Good day, ladies and gentlemen. Welcome to today's conference call to discuss the recently announced acquisition of DeepCube by Nano Dimension. My name is Ian, and I'm your operator for today's call. Yoav Stern, CEO of Nano Dimension, will discuss the acquisition and the strategy, followed by a question and answer session. This call is being recorded. Before we begin, may I remind our listeners that certain information provided on this call may contain forward-looking statements, and the safe harbor statement outlined in the press release that was published regarding this acquisition also pertains to this call. If you have not yet received a copy of the press release, please view it in the investor relations section of the company's website. I would now like to turn the call over to Nano Dimension CEO, Yoav Stern. Yoav, please proceed. Thank you very much, Ian, and good day to everybody. Thank you for taking the time and listening. I'm going to discuss the acquisition of DeepCube, and then, of course, we'll answer questions that you may have. To remind, a reminder, we're looking at three kinds of acquisitions over the last half a year, even more, actually, starting in July or so last year. This is the acquisition of the second Type B. This is an acquisition of a company that has a technology that, within many technology companies we're looking at, that we believed, after due diligence, that it's important to have this technology in-house, and it will be much more time, much more effort, if at all, to try to develop it ourselves. Now, we've been working on this specific company for the last probably four months, which included due diligence, technical due diligence, technology due diligence, very, very intensive. Plus, looking at other companies, the comparison of other prices that other companies like that have been traded or sold, not a lot of them exist, that were sold, and we decided finally to move on, negotiations, et cetera, and we just closed it. Now, DeepCube, out of technology companies by itself, is a unique one for us and in general. We're not buying here technology that we are involved with in materials. We're not buying technology that we're involved with in printing or in specific hardware and specific technologies that involves with building machines. It's actually much more important. DeepCube is the technology that's going to be used as the brain of our both machines, 3D printing for electronics machines, additive manufacturing. And with the DeepCube brain artificial intelligence, which I'll explain in a second, we intend to fulfill the vision of building much more than just very smart machine, but rather building a network, what we call, smart fabrication network of machine, which a brain and control will be up in the cloud, or in the private cloud, and the machines will be nodes in a network of many machines, printing electronics in different places in the world, owned by either a network of a customer or a network of few customers, or connection to our own network. Inventory is going to become digital rather than holding inventory on location and be sent just as need, just in time to be printed in the different locations. So it's a very big vision that can be and will be fulfilled over the next couple of years with the DeepCube brain machine. Now, what is it, the brain machine? DeepCube developed a deep learning technology, and I will give you two words just to people to realize what the difference between general artificial intelligence and artificial intelligence is based on deep learning and specifically machine learning. Our machines are in the process of being developed into being production machines. In production machines, you need to have a machine that is correcting itself. We can't afford machines that are printing, and the results are not very high yield. So, by definition, a physical machine that's printing, is printing with certain errors, and we need the brain that can read the errors as they happen in real time and correct the errors as they happen in real time, and, that way, makes the machine an intelligent machine. So much so for a network of machine. Deep learning, and again, different from regular artificial intelligence, is a brain that is learning without having to teach it. The big problem with artificial intelligence is you have to teach it what is right and what is wrong. Deep learning technology was developed in the academy about 20 years ago, but it was never practical for industry because you needed great computers, huge computers, to enable sophisticated programs and algorithms like this to look at the picture of a cat and a dog, and then make a decision, what is a cat and what is a dog? The old artificial intelligence and other artificial intelligence systems will start to describe the cat and describe the dog, and its two ears and two eyes and a mouth and teeth, and the computer still would not separate between them because it's the same description. So you need very, very sophisticated description of what is a cat is cat and what is a dog, which takes a lot of manpower to teach the computer in a regular AI system. The deep learning is different. You show them 50,000 pictures of dogs, 50,000 pictures of cats, and after seeing that, by itself, it knows the difference between a dog and a cat, and it will start to identify dog and a cat at an accuracy of about 95%. That is what deep learning is. No teaching, inference is reached by the system teaching itself, and that's what we need, because if we were try to teach regular artificial intelligence systems of what is right PCB and what is wrong PCB, good luck for us. This would cost much more than it'd be worth to use artificial intelligence. People, a group like DeepCube exist in about four or five companies around the world, as much as the level of DeepCube people. There's many other people trying to do it, because the ability