Rail Vision (NASDAQ: RVSN)

Doing great, thank you very much for having me. It is a great pleasure to be here with you.

Okay, hello everybody. We'll start with a short movie and later on I will move to the presentation.

So hello again everybody. I hope you enjoyed the video and let's go directly to the presentation. A bit about Rail Vision: it was founded in 2016 by four founders, in which I'm honored to be one of them. We have about more than 60 employees, when the majority of them are only personnel, scientists, PhDs, and so on. We have a strategic investor partner which is Knorr-Bremse, a well-known company in the train industry. They're involved in this industry, let's say, 110 years ago and they're a traded public company in Frankfurt. But I think that the most important thing from our point of view as a small company from Israel is that they have the global presence all over the world. So whenever there is an operator, they're already there, speaking the same language, they're already customers of Knorr-Bremse. So imagine if Rail Vision, a company like Rail Vision, would have to set all of this ecosystem from zero, you understand what the challenge would be. So in this sense, I must say that this is a great thing to have in Rail Vision. So let's talk about the problem that Rail Vision technology solves. You know, a train operator, driver, engineer, whatever you call it, can see and can classify in daylight 300 meters, 400 meters, and at night it sees nothing. And let's say, give or take, the braking distance of a train is about half a mile, means that your train driver is a matter of fact irrelevant in the sense of avoiding accidents. So our system can classify up to two kilometers and by that avoid and provide the driver alerts so he can stop the train, sound an alarm, or whatever is needed. So you know, when a train accident takes place, beside the casualties, God forbid, the damage to the platform itself is so big, is so huge, because it has to go through a payment process when it might take even one year to get it back in service. So as it said, when wheels are not turning, you are not earning. So beside the damage to the platform that costs quite a lot of money, you are jamming all the traffic, all the network is done because you have one track, two tracks, and now everything is jammed. So beside of the direct cost of the downtime of the train and the accident itself, you have all the wreckage spread around, you have to clean it up, and only then the train network can start again. So by avoiding those terrible accidents, we are a matter of fact preventing these huge losses for the operator and the infrastructure. So when the train operators understood that they have a problem, they looked to the right and the left and they saw that the most advanced industry in that sense is the automotive. But unfortunately from their point of view, the distances, the ranges that are discussed in this industry is 50 meters, 100 meters, something like that. But for the train industry, you need at least 200 meters in the marshalling yard and up to two kilometers in the main line. So this is why the only available solution, as far as we know, is only available in Rail Vision. So what is our solution? We have an external sensor unit which involves a visual sensor, a very unique one, and also a thermal sensor. All of those were developed in Rail Vision. The optics, the whole concept itself was developed inside Rail Vision. So we can see 24/7, day, night, rain, snow, and we can classify up to two kilometers through all of these a variety of weather conditions. What you see in the picture is the switchyard system that can classify up to 200 meters, and inside you have the computing unit that runs artificial intelligence in real time. So we don't need any external service or server like Google, Amazon. Everything is running on the edge computing, and therefore this is quite efficient and can be considered as a safety-related feature. On the left, you can see our flagship system that's already sold to Israeli Railways. You can see the installation of the long-term pilot, and after about one year of testing our system throughout the year, Israeli Railways signed a contract with us of delivering the first 10 units. All over the world, this is the first time globally that this kind of system and technology is involved into operational trains that are running operational passengers and freight as well. On the right, you can see our switchyard or marshalling yard system that was developed with SBI Congo. Just for you to imagine: three trains coming from the port carrying 100 wagons each. The first one carries ballast, and the second one carries sand, and the third one carries wheat, for example. Now you have to arrange the train that goes to Berlin and that carries three wagons each. So all of this operation, arranging, connecting, disconnecting the wagons, takes place in the marshalling yard. The thing that quite a lot of employees are running, hanging around, and you have to go from one side of the train to the other one, a very non-efficient process. So with our system, we can increase dramatically the efficiency of this marshalling yard operations. Beside that, we know from a U.S. customer, not only from us, that there's a huge problem of lack of human resources in this industry because nobody would like to go out there and stand rain, snow, outside of the locomotive and arrange the movement of the operational activities. So one customer told us that they trained the people, the employees, for two months, and then after two months they just resigned. They cannot stand it. So with our technology, we can increase or decrease the need for the lack of people that are involved in this process and even go autonomous one day. So let's talk about the market. You know, the market is just huge. So whenever infrastructure and transportation are involved, it means a lot of money. Okay, so whenever we can contribute to the efficiency and safety of this market, it means that we are in business. Let's talk a bit about going from maintenance to predictive maintenance and infrastructure