 Hello. My name is Veli Pekka Julkunen. I'm one of the co-founders of VisualMind and our mission is to make image recognition technology accessible for everyone. Before going to the basic stuff that we have the problem and solution, I would like to tell a little bit about Greg. Greg is my friend and as you can see Greg is a quite snappy guy. He has a nice mustache and a quite nice outfit. And one could say that Greg is quite handsome or easy. This was exactly the question we wanted to get answered to and what we did was we have a quite flexible artificial intelligence model that learns from humans. So we got experts in handsomeness to teach our algorithm. And yes, the computer says that Greg is quite handsome. We teach also many other things for our algorithm. For example, emotions. This is basic stuff. You can see this calm, aggressive, a little bit aggressive, but mostly relaxed and calm. You can see that we can identify style. Greg is hipster type of. There is a little bit rock and also overall impression. So for example, Greg seems to be quite innovative, creative, but there is a little bit arrogance in Greg's overall impression. We can analyze this from videos also. So we can see that how Greg's arrogance develops in certain videos, for example. We can predict preferences. So based on our experts who teach Greg or artificial intelligence algorithm, we can say that, okay, Greg probably likes beer, especially smoothies and things like that. We don't need to focus on persons alone. So we can, for example, we have the algorithm to learn how professional photographers evaluate photographs. So we can analyze whole photograph as from the quality perspective. This is just an examples that what is possible today. And the foreigners are already doing this. For example, Zalando is one interesting company and they have 16% data science team. So my argument or the fact is that companies are sitting on top of a huge gold mine and the real problem is that it's too expensive for them. So here we come in. So our platform, we can deliver in best cases in a couple of hours or within a one day a tailored full solution. Thank you. Fast conclusion. So we have experienced team. We have happy customers. We have patent pending. Let's scale this together. And now for the Q&A. Thank you. All right. Thanks, Veli Pekka. Yeah, a great pitch and a bit similar question what we have before. Since there is a lot of image recognition software available, a lot of computer series has been popping up, what is sort of a unique advantage in the long run? Is it data? Is it your team? At this point, the flexibility is our strength. No one is at the moment providing the same flexibility and how fast we can develop or deploy the whole system. In the long run, we want to be kind of a one-stop shop for all image recognition. Meaning that we have our own models, but we can integrate any third-party model. So you can get everything from one shop basically. I think that's one of the key strengths in the long run because Google is not going to offer Microsofts or Amazons algorithm through them. So I think that's one key point. Hey, thanks, Veli Pekka. So who are your most important customer segments and use cases? Use cases at the moment, they are probably from the marketing technology companies at the moment. For example, we have a model that can assess that how people perceive certain brand images. So we have, for example, tomorrow we are going to announce a partnership with JC Deco, which is the outdoor space advertising company that they are going to use our system to analyze all the images that guns through them and they can enrich their database. So at the moment that is our most important customer segment. Thank you. We have 10 seconds. Yeah, I think that's enough. Thanks. Thank you. And now I would like to welcome Clark AI.