of what DeepCube, DeepCube reached, is to have the deep learning, machine learning system, not on a Cray main computer, but on a small edge device or machine edge device. This is the breakthrough. It's speeds that are order of magnitude higher than anybody else in the world. There's a group like this working in Tesla, there's a group like this working in Apple, there's a group like this working in Google, there's a group like this working in Facebook. I checked, tried to see other groups that reached or getting close to them, including this group. None. This group is working here with the scientists that got together after years of research in this field, so we feel that we're very lucky to be able to acquire them. As I mentioned in the news release, there are contracts over the last year and a half between DeepCube and the most major semiconductor manufacturers around the world. The reason is very obvious, they want to mount the software straight into the CPUs and into the chips. We're not going to stop the discussion on that, even though it's not directly within our focus, but obviously, it's a source of potential revenue, we'll continue it. What we're going to focus with DeepCube is applying this engine, call it brain engine, into our machine now, starting tomorrow, actually started yesterday, and developing the next generations of our machine with the smart capability to identify automatically all problems in fabrication, increase the yields by learning by themselves of what is wrong and what is right, based on dozens of variables, including thermal pictures, which in quantities of thousands of pictures through a process, including of regular pictures, including of temperature, humidity, all the process variables. Everything is going to be swallowed by this engine, processed in real time, and cause the machine to be smart as it is printing and increase the yield, and obviously, getting into production successfully. Next step, of course, is going to be the machine is going to start to be connected through this brain on the cloud between themselves, update each other, which machine, which batch is working or not working, which machine is free and clear, which machine is identifying errors that other machines has to know, and it becomes like a neural networks of printing machines. This is all coming around to fruition or will coming around, coming into fruition in the additive manufacturing electronics industry. However, one thing we realized as we, in the last four months, did due diligence on DeepCube, is that the same engine that we are now going to develop into vertically to electronics, really fits, if you think about what I said until now, fits any other three-dimensional additive manufacturing printers. So if you have our engine, intelligent engine working, we're already considering licensing it out to people who are not competitors in the other areas of additive manufacturing business, not in electronics, which can apply the same intelligent solution for production machines that are in additive manufacturing, metals or plastics or anything else. So we're very, very excited, not only about the ability to take our machines forward into a much better performance and intelligent results, but also expanding our business model that will enable us to be the suppliers of intelligent solutions for fabrication, even outside the electronic industry, eventually. So that's insofar as the DeepCube acquisition, and if there will be questions afterwards, I'll be happy to discuss it. A couple of more issues that I want to discuss with you in this opportunity that we're together, kind of virtually together. Share price. I'm sure you care about the fact that share price of Nano was going down over the last little while. Well, I care as well. First of all, I'm an investor. Secondly, all the upside of all 12, actually 13 management members of Nano Dimension, including the acquisition now and future acquisitions, is tied to the price of the share, which I insisted on. There's no other upside. Of course, they're getting paid salaries, but very minimal bonuses, and all the upside is from share going up. So we are on the same side of the fence, ladies and gentlemen. But the difference may be in between what the interest of us and you lie, which is equal place. Maybe there is illusion that I intend to do something about it, but the share price that is artificial or superficial, that's not my business. I'm not in the business of trading-wise, making the share price up or down. I'm not in the business of fighting the short players. Short players can play with any share. This is the way the market works. This is the way the cookie crumbles. Guys, whoever can't take the hit should not get into the kitchen. The way I will make sure the share price is going up is by running the business the best way I can and reaching results as fast as possible, not for the price of spending too much money foolishly, but spending it wisely because it's your money. We do have enough money to reach the vision I discussed before, so we're very comfortable. I'm not going to announce events too early before they actually happen. I'm not going to push the M&A faster, faster just because some shorts or non-shorts causing the share to go down or up. I'm going to run the business for you, for us, the way we envision it, and it will increase the value in a substantial manner because we're going to have a revolution. Now, you know, not only in the electronic industry, but once we prove the case in the electronic industry, we're going to move on to three-dimensional printing in general. We do not pay bonuses to people who work for you just because they are coming to the office. They will make money together with you or will not. Since we don't intend to raise more money now, we have enough to fulfill the vision I've told you on all fronts, both internal development and M&A, not