Transcript.

Erik: Welcome to the Industrial IoT Spotlight, your number one spot for insight from industrial IoT thought leaders who are transforming businesses today with your host, Erik Walenza.

Welcome back to the Industrial IoT Spotlight podcast. I'm your host, Erik Walenza, CEO of IoT ONE. And our guest today is Aviran Yaacov, cofounder and CEO of Ecoplant. Ecoplant provides energy as a service applications and sensor packages for industrial air compressors. And in this talk, we discussed the impact of air compressor management on energy loss and downtime in factories, we also explored the emerging field of dynamic cloud based control systems that use AI algorithms to optimize performance of industrial assets.

If you find these conversations valuable, please leave us a comment and a five-star review. And if you'd like to share your company's story or recommend a speaker, please email us at team@IoTone.com. Thank you. Aviran, thank you for joining us today.

Aviran: Thank you, Erik, for hosting me. Thank you.

Erik: So I'm really actually excited about our conversation today because I talk to a lot of entrepreneurs, a lot of IoT companies. And a lot of entrepreneurs have the same problem that I have, which is, we see an idea and then we want to go implement it. And before we know it, we've got there’re going to have 10 different businesses or value propositions that we're juggling for 10 different stakeholders.

You have one big problem in scope, you have a tech stack oriented around that. So I really want to also hear the story of how you oriented around that. But before we get into the discussion of your business, maybe you can just introduce where you're coming from and how you ended up running this business. I think you set it up, what about four or four or five years ago?

Aviran: Yes. So my name is Aviran Yaacov. And I'm the CEO and cofounder of Ecoplant. And I'm one of three cofounders. You touch I think, and then in a very interesting point when people study in companies. This is actually my maybe 10th idea. Because my other two cofounders, one of them is my father. My father is 40 years expert in compressed air systems. So he came up with this idea five years ago. My background is IT and information systems and sales and project management. I used to work SAP before Ecoplant, so this is my background; is used to deal with a lot of SaaS deals. So I came from the business background.

And the third cofounder is one of my best friends from high school, we know each other more than 20 years. And when I said this actually was my 10th idea, me and Yurin, the partner, so every week, we sat together and thought to yourself, we want to establish a company, we want to do something big. And we thought about a lot of ideas. And I think the differentiation been between your first idea or the 10th idea, you understand that you need someone that knows the market.

And I think this is maybe the first lesson for me. When starting a company, you must have someone next to you that knows the real thing of the market. And this is my father, he really felt this pain from the market.

Erik: Nonetheless, it's a very focused business. So how did you come to the decision that okay, let's just focus on this? Because a lot of people naturally then say, okay, we've got our core business, and then what are the other five businesses that are all similar enough that we can also include those in scope? I mean, was it just your impulse? Was this a conscious decision to be a very focused company from the start?

Aviran: It's like the real life, me and Yurin, we thinking about a lot of ideas. We thought of the company will optimize, they’re the traffic. This was the one idea. The other one was to optimize their shopping experience in supermarkets. But then my father came again with the idea that says, Guys, I'm a service manager of a company that provide the services to the compressor industry. Usually, today when I want to know about the problem in the field, I need to call the guys, or actually they actually send me pictures of the machine screens, and tells me, okay, what is the 4985 code says, which is? It's funny, because only the experts sometimes know what is the problem. And again, it's not proactive, it's the reactive action.

And when we came with Ecoplant idea, we thought to ourselves, first, it needs to be proactive. It needs to be some of that it will come from the field, from the factory, from the customer. Actually, why he needs to tell the service company what is wrong? We will let him know what is wrong because we track on his equipment.

So I think the first level was to understand the meaning of visibility. To give full visibility to the factory and we start from there. And again, we were very focused on compressed air because we understand the problem, I think, because we have someone who's actually product market fit founding team, this was my vision from very first day, be focused.