monitoring. Our system is already installed on the locomotive, so there is no reason that we will not do additional activities that cost the operators so much money. Imagine a train that goes from Canada to Mexico and you have a vegetation problem through this route. And instead of waiting till the vegetation will hit the train, we can provide with our system an alert when the tree is going closer to the train, and by that, the maintenance guys can arrange somebody to go there and cut the tree and by that solve this issue. But imagine that you have data collected for one year, for example. You can plan the maintenance after that a year ahead and by that increase dramatically the efficiency of the maintenance. So this is what I'm talking about when I'm saying going from maintenance to predictive maintenance. So this is also very valuable for the customer. What is the business model? First of all, we sell the system itself for every locomotive. But imagine that the locomotive's life cycle is about 20, 30 years. So it means that we are obliged to provide service through all of these years. So we set up software updates, features updates, and you know, electronic components for nowadays are lasting for three years, five years, nothing more than that. So once they have a problem, we can also do the service of obsolescence management to the customer and by that supporting the hardware as well through all of these years. So if we'll take, for example, a parallel market such as the military or civilian aviation, in 10 years you can imagine even multiplying the value of the deal that was done with this customer. This is an example, the first system that we sold for the switchyard industry, and this is the Deutsche Bahn Cargo. You can see the classification of the tracks on the right side and the classification of a train and also a person, which is very hard to see. I was on this locomotive, you couldn't see a thing. It was a dense fog and you could barely see something. So I don't know any other technology that can classify to that resolution that you can alert for this person, which is a matter of fact your employee, and you wouldn't like to damage him or hit him in whatever. Let's talk about a real autonomous train that already is running in Rio Tinto in Pilbara, Australia, and those trains are in an AutoHaul project already running freely without any driver inside. But they don't have any obstacle detection system. So this is why they made all the way to Israel to witness our system capabilities. And after that, we signed a contract with Hitachi, which is the Rio Tinto integrator. We managed to deliver on time our system and also conduct the long-term pilot for about half a year. And now we are discussing the next phase. It was a great success. Our system outperformed through, you know, day, night, whatever the train was driving through. Israeli Railways, as you might have already noticed, we already managed to sign a contract with Israeli Railways. You see here the actual installation for the LTP period. Our system ran about a year through Israel with our system, and the system outperformed beyond any expectation. This is why we managed to sign the contract and move forward with that. And as I mentioned before, this is the first time ever that this system with this kind of technologies, with artificial intelligence classification, is running on operational trains, which is a great message to the market. So what about the future? You know, there's a lot of things to say about the future, but I would mention only one thing: that a train is going autonomous without any doubt. Because when you have to take care of employees to be involved, being drivers in locomotives, so you're not really sure that they are fully concentrated outside the train, the locomotive. They have other things to do, to take care of the system, inspect the systems of the locomotive, and also might have cell phone conversations or whatever. Being autonomous is a matter of fact inevitable, or at least ADAS, assistant driving, autonomous driver assist systems as we have in our cars already now. So there will not be any autonomous system without eyes, and as for today, we are the only available eyes in the market, and we are the best ones, going up to two kilometers classification, which is more than a mile. Let's talk a bit about the people. The chairman of the board, which is a senior vice president of Knorr-Bremse, as I mentioned before, has vast experience in the railway industry. Myself, I am in the sensor technology since '94. I was in the military service as an electro-optics expert, where I was after that in the military industry, defense industry, doing missile warning systems, detection systems. I was jamming missiles for quite a long time, and then we established Rail Vision. And also Ophir, the CFO, which has a vast experience of public companies in Israel and outside Israel as well. A bit about the financial data: as you can see here, Rail Vision capital investment till now is about around 63 million dollars. The net cash flow updated to September 2022 is around 8 million, and the cash balance as for September is around 10.4 million dollars. Okay, so if I'll summarize the presentation: we have a strategic partner which is deeply involved in the industry, also participated in all the rounds of raising money and will participate in the future as well. The market, as I mentioned before, is just huge, and we are there to increase efficiency and decrease downtime. Okay, system is already available. We've been through all the hard problems of development, everything is already set in place. Long-term revenue industry, we are not a dream company. We are not talking about any game in the app store. We are here with a sustainable solution in a sustainable industry. So this is why our business is something real. Okay, so the only thing that I have to say is that our management, as I showed before, also with vast experience in technology, sales, marketing, financial, all the things that you need in order to move forward to the success. Okay, that's it, I think. Thank you very much for your time.

