a problem in terms of engineering data and another problem in terms of supply chain and manufacturing. So how did that actually happen for you two guys?

Speaker C: Um, on my side. So yeah, it's a fun story. So, um, with Sufian, so my co founder, we both knew that we wanted to create a company. Uh, on my side I had uh, a few iterations. And uh, I was discussing with Ronald and he told me, okay, next week we work with uh, Sufian, we're gonna travel around in the factories. Do you want to come with us? And I was like, ah, yeah, maybe, uh, do you have something uh, better to do? I was like, no, you know, I just resigned from my job. So maybe the next two weeks I can uh, you know, just travel around. Uh, and I felt in love with the passion people have in manufacturing. Uh, they are really people that love um, their job, that are very uh, skilled. Um, and uh, and they are like really PRAGMATIC So, uh, I was like, okay, I want to. I'm really happy to work with those people. I knew, uh, manufacturing quite a lot because I was working in this area, but I was not sure I wanted to create a company here. But, uh, that's how I felt in love with manufacturing and regarding production organization. At the beginning, we really wanted to tackle something different, which was, ah, um, middleware. So how we integrate data from ah, erps. And uh, by chance I'll say some people told us, yeah, we want to integrate this data and this data to make this super complex scheduling, uh, product. And the fun thing is that when I was working for McKinsey, uh, I did, uh, several projects, uh, where I was the one building some crazy Excel files to optimize the scheduling of factories. So I was like, oh, this rings a bell. Uh, so this is why we, uh, were like, okay, let's go, uh, all in on, uh, how to organize factories and the world's production.

Speaker A: Uh, awesome story. Thank you. Uh, so that was like walking the factory floor. Matias. Same thing for you. Or you were watching an engineering organization and seeing the Excel spreadsheets rather than the PLM systems.

Speaker B: Yeah. So, uh, my story with OSS Ventures started, um, when I met Renault. So I actually, uh, reached out to him when listening to a podcast where I found that he, his thesis around manufacturing in the west was wrong. Really, really true. Uh, he was already on a mission to, um, help the west rebuild an industrial capacity. And I thought this was extremely important for, um, um, France and the west and the US in particular. So, um, I think the mission rung, uh, really true to me. Then I met with Renan. We, uh, bounced a few ideas and found that the biggest problem we could think of that um, was surfaced by manufacturers was around their engineering, design and development processes. How slow this was and how this was true completely across the board in automotive, aerospace, uh, consumer goods, um, mobility, you name it. They all were complaining about how slow it is to reconcile tons of documents that make no sense. Um, so that's how we started. We thought this was a really tough mission, but the problem was huge. And as an entrepreneur, you need to fall in love with the problem, not so much the solution.

Speaker A: Uh, that's a good one.

Speaker B: That's how we started building in that direction.

Speaker A: It's actually, uh, my friend Jason Casper in the chat is asking good question. He says, um, is AI making the digital thread more important or is exposing many to how many organizations never had one in the first place?

Speaker B: I think many organizations don't have one. Um, and now AI and new technologies, especially around databases, uh, knowledge graphs and so on, are indeed the capacity that will unlock this. So there's a great and very strong opportunity to be building that now.

Speaker C: Yeah, uh, for us, um, AI is a big opportunity to make industry uh, more competitive. That's uh, as simple as that. Ah, so it's just, uh, for me it's just continuous ah, improvement journey of digital and impact on manufacturing. Um, the real game changer is also like the ability to capture knowledge and navigate through it. Uh, so really replace a human and not uh, give tools to humans.

Speaker A: So let's stay there just for a second. Um, it's probably worrying for a lot of the younger people listening that don't have years of experience. They're like, okay, so these guys are after killing my job. So what kinds of things should the younger generation? Because the demographics of people watch my podcast, there's about 23 to 32% depending on the podcast of entry level people. Or maybe some of them are actually, you know, just graduating or grad school and they must feel a little scared when you say, well our job is basically to eliminate your job. So how, uh, what should they be doing so they're not on that list of easy to eliminate. Uh, don't bother hiring. Like uh, what, what are the skills they need and why should they come and work for Cognix and Applet instead of going to somewhere boring like Accenture or Microsoft?