Erik: I was really impressed a little bit shocked reading through your documentation around how significant this problem is, particularly in regards to energy consumption. I mean, can you just share a couple of the stats around what is the actual operational or financial impact of this on manufacturing? Why is this a big enough problem to solve that you can actually build a scalable business just around solving this one problem?

Aviran: Yeah. So, when you ask compressed air experts, people that dealing with this industry, they will tell you that air compressors is the secret of the manufacturing industry, because no one's actually knows how to deal with them to maintain them, to operate them in a way that they will be optimized, because air compressor is actually everywhere. But 90% of the produced products in the market contains compressed air.

So, blow up a bottle or chips bag, half of it is actually air. So, a lot of product is done by compressor as a part of the process. Cars, once you to paint the car is a part of the process of manufacturing car. You need to dry the paint with a compressed air. So 90% of their produce products contain compressed air. But what is more amazing to discover here is the 50% of their produced air is going to waste, 50% according to the US DOE, because leaks, poorly time maintenance, blockages on pipes, overcapacity, redundancy, many, many problems that cause this air to be inefficient. So 50% of that is inefficient, which costs to factories hundreds of thousands of dollars to waste every year.

So you can imagine a company such as a Nestle that has 600 sites, they actually waste tens of millions of dollars every year as a result of an appropriate use of compressed air.

Erik: A lot of companies right now are focused on say managing their energy footprint, right? So I understand that compressed air also is quite energy consumptive. Is this the process of actually compressing air, like the air is compressed by air liquid or one of these companies, and then it's delivered? So it's the process of distributing the air that requires energy. Is that right?

Aviran: Exactly. The process of compressed compressing air is that you have air compressors. You actually have in every factory station that has a few air compressors, could be even like 5 megawatts or 10 megawatts of electricity for compressor. So it's actually a small electricity station that you have in every factory that's producing air, and this air goes to the production floor. Yeah.

Erik: So before we get into the tech stack or of your solution, let's talk a little bit more about who you're actually working with. Because on the one hand, I guess this is a very horizontal solution. Like you said, okay, Nestle has 600 factories, every factory uses compressed air. So you could have a top down model where you talk to whatever the CEO of Nestle and then sell down. But I assume that actually in many cases, it's more bottom up, it's some factory manager that is trying to control some KPI. Where do you usually start these conversations? What level of the organization and then which department? Is it the GM? Is it maintenance? Is it manufacturing engineering where the conversations originated?

Aviran: All of the above. Actually, it's both ways, so we work bottom up and top down. We walk in with, with a corporate, so yeah, we talking about sustainability, operation excellence, all the departments in charge have to optimize and reduce costs for the corporate. On the same level, we also work with our maintenance managers and their facility managers, the system engineers in factories, so they are in the local level. We have actually champion program for those guys.

So usually like it's probably the facility engineer or the reliability engineer. And we create like a program for them how to save this energy and how to optimize their operations. We work closely with our partners which is a service providers, company that provides services like maintenance. They sell machines. They do energy audits. There are actually our partners. And we work together with those champions to make their life easier, save energy. Just approach, work with them closely, and create a program for them to save.

Erik: So you mentioned that sometimes you're coming from the sustainability side, sometimes from the operational excellence side. If you just talk about what are the objectives that your customers trying to achieve that is driving the investment decision in the business case? What would you say is the top objective? Is it more from the sustainability side? Is it more from the just the pure PNL impact? What do they actually put down in the business case to justify this investment?

Aviran: It's really depends on the corporate, I can say that, for example, Cardial that have 1,500 factories globally, but they have actually goal for 10% reducing their carbon footprint and reduce the energy. So they are very driven like sustainability. Other corporates are motivated by reducing their energy costs, but also to optimize their operations.

So if you don't have air in your factory, you actually have downtime. If you can't dry the paint on the car, you can't release it to the market, you have recall. So I think it's both sustainability and energy and also reliability and the avoiding downtime. Downtime, it is actually the motivations that every factory or corporate has.

