 Hello. I'm here to present the autonomous engineer. She's trained to reduce risky cost and carbon in real estate development projects. That's what we need today, isn't it? So to give you an example of an industry that has moved from having humans doing the engineering to machines, this is a component from the automotive industry. In the real estate industry, we're all doing what we see on the left-hand side. So everything that is engineered in a building like this is done by humans, or used to be. And we're moving that industry towards that left-hand side. For this component, there is a reduction of 70% in material usage. The real estate industry uses 40% of all the material in the world. So I think we need to question ourselves, can we really allow humans to do the engineering going forward? We also have a situation where the high complexity of doing the engineering of a building is not something we easily solve with human brains, so we do stuff like this. For no apparent reason, we shift the air 180 degrees in these ducts, causing a pressure loss that is six times as much as if you have taken a 45-degree angle out to that diffuser instead, and then saving six times as much energy. And you will also notice that that fire detector is probably the last one to detect a fire in that building. There are certain rules on the distance between fresh air and fire detector, but the high complexity in getting everything right is too complex for humans. Everything is known, so it's a sweet spot for AI, and that's why we're utilizing it inconsistently. So we'll call her the autonomous engineer because we're real estate first. We're engineers first, and then we use AI to solve explicit problems that we have when we do the engineering of buildings and infrastructure. So I'm going to give you some concrete examples to see how the machine can outperform human teams. This first one might look like it's very close to architecture. We're engineers, architecture is a different education, different trade, different industry, but shuffling around standardized rooms, standardized apartments is pure mathematics, and I believe engineers are better at mathematics and architects, and this is a way to get more value out of your buildings. So in this case, for instance, it's a commercial building in Oslo. It's going to be turned on, and there's a hotel coming up. Hotels normally consist of standardized rooms, right? So we ask the machine to find a configuration that gives most value, most tiny Norwegian croners per night for the owner, and in this case, the machine found 24 more rooms that a manual team was able to do. 24 more rooms in this hotel means 7 million US dollars extra worth of value for that developer. It's a major increase of value within hours. Another place we see she performs really well is in all the technical systems. So if you had a geometry from an architect of a building, placing all the technical components in the ceiling grids, connecting it with ducts and pipes and cable trays is actually a huge puzzle of knowns. We know a lot of physics, we know the requirements, we know the regulations, but it's really, really complex. So again, perfect for AI, not so perfect for human groups like we do today. And as you will see here, this is a real project in Norway where you normally use 6 to 12, sometimes 15 months with a large team to get the whole engineering in place. In this project, we spent two days because we had to start a meeting with a customer. We put in all the algorithms, let them go and we did a quality assessment and we delivered to the customer. So we took in this case from 12 months to two days to deliver the full ceiling grid ready for tender for this developer. There's a massive amount of savings for man hours. There's a massive amount of saving for money in that face where you cannot have tenants into your building and get income. And then we have another part of the engineering. We have these plant rooms. In today's process, nobody is actually responsible for the size of this plant room and it takes a lot of space that you could eat yourself, you could have tenants renting it, or you could avoid building it. So just taking mathematics from shipping industry, we are solving these plant rooms so everything fits there. It's situated so it's easy to operate. It's a good place for the operation maintenance staff to work and then we can have the right size of these plant rooms. We normally reduce these rooms with about 50 to 20% in space and that is a lot of extra worth for the developer and it's also a lot of cost savings in operation and maintenance because these rooms cost to operate and maintain as well. So what I've shown now is pure engineering tools, but of course in an engineer, we'd normally also handle a lot of documentation in this industry. It's a very documented focus. So we made some tools that can take out risk in huge packages of documentation as well. This was a very hard tool to explain to our customers before chatGPT. A lot of you said to explain this natural language processing technology after chatGPT came around. But we'll basically take the whole tender documentation package, add it in, make it into data, train the data models and we'll look for risk. If we find risk, we tell the project manager where you have risk so you can fix it before sending it out for tendering. Normally a package like that is hundreds of documents. In this case, it was 650 or 53 documents. Very fast for the machine to go in and look for risk discrepancies, references to outdated standards, everything that should be sorted out before sending it for tender to take down the risk and then the cost of course and less problem in development projects. We also do this with the handover documentation. So when you get a building, if you're a developer and you get a building, you also get tons of documentation for that building. Everything is described. And that is in PDFs. So in the handover, we simply take all that documentation, make it into data, we look, is everything there is something missing? Is it the right quality? Have they tried to written something so the warranty doesn't count everything? And we sort the documentation, prepare it for the O&M system, retrieve all the tasks that you need to set up for O&M system so we make it ready for operation to take over that building. And then the funny thing again with the chatGPT, we build on Microsoft Azure. So we had access to take the chatGPT technology into our platform as well because we sat there when it came around with already trained data models. So we just applied the technology and then allowing all our customers to talk to their buildings. So instead of having a graphical digital twin, sometimes they would kill the digital twin in real estate. You now can talk to your documentation instead and that's what actually operation and maintenance need to operate that building. So we were lucky. They came along a lot of AI tool that we could just utilize, although we were building a lot beforehand and I think that's a great value of having a tech company today. There's so much technology being developed so it's easier and easier to take these really valuable tools out to customers. A little bit about our business. I founded the company in August 2020 in Oslo in the middle of the pandemic. We have now scaled so we have more or less the whole private market in Norway. The only problem is that they don't develop that much but luckily we also have the public market and they do develop. They build schools, hospitals, kindergarten, social housing, et cetera. So it's a good market for us. But as we saw the private market was going down, we hurried to UK. So we were able this year to land in a UK, expand in UK and that's also where we have the first office outside of the Nordics. And then engineering of buildings is actually something you can scale globally because you have international standards. The vendors are global. So it's a good opportunity to build a tool that also scales globally. So that's what we're working at at the moment. In total, we have raised about four million US. We have no VCs in at the moment. We just have industry investors and Ingenberg and I still have control. We expect revenue this year to land on about two million US. Still quite small numbers but last year we only have a little bit more than a half. So it's a good growth and we will probably wait until Q1 2025 to raise the next round because we want the numbers to get higher and we see that we're close to getting bigger numbers. And that is because we are also going into US. We have landed in Japan. Since we made this slide we landed in France. We're going into the Middle East and we plan to scale to the global market as quickly as we can. We have a highly diverse team. We have 16 different nationalities sitting in Oslo. Large team of people with PhDs in various fields of AI and mathematics and physics. And we are female, founded and lead. So if you're interested in talking to us any further scan this QR code and reach out on LinkedIn and we're very happy to follow up. Thank you.