Thank you. We will present omniQ today. Thank you for your time and interest, and let us just start.
What you will see here, we are going to show a short clip of a company's overview, some use cases that we are doing. On a high level, omniQ created an AI machine vision technology, a patented one. We have 10 patents on our algorithms, what we call the engine that we created, and there is going to be an 11th one actually probably in a month from now. So we are investing in our technology repeatedly. What we do is we invested in and created a machine vision AI technology where we can identify objects on the go, still, it does not matter what type of object. We follow a very simple three-step process.
The algorithms themselves, they imitate the human brain. We did that, we started our development about eight years ago. Actually, we create neural networks, same as the brain works, and that is why our machine learning algorithms are becoming faster and faster as we advance, as we are exposed and creating a larger footprint. That is why you will see that we are probably the fastest in the market today, and also the most accurate off the shelf.
The three-step process is very simple as it shows here. We first detect through the omniQ eyes, comes in the shape of cameras. So we detect objects, and then we send that information to the engine, the algorithms, where the brain is. That is where we do the identification of what are we looking at, and then we create actions that come in the form of applications, depending, again, on the use case, which we also design. I hope that clip is going to work now. Yep.
As you will see, we started from very simple real-life use cases. Safe cities, it is a market that we are investing in, creating law enforcement help in being a force multiplier, being in assisting public safety, and also in different other aspects like in transportation, parking lots, trains. Today, we even brought it to a higher level, where we identify the number of passengers inside a vehicle that assists government, HOV enforcement. You can see the number two, the numbers, they are changed. That is a real-life demo of how many passengers are in that vehicle. No one does HOV, HOA today. We do that. We are bringing it to logistics as well, and we will show you some examples there.
The main thing is that as we learn, in omniQ, every single time we go into a different or a specific use case, for example, like parking, suddenly you find that there is another level, and that is how we start creating the scalability. I am going to stop this now and move towards this slide, where it will show you exactly what we focus and where we use the machine vision in.
The first market that we penetrated was the public safety one, and you will see some examples there. The border protection as well. We work with governments, we work with militaries. We protect borders by identifying objects that are coming in. The whole thing behind this technology and the investment is how do we bring the proactive capability to our customers?
In border protection, for example, if there's a vehicle coming in associated with whomever that is on the database a threat, a risk, has a risk level of whatever level the risk is, then we can flag before they even come close to the border crossing point. We can alert the security, the guards there, and give them the exact information of what is there, and how can they start now be proactive in treating it in a separate manner that is isolated from the whole flow of that border protection process, so it doesn't hold anyone else. The proactive measure is important because that's what makes us unique, and that allows us actually to bring added value compared to our other competitors when we're in that specific use case.
Transportation as well, that's another aspect, parking lots, together with security. Always has to bring in also security aspect. Our solution brings in security aspect as well, not only the efficiency that we create. In transportation, a nice use case would be parking lots. You go to an airport where it's a sensitive place, of course. No one walks into a terminal with guns on their back. They will drive in, they will park first, and that's where we meet that risk, if it is a risk. Not only we flag that event and notification, and this is how proactive happens and security is alerted before anyone walks into the terminal. We meet them, of course, at the gate, but also it creates a better user experience.
Today we have 67 airports that are using us. LaGuardia, even Los Angeles Airport is using us. Sacramento, San Francisco Airport is using us as well. And again, we bring a better user experience because we can lift gates in 100 milliseconds. We can stop a passenger from coming in or a vehicle from coming in if there is a risk level. We can create a lot of different rules, customized in order to make sure that we keep the perimeters safe and also create better efficiency.
And of course, in enterprises, there are Fortune 500 customers of ours that are using our technology. For example, if there is a big enterprise company and they lay off thousands of people a year, they're always forgetting to take their RFID tags. These people can come back the next day, et cetera. This very simple use case where we could actually alert and create notification, not open the gate or alert security that the person came in without even needing the gate or RFID tag or whatever.
When we started, we're talking about vehicles and people. When we started, we trained our engine to identify the first object was a vehicle, but not only the vehicle license plate or other companies that do license plate recognition, but the complete vehicle make, color, type, model, and license plate. And that's how we're able to create basically digital signature to that vehicle.
For example, when you go to parking lots, the simple use case we spoke about in transportation, that's why we're in so many airports. A lot of times, many states, there is no regulation to put the front plate, right? If you do only license plate recognition, you can't really recognize that vehicle. Or if there is a license plate on the back of the vehicle, it could be covered by snow, it could be covered by a bicycle rack or whatever. Still, that vehicle needs to be identified and we can do that because, again, of our ability to identify not only the license plate, but to provide to that, assign to that vehicle a digital signature regardless of one parameter that is missing. That is how we can identify that vehicle when they enter the premises and when they exit by that digital signature, not only one parameter of the ones that we can identify.
We are known for, like I said, the patents we have on our technology, 90% accuracy. We commit to 97% off the shelf. We go up to 99.999, whatever the number is, accuracy. You can never get to 100%, but we do commit to 97%, and we do everything, the whole three-step process from doing the detection to the actions, we do it in 100 milliseconds.