Erik: My understanding is that you're providing kind of the full stack. So from the sensor to the software, maybe also the integration, what is the full stack? And then what is it that you're doing and are there any other partners where you would involve them in implementation?

Aviran: As I said, we work closely with service providers, they actually are partners. They integrate, they install our solution. They supported us first year. They're very close to the field. They already worked with the customers. Each one of these companies that usually have thousands of customers already, so we work with them to accelerate our solution and sell it to the market.

Usually, factories, they have already distances installed, so we connect to existing sensors very easily. Our installation is less than two days. So it's actually very fast installation. As I told you before, I'm now in Indiana. So we are installing now in our system all the biggest bed companies producing beds for hospitals. So it's under two days installation. And if the factory doesn't have those sensors, so we can provide as a power part of one of our packages, so it could be just integrate with existing sensors, or we can bring the sensor as part of the package, could be both ways.

Erik: So your core technology is this SaaS product and then if sensors are required you can advise on which would be the right sensors for particular solution? Can you describe this solution then focusing on the SaaS? So I understand this can be used across different interfaces, mobile, desktop, etc, who would be the end users of the actual solution? And then what information are they receiving, alerts? What is the typical flow for a user?

Aviran: Yes. So the solution itself, it's actually our uniqueness. We have two patents on that globally. What we do we collect data from different types of compressors. It doesn't matter the brand or technology, could be rotary screw or vein or centrifugal, doesn't matter the brand. It could be all brands. We are integrated with all the biggest brands in the world. And we pull out data such as the flow and pressure and kilowatt, oil temperature, hours, operations, everything from the compressor itself.

Also, as you mentioned, the also providing all connecting to existing sensors that already installed on the types, the factory. And our uniqueness, and this is our patent, one of our uniqueness in the stuff that we do very different than the others, we do dynamic control, which means we don't just collect data, provide insights or recommendations or reports, we actually act, we create the outcome, the actual savings. So we control the equipment, the air compressors but on dynamic way.

So not like our solutions that you have today that you just have like a static algorithm, that just control the system, according to the expert that defined it prior to the installation or tune it one time, we do it automatically most likely autonomously. And this is through AI algorithms that learns the patterns of the factory and change ongoing all the time on proactive way the operations of the compressor system.

Erik: So what would be like a typical parameter, maybe what are the most common parameters that you would want to modify in real time based on some operational change in the factory?

Aviran: Yes, so we turn on, turn off compressors that it's not efficient, so we turn it off. We send the alerts predictive maintenance if it’s something wrong, and could be effect of the factory operation. We change the pressure of the system. So it's actually an operational system. So once you install a system, you can be relaxed. We will have the most up to date control system all the time in your factory because also, SaaS, it's over the cloud, so it's added all the time.

The system adjust itself to the factory very, very dynamic production levels, because the world is changing all the time. We know COVID, for example, so one of our very good examples is a factory and maybe we can talk further about it later, that lower his production levels in 20-25% three months ago, during the July it was, I think, 2020. So he reduced the production level in 20-25%. But now he just returned to full production, but he actually produces the same air. So we make his production more efficient because he's not wasting more money now. You see what I mean, 20-25% more production, same energy?

Erik: I can imagine that a lot of the time equipment it’s not shut off when it's not in use, or the parameters are just kept at a constant regardless of how it's being used. We hear a lot of cases that are remote monitoring, right, like you said, extracting data and then visualizing it for some user. Remote control and automated control is less common. Because in a lot of equipment, it's a little bit hard to actually get access to the equipment, and be able to modify the parameters. Is this for you? Has this been a challenge? Or it was actually quite easy?

Because I imagined across these different brands, some of them maybe resistant to providing access to third parties, or there's at least some technical barriers, was this a challenge or am I misreading this? Because in many cases, you have companies like Siemens and so forth who are quite protective of their assets, and do put up some barriers for third parties due to gain access.

