Tend to find what excites us the most. And if something's not. Yeah. Especially if a customer said, we've tried to do this, we can't do this. Or the ones that aren't. The ones that aren't challenging generally aren't exciting. Yeah. Quite Yeah, it's not very much fun and just putting some numbers into the box and putting go on the server. I think that I think that that partly speaks to the difference between us as an engineering service as opposed to an analysis service. And we typically try and avoid the role of just cranking out analysis, turning the handle data into one hand or data out. This is trying to provide a full engineering service so that we can say, Well, we looked at your design and we think this we got to do this analysis. Even though we said we might do this, we think this is better. Having run the analysis, we now think you should do this, this, that, etc.. And it's I think that's what keeps it interesting and challenging is actually trying to sort of go not necessarily above and beyond, but but deliver more to the customer. And the thing that I always bang on about in the office is data versus information. You can have a lot of data, but not always information. And the customer generally wants information, not data. So if you if you're just turning the handle on a project, getting results out, actually, what does it mean? What does it mean for the customer? Was it made for what they should do next? What's it mean for where they've been? What does it mean for what they're doing now? And I think that's that's generally why the challenging and the exciting, the kind of the saying. I So yeah, and I think, you know, we try and give the customer the engineering insight rather than some numbers that we've pulled off a survey of what we. Think the customer's in a way, while some of them seem to almost use this as like a brain trust in a way because they know that we're quite capable individuals and probably some of those over the years have a lot of experience within the team. It's just the depth and breadth of knowledge within the team is always worth testing itself and seeing can we solve the problems that they have? That's the great advantage of consultancy, isn't it, That you have an experienced team that you can call on and when you need them and you turn them off and there's no expense to your business when you don't need them? Because ultimately a lot of the time our customers are coming to us because something is challenging and the, the, the more straightforward run of the mill projects they might keep in-house or something like that, they might have the right member of staff staff that can do it. But they come to us because something's challenging, because they don't necessarily have the expertise. And going back to to what you said, I always and what Laurence said is we see a lot of the same people popping up at different companies say, so someone might be at an automotive company for one year and they come to us and we we do a lot of work with them. And then two years down the line we might be sitting there thinking, haven't heard from so-and-so for a while, and then they pop up a marine company or America's Cup Boat team or something like that. So because we are quite adept at dealing with some of those really tough, challenging engineering problems and we've got a good relationship with with all of our sort past and present customers, we do find that those challenging problems come up from the same people in different places. Quite often. There's a lot of talk about AI and its use in, in simulation, and there are people developing structural solvers and fluid solvers that are entirely based on on AI and sort of large data models and that sort of thing. But ultimately everything comes down to the laws of physics, the engineering first principles. So there might be many ways to to get to an answer. I think with everything, everything is generally going to go AI and to some degree in the future. And it's just managing that that quality assurance thing again and making sure that when you when you put a question into the big black box, then how do you know that what you're getting out is a good answer is definitely is definitely going to happen, is definitely going to it is definitely going to be a thing. And if it works and it's good, it's definitely going to improve computational time and reduce computational requirements and potentially improve delivery to customers, that sort of thing. But it's still very from our point of view, not being heavily involved with it. I suspect it's is still very immature and it's sort of life in its application and but it's going to happen at some point. I think it's going to kind of enhance that digital twin, which has been a bit of a buzz word for a few years now. And I think really what is probably going to offer and if game changing is that kind of long term product solution. So your A.I. might be able to be continuously updating the models that it was run on. And so you've got the validation cases that you've made when you built the model in the first place. 20 years down the line, you're not necessarily seeing the same conditions. And in the background the eye can be learning from the real world case data that's coming in and rerunning those simulations and just highlighting any potential areas of concerns before they crop up as a major issue or a major defect. You know, as Frank was saying, that we are still going to have quite a lot of human involvement in this process. And we see this when we use optimization tools nowadays. You know, the numerically driven that there's still a degree of a use a senior analyst looking at a result and saying, well, does this match the customer's requirements, i.e. from a manufacturing perspective? Or you might look at certain reinforcing material load fastenings and think, well, that's just not feasible in the real world. And so I can see that there being quite significant human involvement even when these tools start to get involved, you know, often think that when we look at similar projects in similar industries, you often have, I call them back pocket solutions. You think of seen as a problem. Now got something back online I can plug in there to fix that and I can see how jumping in on that early on and recognizing patterns quicker than we can. So there's a bit of evenflo's. And now with using these tools. Another tool in the arsenal really, I suppose I might be curious to see how it can be applied to the more routine or even mundane aspects of the job, which just like meshing. If you can train an AI to mesh properly, that would save time and a lot of cost of the customer as well. So it's the benefit for both parties ultimately. So it'll be interesting to see how that pans out with time. It's not like we haven't been using machine learning in some form or another for, you know, at least a decade in terms of design of experiments, exploration, that kind of thing. It's a new buzz word, obviously. I know, but the underlying technology behind it has been in place, certainly within our group for quite a long time. suppose VR is a is a is an interesting topic. I know it's becoming quite useful in larger design and card elements where people can get together and have a look at something together. And you know, I can see that being the same thing when you're working on especially bigger, more complex problems, especially if you're reviewing something with a customer because we've all been there with with especially larger projects that if you've got tight deadlines, then obviously, you know, you have to be thorough, rigorous through looking at analysis results. But if you had something that, you know, you can walk around, you can move around an object and bring other people into that discussion quite easily, that, you know, you might be able to visualize problems. It's a structure and things more easily. I can see that becoming, you know, more or more of a tool in our arsenal later on. Or easier to see your mismatches in VR than Yeah. Yeah. It would save us a good chunk of our time is on report and in post processing resource. That is to be able to just give a customer a file that they can actually just walk around and let's save them a lot of hassle. And trying to explain to us the, the consumer time behind them about different parts. Life on a video call it isn't it sort of cuts that out in the sense that it's more useful to them if they can just walk around the simulation and with our help service against another tool. And about