Speaker C: So those are two different questions. But for the guys in supply chain, I think I will make uh, I mean OPLIT and the AI wave will make their job more interesting because they will really be able to extend the scope and the impact they can have, uh, uh, on a company and uh, on a factory. Before Pleats, the guys were just saying like okay, after apple pie, should I do strawberry pie and then banana pie and so on. So every day the same questions and so on. Know they can really control the factory and say, okay, how do I increase the throughput of my factory? How do I deliver on time my clients? So you really are focused on uh, you know, the, the output rather than really doing some constraints, uh, every day. So in that in a sense the job is like really like 10 times more interesting. So you just need to really understand the basics of supply chains, the basics of production and then be someone super structured. Uh, and it will uh, help you master your job for the next 20, 30 years. Because at the end you will always have some strategic decisions to make. Uh, it's always trade offs that you do some arbitrage, uh, between your clients, basically, and the competitivity and the productivity of your factory. Um, and why should people join us? Um, you should join us if you like to optimize stuff. Uh, for example, you know, the morning I go to work, uh, running, and I listen to a podcast in the same time, uh, or I do a call, uh, every time I'm thinking in my head, should I, you know, start my coffee now and do something else in the same time? So if you're crazy like that and like to optimize stuff, you should, uh, you should join.

Speaker A: I love it. How about for you, Matthias?

Speaker B: Yeah, I would have maybe a slightly different opinion on whether Cognix is going to replace engineers. I don't think this will be the case. Um, I think the engineers will definitely be a lot more productive in the sense that they'll process probably a lot faster and a lot more information, probably for a lot more products. Products might be a lot more complex, so they can handle more complexity with the right software tooling. Using, of course, a lot of AI. I think it's pretty similar to what happened maybe 30, 40 years ago for accountancy. So if you introduced excel to accountants 30 or 40 years ago, they might say, oh, okay, this is the end of my job. Reality is, 30, 40 years after, there have never been, uh, you know, there's still a lot of accountants and probably a lot more than 20, 30 years ago, because now finance has moved not to accounting, but to controlling and financial projections. And so you can handle a lot more work with the right tooling. So in that case, Excel, and of course, many of the SaaS and AI that came after this. So I think that's more the direction that I'm seeing, uh, using Cognix, uh, manufacturers will be able to handle a lot more complex products, uh, ship them a lot faster with a lot more consistency and a lot more reliably. And that's what I hope will make Ingenious Life a lot more interesting.

Speaker A: And just as we're talking about students, there's a student from HEC that just wrote to me, uh, Louis Colamichel, and he says, um, a question about reindustrialization in the west from Matthias and Thibaut. How can we attract the next generation of workers to this sector, especially in France? In other words, not all moving to California or Boston. Right. Um, industrial jobs have long been devalued because they're seen as dirty old jobs is a question that would probably require 10 episodes. But what advice do you Matthias and Thibaut have to policymakers, um, in order to keep the jobs here.

Speaker C: Um, yeah, so m. For me it's one of the reason why we created opit. Um, so opit, it's called, it's the contraction of operations lit. So the, the story behind that is to say, you know, manufacturing, industrial, uh, sector. It's cool, it's lit, you know. And we want to showcase to everybody that there are some fun problems to solve. There are some really concrete things, uh, that are being uh, built. Uh, and when you go to a chocolate factory or when, even when you go to a fasteners factory, it's really fun to uh, see. Uh, so this is really embodied in our company, uh, to say to people, look, manufacturing is very cool. Uh, you're gonna learn a lot of stuff, uh, and you can really be passionate about it for the next, uh, 30 or 40 years, uh, of your life. So this is what we try to scream to the world. But yeah, it's tough. Uh, so for example, I remember, so I did an engineering school school called Central in, in France. Uh, and uh, I think we were like 300, uh, uh, in my, um, in my division and maybe like five to ten people went uh, working for industrial companies. Uh, so we need to increase that number, uh, for sure. Maybe less consultants and more guys working for industrial companies. Uh, because at the end, uh, that's uh, that's what is uh, important for your country. Uh, you know, you need to, to have some sovereignty and some ability to uh, to build your own stuff. Right. Uh, otherwise you just consume what uh, are done by others and uh, and you're stuck.

Speaker A: So Matthias, what advice do you have to the policymakers to keep those jobs here?

Speaker C: That's tough.

Speaker B: Yeah.

Speaker A: So first of all, taxes, of course.

Speaker C: That's like number one, increase taxes.