Aviran: If it wasn't challenge, it wasn't unique what we do. But I think, again, what we do here using open source communication protocols to connect to and communicate with the air compressors, if it's not available, so we have other ways to control it. So, we figured out how to do it. The connectivity was the first level of the solution.

The real, real challenge is over the cloud there. The real, real challenge is the algorithms is the autonomous algorithms, it's to be up to date, to fit yourself to dynamic changes. I can tell you for sure that it's amazing we see many control systems in factories. Most of them, the average of system to get out of tune is on average is three months, and you pay for that tens of thousands of dollars for implementation. Then after three months, one quarter, your system is not up to date is not. It's like you see how your energy efficiency in factory getting lower and lower and lower over time.

Erik: So, is it the algorithm needs to learn the asset, but then once it learns a particular type of equipment, that applies across any factory, that type of asset that brand or that model is being used on?

Aviran: I think it's very good point because it's both on their equipment type, technology, brand, and industry. So for example, in the food and beverage industry, you have air compressors that you actually it's oil-free compressors, which behaving in a different way that oil floated. You can actually tell to the factory you should prefer this compressor technology than the other because we know best practice, you can compare it to other factories in your type, or like you, the same industry.

Erik: But do I guess people often ask you what models they should be using, right because you have a lot of expertise there? Is this a case where you're frequently recommending manufacturers to use one model versus another for a particular case?

Avrian: Yeah, we are independent. We're trying not to recommend a specific brand. But we can sure help the factory to profile his system and see what capacity of the air compressors needs, what is the technology that is preferred to use on his case, so stuff like that. So we can help the factory to design his compressed air better.

It's a problem that many, many factories has, because you build a factory, you can build it in the 70s, or the 60s, and you build more, more departments, you need more, more air. So it's patches. Every few years you just adding a new compressor to your system. So in 30-40 years, you have a station of compressor that have diversity of many, many technologies and corporations, and the sizes and technologies. Again, it's caused, because time changes, and you adding more and more into production, you have different needs. But not always you have the right advisor that will help you to design your system better.

Factories, most of the times don't understand compressed air. They know how to produce very good chocolate. They know how to produce very, very good cars. They don't know nothing about the compressed air systems. As I said, compressors is the secret of the manufacturing industry.

Erik: Coming back to this topic of the algorithms, so I understand that the algorithms are optimized around particular models, brands, is the algorithm also optimized around a production line? So let's say, you deploy on production line for chocolate manufacturer A and then you later deploy on production line for chocolate manufacturer B that uses the same equipment and produces the same thing but maybe they have different volumes, they have a different production processes for the form factor of the chocolate or whatever, it is there also optimization at that level of the production line, or is it focus more on the asset class?

Aviran: We have two things here. First, one of the biggest problems that factories has is leaks, they have a lot of flicks on their system. And the most worst thing with compressed air, you don't see the leaks, it's air. It's not like water. You don't see the leaks. And leaks, most of the cases are inside the production floor.

So what we do with our system, this is because it's an IoT, SaaS, it's something that it's wireless, we can connect as many as you like sensors to the system. This is our business model very encouraging the customer to connect as many sensors as he want. He can just buy off the shelf sensors and just connect to our platform. We analyze the rate of the leaks, and help the factory to connect it inside the production floor. So we can also detect the leak rate in each one of the production lines. So the factory is a part of the service.

In addition to that, we created like every, like standard API's to the factory. So he can actually send us his tonnage, what is the output of the factory. So if you have certain of tonnage that you’re producing, we can actually correlate the stone age with your compressed air that you produce. And again, it's very unique, because other solution is just focusing on producing of compressed air but it’s just regular central control systems.