Okay, great question. So first of all, our system gives enough time for the driver. At the moment, it has an ADAS, the same as you have in your car. It provides an alert with enough time for the driver to respond, so he can stop the train, he can decrease the speed as to his sure about what to do. Consider a train that travels at 45 or 40 meters per second and you have a few hundreds of meters before you need to take some action. So you have about 10 seconds, 20 seconds to respond. It is quite a lot of time to do that. So about the false alarms, it depends, of course, on the size of the obstacle. But I can say that from a standard obligation point of view, a regulation point of view, all you have to do is be better than a person. And according to available research, you can see that artificial intelligence is doing much better. Different technologies are at 93% classification. So our system can go up to 95% and in some occasions even much more than that.

Sure, if it is an obstacle that caused the derailment, just the accident that took place in Missouri when there was a dump truck that was stuck on the tracks with the terrible train that was there, yes, our system would probably, I wouldn't like to say 100%, but very close to that, would be able to prevent this kind of terrible accident.

I can say that in Europe, Asia, we have POCs there. Also in the U.S., I can say that some of you might have saw a press release of a company in the U.S. that came all the way to Israel to witness our system, and we just placed a PO for one system for a POC. So I think our existence is almost all over the world.

For the mainline system that classifies up to two kilometers, I can say proudly that at the moment we don't see somebody that is even close to us. Okay, in the switchyard, there are some attempts for a Russian company or guys from the automotive industry, but in terms of ranges, they are not close to what, as far as we know, we are not close to what we can offer up to 200 meters classification.

Well, this is a real great question. We are collecting data since even before, as founders, we're collecting data for seven years. And because of the contract that we signed with Israeli Railways from the beginning, we had access to the relevant data for the train industry. We usually don't use data that, you know, the city provides. So whatever available data that you have out there, it is good, you know, for playing around. But the actual data that is related to the train industry in our system was collected in the train industry all over the world. It was in Germany, it was in Israel, U.S., Asia Pacific. So we're talking about hundreds of terabytes of data. And dealing with that, we have the data management in-house. This is a thing that usually we are not outsourcing for that from various reasons. But the main reason is that you have to control the work. You have a lot of rework on the data management. This is why it is very beneficial to do that in-house.

Well, at the beginning, you know, all beginnings are tough. Same here. The first customers would like to see a transform at least half a year. But I think that what we achieved till now will make our life much, much easier. Once they see that it is moving forward with Israeli Railways, with Australia, with Rio Tinto, and with DB Cargo and some other places around the world, it's going to be much easier. Okay, so keep in mind that every customer that we are talking about means the deal size can go up to tens of millions per customer. So we're talking about working hard, yes, but it will be very beneficial, if I got your question right.

First of all, I'll be grateful if Jeff will find the time for a chat with me later on to have 30 minutes. I'd love to hear his opinion, his insight on what's going on. But talking about these companies, the company in Canada, we know them very well, as a matter of fact. Okay, let's give it this way: they're talking about their Rail Vision Analytics, not Rail Vision. Okay, Rail Vision, the UK, are talking about some kind of device that will be attached to the train, and this is about inspection locomotive, the thing that takes a downtime to the infrastructure itself. Okay, they're all Rail Vision laser-based, and we know the technology very well. But we are talking about looking forward up to two kilometers. A thing that the companies, it might seem the same concept or the same technology, but it is not. But of course, with time, merging with companies like that is a very reasonable economical logic.

Thank you very much. It was a great pleasure being here with you.