pressured under many other circumstances of other companies to do to raise money in a down round, low prices or whatever. We're just going to run the business the best way possible. And inasmuch as it relates to this, I want to remind you, we are not a SPAC. We're not running to buy or running to announce because the price of the share or because other reason that the lifespan of the SPAC is finishing in 18 or 24 months. We're an operating company, well-financed, thank you, and thanks to you, with very sincere thanks. But we are going to deliver for you a value that will be multiples of the value today. We're going to do it by the way I've described it today and then a few times in phone calls before. Thank you very much for being supportive. We do care, we are attentive, but we are going to do it the right way, which is practically the only way. I'll stop here, and I would like to be attentive now for your questions and try to answer them. At this time, we will now begin the question and answer session. To ask a question, you may press star, then one on your telephone keypad. If you are using a speakerphone, please take up your handset before pressing the keys. To withdraw your question, please press star, then two. At this time, we will pause momentarily to assemble our roster. Our first question comes from David Masterson of Taylor Frigon. David, please proceed. Hi. Thanks, Yoav. This is Dave, and congratulations on the acquisition. I was wondering if, as you sketched out the vision, you could explain what if I'm a customer, an additive manufacturing customer making PCBs, is the idea that this deep learning network is going across customers, or is it just looking at the data from my manufacturing? I'm just thinking about the deniability. Yeah, it's a very, very good question. The plans, once we get to establishing the network, the neural network for additive manufacturing, is multiple, and your question is right on. First of all, a large customer, if they have multiple locations or even in their own location, they will have their own network and all the machines networking within themselves. Obviously, certain, for instance, defense customers would not want to share this, maybe not even the public cloud. They'll have their own personal cloud. We will have our own network, which will connect to all the customers that allow us to connect, and we'll have to make sure we supply them with the cybersecurity that they request, so they don't feel that certain information is running out without control. And we will use this network connecting to our customers to help them in maintenance, to help them in updating the machines in real time about errors that are happening on the other side of the world, or maybe batches of ink that are not proper or certain things that takes afterwards, weeks or months, otherwise, to conclude by reading reports by manpower. This artificial intelligence and deep learning, machine learning, can, in real time, reach results. And if it's not secretive results, quote, unquote, "It will and can publish it to customers to the improve- to improve their performance on the machines." So that will be our part of the network. So it's multilevel. Thank you. Our next question comes from Sam Rebotsky of SER Asset Management. Sam, please proceed. Yeah. Good morning. Could you discuss DeepCube's sales and backlog and the $40 million that we're paying, plus is the $30 million depository receipts for Israeli stock, and how does that compare to domestic U.S.? ADR are securities that are traded on Nasdaq. It's completely comparable to any other share you buy on Nasdaq. The company is not traded in Israel. Everything you trade, everything you buy is American depositories against one, each depository note, call it. There is a share behind it, which is a share of the company. So as much as you're concerned, you're buying American security, selling American security, there's no difference. That's point number one. It's just a mechanical tool that enable easily to have the Americans invest in American paper, which is backed up with an Israeli company's share, common share. Point number one. Point number two, regarding the question, if can I describe the backlog and customers of DeepCube? The answer is no. In other words, the backlog and, and has DeepCube been profitable? No. No, we're not gonna talk about that now. Is that what you're saying? No, because it's confidential, and I don't wanna- do not want to describe this publicly, but the answer is they were not profitable, and we're not going to discuss other numbers regarding DeepCube. Okay. Do they compete with Sigma Labs? With who? Sigma Labs? Yes. I don't recognize the name. I'm sorry. Okay. Okay. Specifically, I don't recognize the name Sigma Labs. Okay. Well, good luck. Hopefully, DeepCube becomes profitable and everybody gets to know it. Thank you. No, no, I wanna correct you. DeepCube is. The plan is not to get DeepCube profitable at all, actually. The plan is to integrate DeepCube technology into Nano machines and network, and fabrication network, and make Nano Dimension profitable. We're not going to build DeepCube as a subsidiary and run it and make it profitable. Absolutely not. We are going to integrate the technology, which will enable us to be profitable on Nano level and grow Nano sales faster. Okay. Thank you. Thank you very much. Thank you very much for your question, sir. As a reminder, if you have a question, please press star then one. Our next question is gonna come from Brian Herman of ViewTrade Securities. Brian, please proceed. Hi, good morning. Thank you for the call. The question is, can you summarize the terms of the acquisition and talk about, if there's any performance bonuses on the DeepCube side? And just a general summary. I read a couple of new or, The transaction was done at $70 million. Actually, if you look precisely, the company has about $2 million of cash. So really, the net is $68 million. But let's think about running numbers, because we paid 70, and we got the company with $2 million of cash. So if we speak about 70, so it's easier to understand, 40 of it was paid in cash, 30 of it is paid in shares. Most of the shares were paid to the founders, Dr. Eli David, and a couple of partners that are very, very important to the continuation of the business. They joined Nano Dimension, and those shares are being subject to standstill. Some of them, 12-month standstill, which means nobody can sell them in the market in 12 months. And for Dr. Eli David, the father of the technology and one of the world leaders in deep learning, machine learning, being an advisor to people like Samsung and to other, that I can't name their names, but I told you about the semiconductors company around the world. Him personally, his shares that he received are going to be held in a standstill or in a block, blocked from sale for three years, and will be subject to him being with the company and performing. So that's kind of a long answer to your short, good question. Great. Sounds great. Thank you. Thank you. All right, next question comes from Assad of One-on-One Consulting. Please proceed. Hi, thank you for explaining all the process. I just want to know that acquiring this machine learning and integrating with your existing machine how complex this process is, and have you run any prototypes on this? Or the idea of integrating the deep learning with the Nano Dimension machines, how it was conceived, ensued, and how long will it take to get it to the implementation level? Thank you. The process is very complex, but it's not a technology barrier, because the most complex part of the process is to develop a deep learning, machine learning brain. That is done. That's what we're paying the money for. So that we probably shortcut the cycle that would have taken us 3-5 years, I would say. So we have the engine, we have the brain. Now, we are implanting the brain into our machine. So the implantation is not building a brain, it's about connecting the brain, if you want to use this metaphor, into the nerve system of our machine, so the conclusions and the results of what the brain is thinking, analyzing in real time, is being sent through the nerve system into the different parts of our machine. That's a much less complex process, but it's a lengthy process of developing mostly software, by the way, it's not a lot of hardware. It's not going to be implanted into the existing machines. We are already in the process of developing the next 2 generation of machines. It's going to be in the next generations, and until it will be effective, it will take no less than 4 quarters and probably more than 6. So we can say that it will take up to 3-5 years to complete that integration and end-to-end operational model? No, no, no, no. I said exactly the opposite. I said it would have been 3-5 years to develop what DeepCube have developed in deep learning and machine learning. That one we saved. We don't have to go 3-5 years. You, you heard me the opposite way of what I said. I said that what we have to do is to implant the brain into our machine, and I said how long it will take. Thank you. Thank you. This concludes our question and answer session. At this time, I would like to turn it back to you, Stern. Thank you very much. Actually- Yeah. Pardon me. More questions. Please let people that want to ask, we'll just be patient and wait if more, more questions come up. Certainly. Our next question comes from Bob Ceccarini of Retail. Please proceed. Hi, great acquisition. Hi. I just wanted to ask a question on the next generation machine. In one of your interviews, you said that it was, looked like it was coming out over the summer or maybe even prior to the summer. Are we still on schedule for this? Yeah. I want to make it clear. The next machine, which is based on the DragonFly, is coming this summer. It's entering alpha stage in a couple of weeks, and then beta stage. There's practically no risks of development. It's now only implementation and getting into a commercial model that is, all development is completed. So, yes, coming in the summer. The next, we have two more generations already on the, I would say, on the drawing boards, but it's more on the drawing boards, it's already in development. One for 2022, one for 2023, and those are more advanced machines. The second from now is totally not based on the existing machine. It's totally new technologies, and they will come and get us into the inflection point of sliding into the early production models and where the market size grows 10-15 times, because we're beyond the just prototyping. So those is 2022, 2023. The summer one will come on time. This next generation that you're working on now, that'll handle light manufacturing, correct? The first one will be early manufacturing, just very short runs, and the next one over is full manufacturing, yes. Okay. Thank you. Thank you very much. Our next question comes from Moshe Sansov, a private investor. Please proceed. Hi, y'all. Good morning. How you doing? Thank you very much. I just have a quick question. For the next acquisition, is Nano Dimension gonna be looking to buy an existing, and you don't have to answer, like, exactly, but are we looking to buy an existing 3D company or just something in the field? What was the other alternative? Or something what? Or like something that's gonna add to Nano Dimension's productivity? Are we gonna, like, buy an existing company or, like, just a small pieces to add to the productivity of Nano Dimension? We're going to buy either a company in the PCB business, which is a traditional PCB at a very high end that is selling to our own customers or our own future customers, that we're going to use this PCB manufacturer with traditional analog in methodologies, and