Our platform is a 360 platform that also help you to understand what is your tonnage, what is your correlation to your compressed air of tonnage. So, if you see, for example, that you were producing more, we see it as a part of the process to do baseline, so we see that sometimes in the first three weeks we just monitor the system without controlling. So we see the tonnage, for example, goes down, which means the factory producing less. But if we see the compressor system, because we measuring the flow is going up, so, it's not correlating.

It's interesting, like you producing less, but you consuming more air, something wrong here. This is one of the indications of compressed air leakage. So we provide those analytics to the factory to connect his infrastructure to the production floor, his utilities to the tonnage, and on the same time, we have him to reduce it by control the system better identify the leaks, and just keep up to date with his compressor system if he needs to design or add new production line.

Erik: From a system integration perspective, every factory has dozens, maybe even hundreds of systems, and so it can be a little bit cumbersome for people to pick out where the information is. I mean, are you are you connected directly to MES to gather this data? And then do you have any outbound connections to MES ERP, 20 other systems where you can report this for? Because it sounds like for financial purposes, for example, this data might be useful. Now, what does it look like from a system integration perspective?

Avrian: So there are many, many, of course, production and software's, and as I said, MES, [inaudible 27:38] many, many systems. We provide standard API. The customer can actually pull out the data as many as you like and he can just take the data and analyze that in on his system. Any factory today, you have people, it's like a data people, they can collect data from different systems. So we provide a standard API. They can just take the data and analyze it manually, or automatically take the data and integrate it with another system, vice versa.

So we've also worked with a few IoT other companies, that for example, they measuring already kilowatts in the factory compressors. So we created like cloud to cloud integration with those sensors. So if it's something that we see this it has like great potential, and for example, we do like a corporate deal with a customer and he says, guys, I have thousands of sensor that measuring kilowatts in my compressors. And we, of course, also very agnostic, and if it's something that makes sense for the deal, we, for sure, consider it also.

Erik: Maybe we can discuss the business model that a little bit here. I guess if you're doing sensor and so forth, there's some CapEx investment. But is it typically a SaaS operation? And then what would it be, just like a flat monthly or per compressor, what would be the typical business model setup?

Aviran: Really depends on the size of the factory, but it's a flat annual price for the factory, which we tried to keep it very simple.

Erik: But then you'll say, okay, this factory has whatever, 50 compressors or 10 compressors or 200 compressors, and you'll have different levels, is that…?

Aviran: Yeah, usually, is the capacity and again, is a fixed price. Again, it's not related directly to the energy savings because we will do much more than that. Usually, the energy savings, it's something like 20-25% of the dollar value that we provide to the customer on the annual basis. It's a fixed price, very straightforward.

Erik: I know that the answer here is always that it depends. But can you give a high level range if we've got like a medium sized factory that wants to get set up, what would they be looking at as a starting?

Aviran: It's vary between factories. But it's a really wide range.

Erik: Because there's always discussion around from a lot of the OEMs, the equipment manufacturers about how do they actually gain access to data, they have also a stake in this data, right? This can be useful for their R&D. They can realize that certain functionalities are not working appropriately, and so forth. I assume the customers have access to this data, so maybe you own the data, so maybe you don't even have access to the data? Do OEMs ever come to you, and say, hey, can we work out a deal where we can actually gain some access to the data that you're getting around our systems so that we can maybe bring that back into our R&D processes and improve the performance of our equipment? Or is that a conversation that's come up?

Aviran: We had few opportunities with a few air compressors manufacturers that we did some tests, and we thought about it. I was most mostly on the beginning of the company, so the first day, maybe two years. But we discovered that at the end of the day, we want to stay neutral, and we don't want to be colored by one manufacturer. We are fully independent and flexible.

Erik: Also, from business perspective, then you just have one customer group, it's very clear who you're servicing?

Aviran: Exactly.

Erik Okay, you’re a focused man. I like that.