Speaker A: Yeah.

Speaker B: Especially in France. Yes. Uh, well, I would say that uh, we have no choice but to re. Industrialize. I mean if you're looking at the geopolitics today, um, we have no choice, uh, we need to ramp up industrial, uh, jobs and industrial capacity in the west. Being able to make our own things, our own cars, our own weapons, our own everything. Because the reliability of the supply chain is being questioned every day as the world is getting more crazy. So being able to make industrial and design our own, um, products, uh, becomes pivotal for the company. Now I would advise, uh, probably policymakers to stop having, I would say, a romantic idea of re. Industrializing the West. So you might be thinking, oh, we'll have the small factories in our country size and this will be nice. We have workers, you know, uh, doing everything by hand. No, you have to get rid of that idea. This is not going to happen. Uh, the factory of the future will be extremely technical with heavy investment, heavy automation. Probably 10, 15, 20% of the cost of the factory will be into software to make sure that it's automatized enough and that you have a lot of leverage, uh, from software on the hardware being made. So I would definitely view, um, manufacturing as a heavy investment area. It's not coming back like 50 years ago.

Speaker A: Um, which leads me to the last question for this section and it's interesting because you mentioned the AI factor of the future. I remember last year all of Jensen's GTC GTC talks were about this AI factor of the future, that every physical factory will have an AI factory and every AI factory will have a nuclear power plant, preferably fusion, uh, rather than fission. And I think that you guys, the startups are all building like one brick at a time. You're building that AI factor of the future. Right? So at the same time we saw an OpenAI moment for programming, right, with cloud code and cursor. We saw the OpenAI moment in 2022. I'm not sure we've seen the OpenAI moment for supply chain and engineering or maybe you guys made it and I didn't see it. But how long do you think before we have that there's a before and after? Because clearly with OpenAI and with Cloud code there was a before and after and nothing will ever go backwards again. Right, so when do you think we're going to hit that in your respective areas?

Speaker C: I think on our side, I think the uh, it's going to come in in the next month. Uh, we really uh, at this uh, moment, uh, you know, for, for a supply chain. Really? Why? Because it's so it's problems that are full of data, uh, super repetitive, quite complex and for one problem you have one solution that is obviously better than the others. So it's really good AI playground. Uh, and uh, we see that a lot of startups uh, are trying to solve this. We're trying to solve this. Um, also the tech giants like uh, SAP, uh, and similar companies, uh, are working on agency planning. So a lot of uh, efforts are put uh, into this. Many people think it's going to happen uh, by uh, 2030. Uh, so yeah, this is 100% sure that by 2030 nobody uh, is gonna uh, plan using an Excel file, uh, in uh, advanced factories.

Speaker A: I'll take you as a bullish. Very, very bullish. Okay. And Matthias as bullish in terms of engineering or less so.

Speaker B: Exactly, super bullish. I mean, I think what you're seeing is that AI has not been productized yet for the engineering use case. So we are of course building very aggressively in that direction. And the question is, uh, how soon, uh, can we actually have the whole experience working? But in order to do the, like I said, the cloud code for industrial products, you need a lot of framework before uh, you can do this. So you need of course, for all engineers to work on a single source of truth, uh, which is not the case today because they're working in different documents. You need, um, engineering to be composable, so they need to build from existing modules so that humans and AI can understand what they're doing. You need knowledge to be executable, so it needs to be understandable by again, a human and a machine. And once you've done this, then you're able to do full agentic engineering. Um, but you need to have a lot of scaffolding before you're able to actually deliver that experience. Similar to code.

Speaker A: Code, I was about to say, I think that the real, the bottleneck is the fact that in both supply chain and engineering manufacturing, we're extremely deterministic, I mean, obsessively deterministic. And LLMs are unfortunately obsessively, uh, uh, probabilistic. And so that's the rub, right, is how close can we get to determinism with the guardrails, with all that kind of stuff. Um, so I wanted to finish because I know, uh, uh, Thibaut has a deadline. Um, I like to talk at the end about digital maturity in general. So, um, we've been talking about Excel a lot on this call and that's sort of the base of my question where I think about when you go to your customers, so you go to the customers of Cognix and questions of Oplit. I think of digital maturity sort of on a Spectrum M from 1 to 5. Like 1, they're still using teams to communicate and they're still using Excel for almost everything else. Okay, they bought windchill, it's 3x, but actually nobody uses it because, well, Excel and then on the five, you know, you're in completely agentic, adaptive, um, uh, autonomous digital twins. And basically nobody's at five, right. I mean there might be one division of SpaceX that might be there, but it's pretty much, uh, la la land still. So what is your perception when you meet the Customers, I bet. Because I can't ask a customer that a customer is going to say, hey, I can't say that. I'm going to look like an idiot. You guys, without naming the customer can say, well, I feel that they're closer to one. Closer to. What is your impression of where they sit on that spectrum?

