 Okei, hello everyone, my name is Tuomas Rittala from Selco Technologies and we are going to save fortunes by bringing AI to engineering design. This is the most expensive building in Finland, the newest nuclear power plant in Olkeluoto. They started with a round 3 billion euro budget and ended up more than 5 billion over the budget. And there are similar cases of massive technology projects with even more outrageous budget and schedule overruns. And what's similar in these products is that they are complex, safety critical, highly regulated systems. And that the course of the development here is defined by thousands and thousands of pages of requirements. So technical requirements, there are simple natural language sentences that somehow describe what the system should be or what it should not be. But the problem here is the sheer volume of the requirements. There can be tens of thousands of individual requirements and they can come from many different sources or many different documents and from different stakeholders. And still they're processed manually by people, so people read and evaluate them. There can be several years of work related to the task, different quality issues, ambiguity and contradictions between the different sources. And if these things go unnoticed from the early phases all the way to the production, it can lead to catastrophic problems down the line. So at the moment this work is done by people manually and we want to leave it to computers. Our AI and semantic analysis tools automatically read the documents and collect the requirements from the regulation documents. Analyse the quality of the requirements, organize them, categorize them. For example, flag contradictions between different sources of requirements. And visualize the results for better communication and export them to the commonly used engineering tools. So we save time up to 80% by automating the process. We eliminate defects from the requirements and save fortunes by increasing the quality of the requirements models, which are in practice the foundation of the whole design process. We have a cloud-based tool and a standalone tool for sensitive applications, and the goal is to link the pricing to the benefit, so the amount of data analyzed. We target companies with massive development projects and the savings potential in these systems for say multi-billion euro market. So far we work with companies from aerospace, defense and energy sectors, and we want to target companies like Airbus, NASA and Thales. Our competitors do mostly requirements management and we are the only company that does automatic extraction of the requirements from the regulation documents. We are a team of four people plus three advisors and four of whom with PhDs. So we are a team with deep technology background, the strong experience both in AI and the engineering vertical. And we have roughly ten years of background, research background behind the tool. We are looking for a 200,000 euro funding and the goal is to leverage that with public instruments from Texas and EU. We have a pilot version coming out in January, so in case you're interested either in investing or in piloting the tool, please contact me, send me an email or come see me after the pitch somewhere around here. Thank you. And judges, if you have any questions, I'm happy to answer. Good pitch, good energy for a first pitch in the morning. Tell me more about how you represent the output of this, so you understand the text. How do you then turn that into an engineering piece of work? Well, basically requirements are managed in kind of dedicated Excel type of tables in, for example, IBM doors. And so we can feed the data into doors. So we can kind of work before you get the doors in complex technology systems. We can feed that data there or then we can just visualize it for better communication. So have like interactive or dynamically zoomable like visual graphs that you can show the whole requirement models easily. Cool. I think very, very interesting project that you're tackling. Look, I'm from Berlin, so I think Berlin is pretty famous for not being able to build an airport. So I think it's highly needed. So one question is a bit, so if you have kind of all the regulations in your tool, right? So the biggest value lever is more about how do you make sure that they're actually implemented at the end? Because I think having them all in your software is great. But how do you make sure the construction companies actually also take care of it and implement it accordingly? I think that's something we haven't targeted at the moment much. But if we increase the, like how it's communicated, how can these requirements can be communicated in the company or between the different stakeholders? That should also help that process. And actually today we have a meeting with the company who does that, the verification and the testing part. So we're trying to find connections also there. Sorry if I missed this, but what's the main software that these companies are using today? And are you integrating with that or are you kind of something separate from that? Well now we're completely separate from that. It's requirements management tools like Polarion or Doors. But our intention at this stage is to be kind of feeding information to them, because that's the de facto standard in these kind of complex technology projects. It doesn't make sense for us to compete there, but then the goal is to feed information to that, to those softwares. So when I look at productivity, let's say gains of startups like yourself, I always say like the traditional rule that you improve even costs to one tenth, it's not always enough. Everyone is trying to now collect data and everyone is trying to automate the processes that you described. So what really makes your approach unique and what do you think that you really have an edge? Well, at least I haven't seen these kind of solutions in this vertical. So it's unused I think in these projects, these kind of projects. And well also I think that let's say that these kind of technologies, if we can use them in these kind of complex projects, the value creation potential is so huge that if we can do this there, I think we should make a difference. Okay, many thanks. Thanks.