Aviran: Yeah, it's very hard to stay focused. You have so many opportunities. I had the opportunity, I think it was three months ago, was a deal on the table with a factory that has 50 sites. And sometimes people get through your pilot, and they want to try and buy. They didn't ask for that. They want just to pay for the first year like just a regular customer. But we said no, because I think one of the things that we very try to do is keep it on regular basis is to be in connection with our R&D team, working with also very close with my partners.

So one of the things that they said, when I brought with this business opportunity, and again, it's a huge opportunity, the product that they wanted was actually on premise. So, we are cloud based company, SaaS and everything. They agree to pay SaaS, but they want the solution to be on premise. We can talk another hour about security on IoT. It's a whole other subject. But for us, fortunately, we said no, because make our solution to be on premise, it's a fundamental change. And we are really, really don't want to do it right now because, again, we want to stay focused and leverage our product. It's a private company, not the service company.

Erik: Okay, because then you get into a big R&D project for this one customer, and then the trend is towards cloud in SaaS in any case, so that's probably something where you have a business for the next X number of years. But in any case, you're already providing the solution that the market is trending towards.

Aviran: I would say that all the answers are correct. It could be also good opportunity, it can open up a lot of doors, but the risk was higher than the opportunity. You do the internal analyze and you say to yourself, the risk here that I take is bigger than the opportunity. And we are, in the end of the day, a small company, we’re a starter company, we can allow ourselves this risk for us.

Erik: Have you accepted any venture capital? Are you self-finance? What have you used to finance the technology development?

Aviran: Yeah, so we raised a total of $8 million for the company. Some of that was not a lot of money from the government funds and grants. We funded by the Israeli innovation authority, and also the USA Department of Energy, which I think is great because it's a recognition, it’s prestigious. And we also raise money from VC based down the valley. And our lead investor actually it's a company called Ecolab. It's huge company. It’s $60 billion. Both company they have 70,000 customers globally in more than 130 countries. They’re based in the Midwest here in Chicago. We have a greater partnership with them. They are both customers, and the partners go to market, so we also work with the business departments to develop our champion’s problem.

Erik: So why don't we wrap up here with one or two walk through? Let's say, starting from the initial conversation, who are you talking to and then what does it look like? And if you can get into some of the details around how much time it takes to get up, not just to install the system but also for the users to get up and running on it? Choose a common scenario, and then walk us through the process.

Aviran: I will give, like a real example from life. I think is the best examples that you can give. So, well, usually what happens we’re talking with a factory level, or it could be also with the corporate level. It really depends on the opportunity. But it could be bottom up or top down. If it's [inaudible 36:39], for example, it's on the factory level, the on-site level, so you have maintenance managers, you have facility engineers, you have the people that it's actually on site, and they looking for solution to reduce energy. Maybe they have like a big downtime in the factory lately and they want to upgrade their control strategy or solution.

So what we do we do a technical visit. We or our partners, we analyze and do the initial qualification of the factory, the size, the type, the complexity. Then what we do with we just installed the system. The installation is less than two days, then we do three weeks or four weeks off the baseline. So we learn the patterns of the factory. We learn what is the usage, what is the common practice that they have in the factory. We analyze all this data.

Again, we take data from the air compressors, from the controllers, from piper sensors, everywhere, also the tonnage of the factory. So we take all bunch of data, analyze that, and after three weeks, we provide the first Ecoplant monthly report to the factory, then of course, with the potential savings estimation, and everything. If we decided to move forward, so we move forward to a full SaaS contract, usually, it's between 3-5 years. From there, it's an annual subscription. We build, like the annual program for the factory, how to save and also activate our dynamic controller optimization, which means that we are controlling the equipment, changing parameters, turn on, turn off all of this through our AI engine.

And then of course, you know, after we have success with a site, so we do it also as a corporate initiative. It could be sometimes just through the corporate, so it's already corporate initiative. But this is another way to go to the corporate.

Erik: So this initial month or so, so during the assessment, the initial deployment, and then running this three week data processing, I suppose that's a paid pilot?