Speaker C: I think there's a lot of, uh, heterogeneity. Uh, so I'm saying something that is very obvious. But, uh, clearly some companies are, uh, at two and some companies are at four.

Speaker A: Four? Really?

Speaker C: Yeah.

Speaker A: Some pretty.

Speaker C: I mean, if we want to say for me, four is, uh, I have structured all my data. My data makes proper sense.

Speaker A: Uh, I have a data organization with a CDO on top. Okay. Yeah.

Speaker C: And I can, uh, extract and use any data and share it. Right. And I'm starting to play with AI agents, uh, that have value. Right. So that will be for, for me. Um, and we see some companies that uh, are able to, uh, make it happen. Uh, usually it's more like big companies that a few years ago, uh, you know, like took a bet and said, okay, I'm going to have a proper data lake. Whether it's a data lake or it's structured within my erp. But I will have this one single source of truth. And I will have internal people that will be developers that will help some suppliers. I'm going to have a lot of tech, uh, suppliers. Uh, and so some companies took this bet and now I think they're quite, uh, advanced. And some companies, you know, it's uh, a, uh, complete mess. But to work with us, it's easier and you will capture way more gains, uh, if you start from three or four, uh, rather than if you start from a one or two. So we have some other products in the company for, you know, like if you have a lower digital maturity and you just want to execute, uh, easy stuff. But if you really want to have AI agents and AI brains that uh, you know, like schedules your factories, that pilots your factory, you need some proper structure, uh, to capture those gains. Uh, and you need the data and you need the knowledge, uh, the technology and the uh, supply chain and industrial knowledge.

Speaker A: Thank you, Tipa. Well, um, it's the first time I really, uh, said that there were so many fours. That's really, uh, Matthias, I'm just guessing here, but I'm going to guess that there's not as many fours in our area of PLM and engineering. Right?

Speaker B: Yeah, I mean, when we walk in, there's usually the same kind of maturity everywhere. So sitting between two and three, I would say. So they usually have a PLM with part of the information. They usually have a lot of shadow engineering on Excel, Um, and they usually have many standard operating procedure and many knowledge, uh, in PowerPoints and PDFs documenting how things should be managed. So that's the status when we come in. And what I can tell you is that every board right now has this conversation. So how fast can we embed AI into our most critical workflow? Because we need it for competitive reasons or for uh, efficiency reasons or for bottom line reasons. Um, so they're all having this conversation. I think at this moment. They've tried some things, so they've tried some, maybe proof of concept, uh, maybe with a few copilot here and there and realizing that this is not making the cut. So they need to be leaning uh, in a lot more heavily. And uh, probably that means investing into the right tech, but also investing in human transformation. Uh, so how do you change the people that are going to work with that technology? Because once you introduce such a powerful and transformative technology as AI, you need to rewire the processes and essentially the human brains around it. And that's a lot of work. So uh, in our case we're here to assist that. So we deploy uh, specialists along with our technology to make sure that this transformation can happen. Um, but that's not to be underestimated.

Speaker A: I appreciate that answer. In fact. So the next to last question, last question is just a goodbye question. The next question is, um, have you seen when people, uh, were at three or four, well, more like two or three, and they use Cognic, so they use op. Is there sort of an, has there ever been an aha moment like oh my God, if I, you know, if I broke the data silos, if I got the data out of the old files, if I got different departments talk to each other because they don't talk to each other. My God, what I can do all this great stuff and I can have better ROI and get product out there. Have you, have you seen that aha moment, uh, in your customer's eyes?

Speaker B: I love this question. I definitely did. Uh, and especially the customers that have this aha moment are usually hardware engineers that can also somehow code software. So they're software developers, maybe on weekends because they like to code code things and so on. And so when we show them for example, how we translate an engineering or manufacturing rule into code, so Python or Cel in our case, and how this can be applied to their model, subsequently they're like, oh, so this is like a test pipeline. A CI CD was like, yeah.