This is an example of one of our Fortune 500 customers that we work with, and you can see how they use our machine vision and got rid of hardware. You do not need to now rely on RFIDs. You do not need to do anything. Everything is done from a dashboard that we create and bring to our customers. They can update this dashboard with vendor lists, with employee lists. They create basically blacklists, whitelists, and now you can really protect the premises by who can enter, who cannot, or if you let them in, at least someone can come, if that person is not supposed to be there, someone can come and actually take care of that proactively and reduce that risk.
In law enforcement, that brought us, when we started from fixed cameras to do the detections, then we moved to mobile cameras as well. What we call mobile is when we assemble our cameras, we trained our engine to detect objects and vehicles on the move, and that is how we assist and created this solution, the mobile solutions, where we could actually identify every vehicle when we are conducting the enforcement while the vehicle is driving. That is how we can identify on the move, additional threats and be able to treat that as a proactive measure for law enforcement.
That brought us basically to this slide where we see the most scalable product today, Q Shield, one of omniQ's products. What we did is we actually took everything that we learned from what you saw before, but now we created also a very attractive business case, and that is why we are here as well.
The business model says very simple thing. I will back up and say that we showed in this third, fourth slide that we are involved in border protection, but also it came from public safety. That came from, this is exactly what we mean when we say public safety. Every city wants to be a safe city. Every city wants to have their streets safe for their kids to walk on. But not every city has the budget of Los Angeles, for example. Actually, out of the 22,000 towns in the U.S., the majority, 16,000 of them, are ones with up to 100,000 residents. So very small towns. That is most of the towns in the U.S. But they still want to be safe cities.
What we did is we went to the smallest town we could find. Actually, Bill, one of our sales team, Bill Fisher, he lives right next to Adrian, a small town, 6,000 residents only. The chief of police is shared by two other towns. So three cities are actually using the same chief of police. He lives right there next door with the mayor. That is how we got, in an hour, the approval to put Q Shield eyes, omniQ's eyes and sensors. We installed it in one of the intersections, and what we did is we connected our server behind the firewall of that police station. So now we created an integration with the different databases, and it was federal databases, the police station databases, et cetera. Now what we did is we started connecting the dots.
That is basically created now the next level vision of omniQ, where we do make the invisible visible. Everything is there. You got on the databases, the government knows everything. They spend a ton of money, billions, on maintaining these databases. They know whom am I talking to. They know everything about everyone. But how is that creating a proactive approach if it is not knowing what happens in real life, in the physical life? We do that handshake. omniQ is exactly the handshake between the physical world and the virtual world, and that is when you start creating scalability and understanding of what is happening and how to treat and manage it.
So coming back to that Adrian city, we installed the cameras. We turned on Q Shield, and it is like seeing a black screen when you walk into the office, and then you turn it on, and suddenly you manage everything. Exactly like that with Q Shield. We turned it on, and boom, suddenly there were notifications of stolen vehicles, AMBER Alert. On the first week of our deployment in Adrian, we actually assisted in solving a murder case of a missing person that was reported. It was nationwide. It was reported actually in Florida, and we were able to identify the vehicle in Georgia. We helped solving that case, and that was only in one week.
So immediately, that is what Q Shield did. Our product identified all the vehicles that passed through that intersection, 100% of the vehicles that passed through that intersection. We identified the vehicles. We knew how to connect the dots together, everything that existed on the virtual databases. That is how we started creating notifications, alerts. We knew if the vehicles are stolen vehicles, drug dealers, and suddenly you can start managing and enforcing law enforcement, creating a better, safe city. That to us, of course, it changes the lives of the residents there.
What makes it even more unique, as I mentioned, these towns have no money. The next question that we were asked by the mayor, he said, "Okay, we love it, but we have no money to pay you guys." We said, "No, that is fine. We will pay you." What I mean by that is on these databases, there is all the threats, of course, and risks associated with every vehicle, every person. But also it tells you if the vehicle is registered or not. It tells you if the vehicle is insured or not insured. That is how we started understanding that. The business model says that every single time we detected and identified a vehicle that is unregistered, we then issued an electronic citation. We get 50% on average of that revenue.
Now, just to make it simple, Watkinsville is another city that we operate in. We do about $350,000 in our share. Imagine when we go to 100 cities, that's $35 million. Today, after piloting, after starting collecting monies, after proving, after being now in five cities, it started from four because it became like word of mouth. Jason, who is responsible for our financing among other thing, he's actually writing checks every month to mayors. Now they have money. We created a revenue stream, and now they have money to pay for laptops, to buy laptops for students, et cetera. Suddenly there are resources.
Just to understand why this is scalable, why we're here is because we want to get to the 100 cities very quickly. In a few months, we are at 10. Our ROI is between a month to three months. That's how fast it is. We need to invest more in salespeople. We need to invest more in making sure that we get to the 50 cities very quickly. By middle of next year at the most, we need to be at 100 cities. That turns a business model from $5 million it generates today to possibly $35 million. These are big numbers, and that's only 100 out of the 22,000 or 16,000.