Aviran: Also know for the factory, it's really, really good experience to get a taste of what we do just the first month. And it's also like a paperwork. So you can actually take this document, and you can do whatever you like. It's like a report. It's like a study of your system. Of course, what we are going to do here is to establish a long term relationship and it says, okay, this is just a study, we can also take this understandings or what we just saw from the system and make it into an action, from recommendation to outcome.

Erik: So let's say that a customer says, okay, this is great, let's sign the contract, let's do the full deployment. I suppose the data, the analytics recording, you're able to activate immediately. Is there any longer training time required before you are able to implement the control processes? And you find that customers usually have no problem providing control?

Aviran: Again, you have like other solutions that already do a control, but they don't do dynamic control. So, after this three weeks, immediately, we can just activate the engine. We have, we have features, it's an optional, of course, for factories that can actually have something like semi-automatic, which means we will provide all the recommendations, and you can just press here, okay to activate the actions. But most of the factories, they really want peace of mind, they don't want to deal with air compressor.

Erik: Yeah, it's a great asset class to be working with. It's like everybody needs it. It's a significant cost. But it's not something that's directly related to the quality of the output. It's just a cost center. You don't have to worry too much about people worried that you're going to impact the quality of the outcome product, that makes maybe a little bit easier here.

Aviran: Also, you have a lot of ISO requirements with compressors. So actually, our last grant from the US DOE was about air quality, compressor quality. So now we do like five pilots in the US in food and beverage factories, which is really, really sensitive to air quality, they need their compressor to be very dry, and clean from particles and oil in the air. So part of the things that we are providing is a package of sensors that measuring this air quality. And our control algorithms, we also control open and close dryers. So dryer is a part of the compressed air process. And we also provide the factory, the way to protect and reduce the risk of having bad air quality.

Erik: So what is next for you guys? Are there any new product developments or technology developments that are on the short term roadmap for you? Or is it just you already have the product, and now you're just in ramp up mode? What is next on the horizon for you?

Aviran: In the short term, it's a lot of a ramp up and just acquiring more customers mostly in the US. US is our main focus now. Yes, so we are getting new customers and expand our offering to air compressors and specifically in a way expand to other customers.

Mid-term, long-term strategy, also, we already did, but it's something that we are now building. So it's a complete platform for the whole utilities, the whole infrastructure. So we will bring our dynamic control solution also to the water wall, so the chillers and pumps. Other utilities, according to the USDA, all the utilities together is equal to 70% of the entire energy in factories. So now we're dealing with 30%, 35%, which is half. But our goal is to get to 70%, maybe 80% of the energy in the factory to control it better, and to reduce energy and CO2.

And I must say personally, I'm proud to be part of the company that do something good to the world. It's great and fun to develop a chat applications, which is very important and make your life maybe easier. But again, we try to make the world greener and better. And this is a part of what Ecoplant does by just solving a specific problem that the industry has.

Erik: Well, I wish you success here. I mean, it sounds like you're already having plenty of it. But definitely you're doing something that's very important and it sounds like you're building a great business along the way. So wish you continue to success. How could our listeners reach out to you? What's the best way, is it on LinkedIn? Is there a general email they should reach out to? Is it just your website? What's your preference?

Aviran: So you can just simply send me an email. I'm getting emails in there. I like opportunities. So if you have like interesting opportunity, just reach out to me. LinkedIn would be good. Website, everything works.

Erik: We'll put all those into the show notes. Well, thank you again for taking the time to to discuss with us. I think it's a great business and yeah, I really appreciate you taking time to walk us through the technology and the model.

Aviran: Thank you very much, Erik and have a good luck. Thank you.

Erik: Thanks for tuning in to another edition of the industrial IoT spotlight. Don't forget to follow us on Twitter at IotoneHQ, and to check out our database of case studies on IoTONE.com. If you have unique insight or a project deployment story to share, we'd love to feature you on a future edition. Write us at erik.walenza@IoTone.com.