Speaker A: Ah.

Speaker B: Oh, that's amazing. So then I can accelerate because I know it will test. Yeah. Uh, and so that's the haha moment for them, uh, is to get the system thinking that is so powerful in software engineering that they can transpose to hardware.

Speaker A: Awesome. How about tiba? Have you seen that too?

Speaker C: Yeah, for the aa ah moment I feel is more for the managers, um, maybe our Persona. So schedulers are. They're geeky, but not as geeky maybe as uh, you know, like engineers, uh, cogni are working with. So they more see like, okay, it's going to be automated is a challenge. Or do you take this rule into account? And so on. So they're really more into like making the thing work. But, uh, uh, they do not. I do not feel this big moment. But once the manager, you, uh, know, like the supply chain director, the production director says, all right, but like the schedule has just updated by itself. Yeah, yeah, that's. That's the whole point. Right. So that's how it works then. Uh, it's a big, big, uh, uh, aha moment because, like, it's like a senior developer, uh, who understand. Okay, now I can control, uh, 10 junior developers and they are going to do exactly what I told them to do. Yes. Uh, oh, yeah. This is cool. Uh, so now I'm sure that my scheduler is going to. The schedule is going to be aligned with my strategy goals. Yes. Because you're going to be the one who will define the rules and the rules are going to be respected. Uh, so, yeah, the a moment from. For us comes more from the middle managers. Uh, I'll say.

Speaker A: That's a fantastic answer. Thank you. And I think both of those examples really point to my central thesis of some of these calls, which is that if you want to move the dial towards that five, waiting for SAP or the other big three to do it is probably not the right call. It's probably better to go to Matthias or Thibaut and use, uh, their much faster software. Um, I, uh, appreciate you guys being on here. Just before we go, I just wanted to, uh. We're gonna go on our summer break, but maybe in the fall. Are you guys going to be at some trade shows? Maybe. I just saw the first invite from, uh, Adopt AI and I think you guys were there last year. Um, what's up? So how folks that are coming to Paris or maybe you guys are traveling internationally, where can they meet you and see, uh, your solutions and demo. Besides online, of course they can come

Speaker C: uh, to ah, our office, so which is based in Paris. We're going to uh, open office uh, in the US Uh very soon. So I'll be happy to, to uh, uh, to meet anyone, uh, physically. Uh, and yeah, we, we cover a lot of trade shows, especially aerospace, luxury and, and batch process. So uh, if you're on a trade show for this type of, uh, of subject, uh, you just can, can join us and we organize a trade show for a space, uh, in uh, in September in Bordeaux in France.

Speaker A: Uh, so we are, uh, the guest speakers.

Speaker C: Um, uh, yeah, but so it's really focused on aerospace. And so the. So we're working with the gfas, which is a, uh, uh, industry so consortium. Uh, and so it's Opulet and the gfas, uh, working together on uh, digitalization for aerospace. So it's 22nd of September.

Speaker A: Awesome. How about you, Matthias? Where can we meet you? In uh, Cognix?

Speaker B: Yeah. So far I haven't been a great party guest in those conferences. I must admit. I've been very busy at clients on factory floors, as Thibaut said, uh, making sure that whatever we build is actually used on the ground. So that was most of my time for the last months and years. Uh, but I will be at Adopt AI and we will also open in the US Uh, I would say in the coming months. Um, so you will definitely see us um, in Paris or probably East Coast.

Speaker A: Awesome. Well, um, in the interest of finishing exactly on time, I wanted to say thank you very much to both of you. Uh, of course there'll be another credit conference and you guys will be obviously privileged guests if you wish to attend. Uh, to everybody else, there'll actually be another podcast with Techsoft 3D in about an hour from now. Uh, thank you very much. We'll be back, I think. Not sure going to get any more podcasts then before September. We'll come back, um, in the fall. But, uh, thanks everybody and a big thanks to Thibaut and Matthias for taking your time today. Thank you very much guys.

Speaker B: Thanks a lot. I had a blast.

Speaker C: Yeah, it was a pleasure. Thanks a lot, Finault. And have a great, uh, holidays.

Speaker A: Cheers.

Speaker C: Cheers.