convert them with our machines, slowly populate them with our new machines, and use them to get to our end customers and OEMs... or we're going to buy, and we're looking at companies, and we're negotiating with companies. I told this to everybody for the last two quarters, and we are moving ahead with few of them. The second type, Type B, is companies that have technologies that we believe our machines in the next two generations will need those, and instead of developing it ourselves, it's worthwhile to pay, even if it could be premium, to buy the companies that have these technologies. That's the second type, Type B. And then Type C, we're going to buy, and we're looking at companies that have complementary technologies that are not necessarily to be, to be integrated in our machines, but they're going to be part of the fabrication network that we're bought, which we are building, and they're going to be machines that are catering to the same customers, even though doing other things, not necessarily 3D. Maybe they do inspections and maybe they do packaging. Those machines, since they're being sold to the same distribution channel, same customer that we are, are going to expand our business and add contribution to our business and strengthens our position with our, with our customers, in supplying a total, total solution of digital manufacturing network. That's the three- Okay. types of acquisition we're looking at. Thank you so, so much for your answer. I'm a, I'm a young investor, but I believe fully in you, and I'm looking forward to our bright future. Thank you so much. Have a great day. As a final reminder, if you have a question, please press star, then one. Okay. I think at this point, is there any other question, operator? Yes, correct. We actually have a question from Jonathan Buckley, private investor. Please proceed. Hi there. I was just wondering, what are you guys doing to improve upon the material that you guys have? Because I know you use a silver ink. Is there something you guys are looking to expand upon that? Yes. That's the answer I was expecting. All right, thanks. Our next question comes from Randy Simmons, private investor. Please proceed. Good morning. I'm curious as to when you're gonna be generating more revenues that should support the share price. I have no idea- You know, what the- I have no idea what you're talking about, supporting share price. I can tell you about what's our plans, and if you're looking for substantial revenue in the next three quarters, you are investing—you're invested in the wrong company. I said it for the last year. That answers my question, then. That's... Thank you. No, I've, I've, been an investor for, two or three years now, so I'm, I'm holding. That's great. Thank you for what you're doing. We count on you guys because as I tell you, I'm very encouraged on how we proceed. Obviously, the revenue will be growing, but I'm much more excited from what opens up with the technologies and the fact that the Corona, once it clears the way, I believe there'll be kind of a slingshot effect. I think we mentioned it, and we spoke about it before. But I don't wanna waste executives' time in running after customers that don't even come to the office because in Germany, for instance, because they're not allowed to be in the laboratories. So we are kind of treading water very carefully to spend the money at the right time, and we believe it's coming. I do, too. Thank you. Thank you. We have another question from Brian Herman, of ViewTrade Securities. Please proceed. So Yoav, what milestones would you look at for an investor looking to see if the company can scale and actually start producing revenue? How far out do we go? Can you talk in general terms as far as, from a distance, what we should look for? Yeah. as the company scaling? 2, 2 years. So if you're patient- Okay. And you'll see in the next two years, not in two years. I'm speaking about within the next two years, you'll see growth in revenue, both organically and through acquisitions. And but, I believe if you're, you ask, if I understand your question, when do you want to see inflection point substantially, you have to have a attention span of two years. Okay. And is it? Are we gonna see two lines, where you're selling machines and getting revenue, and then another line where it's actually type environment? Yeah, yeah. With your- You may, based on acquisitions, potentially that I'm looking at, you may end up seeing a couple of lines, yes. But- Okay. but all the lines, they're obviously going to similar customers, similar verticals, to a similar distribution channel, similar go-to market. Otherwise, we're becoming an investment company, and we're not. We need to get the lines sold in a way that is synergistic and, and leveraging the, the business model. Okay. And do you see a need to access the capital markets, equity or debt or any convertible securities in the foreseeable future? Absolutely not. We have $1.42 billion of cash. We believe we have proper funding for all the plans and the visions I have described here, including internal development and acquisitions. Wow, that's great. Hey, congrats on the progress so far. It's been great to watch. Thank you so much, sir. Much appreciated. This time, it looks like we have no further questions. I will now turn it back to Yoav Stern. Okay. Please. Thank you very much. I think everybody now is in New York, at least, going back to work, and I congratulate you, and hopefully, you'll have a good day today and a good weekend. Thank you very much for listening to us. We are looking forward in the next short while to talk to you again, because we think we have evolution and developments, and I promise to be back on this line and hopefully shortly to speak with you. Thank you very much. This conference is now concluded. Thank you for attending today's presentation. You may now disconnect.