So our plan is of course more is higher than that to get to the 1,000 cities and more. But we certainly are taking it and learning from this as we advance. In addition to the risks that we resolve, the proactive, how we're making the physical world visible, the invisible visible, et cetera, we also then create suddenly small towns with the ability to create analytics. Same thing as Los Angeles does, right? Suddenly they know how many traffic goes through this intersection.
Now they want to put us in hospitals. We have several already deployments in hospitals, in K-12 schools to prevent shooting. We proved that we can prevent shooting by at least 70%. Imagine someone that drives into a school where yesterday they bought, I don't know, it's on the database, right, and we're integrated. So let's say they bought last week M16 in Walmart or whatever, and these guys come in in the middle of the day, which is an anomaly that we can detect.
Now, suddenly the police officer in that K-12 school, and this is a real story that I'm telling you, the police officer before was waiting for something to happen and react the best he can. Now, he can be proactive because that person drove in, and we created that alert with a picture and everything. Before that person even parks, a police officer walks to them proactively to even ask a question, "What's up? What's going on?" 70% of the times, nothing will happen. Only because of that.
This is how you start managing. That's how you connect the physical world with the virtual world, and you start creating proactive capabilities. Again, we till now did everything with our own resources. 67 airports, schools, 10 cities. We got 22 in the pipeline already. Things are very exciting. We feel the dynamics and everything, so we want to move even quicker, and that's basically why we're here.
But this is the most, I think, scalable opportunity. That's a map of the airports that we already deployed Q Shield in, and some numbers, the revenues. A lot of the expense, when you break it even further, a lot of the expenses went directly into R&D, of course. Now, once we finished all the pilots and everything, it needs to go into the sales and continue our footprint.
What we see on this slide, what you see here, again, we always work with our own resources. Basically, we are selling a lot as a one-time sale. Why? Because we needed the cash quickly to go into R&D, et cetera, and finish our pilots as well. Now, we see another dynamic trend that is happening in the company where the revenue is becoming, if it was 0% recurring, suddenly it is 30%. That is what this slide is about because we expect next year for the recurring revenue, which comes from revenue share mostly, will be towards the 40%, 50% to be conservative.
And of course, like I mentioned, every use case that we go into and implement our systems at, it opens up a new glass ceiling. Something new that we did not think about. For example, in the safe cities, in all the citations, suddenly what we did as well is, why only registration? Let us do also insurance, and we did that. Let us also train the engine, our algorithms, to detect seat belt violation, to detect cell phone use violation. Then we did that as well. Speed by machine vision, not the laser, what is done today. So we did that as well.
But we did not yet implement these additional functionalities. We are just learning. And the analytics, which intersections have more traffic when? How can we predict later on in the cities and other customers, how can we predict and tell them, "Next week, you better have something done because we learned that this is exactly when there will be a lot of traffic," and they can resolve it by being proactive. So a lot of analytics, a lot of things are coming from the big data.
Just to tell you, today, we do probably millions of detections and identifications of vehicles a week. When I say a week, I am conservative because it is less than that. It is millions. We are considered one of the biggest data company when you talk about vehicles. And the U.S. government is working with us on that as well. It opened up another opportunity for us. And I want to stop because I see that it is already two and a half minutes, and if anyone has a question, please. Yeah.
Is the revenue share to perpetuity or is there an end date to the contract?
You are asking if the revenue share is?
Perpetuity. Is that forever?
Oh, yeah. It is forever. No, no, it is forever. I am sorry. We usually sign a contract for at least three, four, or five years with the city because they have no investment into it. And it just moves as long as the contract lives.
Okay. Early on, did you get some pushback from privacy concerns?
Good question. Thank you very much. One of our competitors is Flock. I do not know if you heard it in the news. And their cameras are getting ripped from every city. It is crazy what is going on there. We were not smart. We were lucky. And that is what differentiate, and that is why you saw the slide about the dynamics of what is going on and the need for our systems. Why? Because every city that scraps Flock comes to us now. They were the first. They do it differently, but I don't want to get into that.
To respond to your question, as I mentioned, that's where we're different. We put our servers behind the firewall of the police station, right? We don't care about any other information other than to issue a citation, right? So they get all the notifications. It is behind the firewall. We don't take or deal with personal data, et cetera. We do the same thing as if a police officer was writing the ticket, and that's how our software is built. Standing right there next to the driver. That's exactly what we get in order to issue a ticket. They decide who, they decide what, they decide everything, and we have no visibility into it.
A virtual ticket is legal?
I'm sorry?
A virtual ticket?
Depends where. There are states that it's not. When I say not legal, it doesn't say it's not. It's just difficult to work through the regulations to get it. There are states, we call them red and blue. There are states, like Georgia and others, that it's easier. We are learning. To get to $350 million, that's 1,000 cities, right, out of the 22,000. That is something that we can definitely do regardless if it's legal in some states or not. The opportunity is much larger for us.
By the way, we have the ability to actually even in ones that were not there, to work with them, and they can issue the citation. They have that capability as well.