Neil Patel

I hope you enjoy reading this blog post.

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Ira Cohen’s SaaS startup has already raised $70M and is expected to attract even more capital from investors this year. Anodot has acquired funding from top-tier investors like Redline Capital, Alicom, Softbank Ventures Asia, and Intel Capital.

In this episode you will learn:

  • The unfortunate politics of startup fundraising
  • The costs and time loss of failing to focus intensely
  • Why their tech startup has a VP of Fish Care

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    About Ira Cohen:

    Ira Cohen is a co-founder and chief data scientist at Anodot, where he’s responsible for developing and inventing the company’s real-time multivariate anomaly detection algorithms that work with millions of time series signals. He holds a Ph.D. in machine learning from the University of Illinois at Urbana-Champaign and has over 12 years of industry experience.

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    Connect with Ira Cohen:

    Read the Full Transcription of the Interview:

    Alejandro: Alrighty. Hello everyone, and welcome to the DealMakers show. I’m very excited about our guest today; obviously, Startup Nation and there are incredible founders coming from there. The discipline and the mindset are really incredible. We’re going to be learning a lot about building, scaling, financing, and then going from corporate into startups. I think there’s a lot to unpack in the episode that we have today, so without further ado, let’s welcome our guest today. Ira Cohen, welcome to the show.

    Ira Cohen: Thank you for having me, Alejandro. It’s great to be here.

    Alejandro: So born in Israel. When we’re talking about your upbringings, that definitely entailed moving quite a bit for the first 18 years of your life, so tell us a little bit about growing up.

    Ira Cohen: I was born in Tel Aviv, and one of my parents is an engineer, and my mother is a mathematician. In the early days, we were in the Tel Aviv area, so the center of Israel where most people live. But at some point, they decided to move to the south of Israel, [2:24], a place that had a movie on it called Turn Left at the End of the World, so it was a small town. That’s where I grew up most of the time. But they had the bug of moving, so we moved, at some point, to the U.S. for two years and lived in the LA area, which is very different from where I grew up. Then we went back to Israel to the desert again. Then we moved again to a greener place. So I had encountered a lot of different places and very different environments in my first 18 years.

    Alejandro: I’m sure that was difficult as a child because you had to make relationships from the start; you had to deal with uncertainty. I’ve seen this with other entrepreneurs, too, that had a similar upbringing that, to a certain degree it shapes up the way that you are able to deal and analyze uncertainty. Do you think that played a role in you being able to become an entrepreneur later and how you deal with uncertain situations?

    Ira Cohen: Definitely, because my life was changing so much. I had to adapt and know how to adapt to very different environments, very different types of friends, and very different types of even cultures. I do believe it helped me a lot to see people for what they are and not who they are, and that helps a lot also as you move around.

    Alejandro: You did the military service, which is mandatory there in Israel. Then after that, you did your engineering degree. But then you decided to pack the bags and come to the U.S. Why did you decide to do that?

    Ira Cohen: I don’t know. Ever since I was a child, and we lived in LA, I had this dream of going to CalTech. That was a bug that came to my head at some point and never left. When I finished my undergrad, I said, “Okay. Let’s get that bug out of my head. Oddly enough, I never applied to CalTech. I actually applied to other universities in the U.S. and was lucky to get accepted to the University of Illinois at Urbana-Champaign. That’s where I went. I knew that I wanted to get an advanced degree. I wasn’t content with just an engineering degree. I felt that changing the atmosphere of where you studied—in Israel, it’s too small for a big change. Even though we have multiple universities there, the mentality is exactly the same. I wanted to experience a different mentality and a different form of learning, which the U.S. provided. But the main reason, if I’m honest, is that bug that went into my head sometime early on in my life that I should go to a top-notch U.S. university like CalTech.

    Alejandro: Then, after doing your Masters, you joined Hewlett Packard, and you spent quite a bit of time at Hewlett Packard. First, here in the U.S., and then you went back to Israel and were building teams. You were before all this craziness around machine learning and AI. Now everything is AI and machine learning. So when you were building those teams, and you were seeing things around you, first and foremost, what did you learn from working at such a big organization like Hewlett Packard? And what was that experience of seeing those trends around the incredible growth around the talk, and conversations, and machine learning, and AI?

    Ira Cohen: Yeah. It was quite a ride. I moved to machine learning. My undergrad was computer engineering. I worked a lot of single processing and image processing. When I moved to U of C, I thought I would be working on image processing, very theoretical. But then I got to the U.S. and started working on problems that needed machine learning, and I got the whole notion of analyzing data in the way that machine learning does with these models. I don’t know why, but it resonated with me internally, and I really like the field. I like to apply it to images and videos. I went into it because I love data analysis. Like you said, there was pretty much no market for it, but you do what you like when you do the Ph.D. That’s where it took me. As I started my professional career, both at the beginning of HP research labs, we started working on applying machine learning to various domains that HP was involved in. The reactions were always, “What is this? This is magic. This is pixie dust. This is not real,” even from fellow researchers that came out of Berkeley and had some understanding about machine learning, it always sounded like magic. I would say, “This is not magic. This is data talking. It’s taking data and making it talk instead of just data sitting around.” As time progressed, when I went back to Israel, I went to the software division and went to the executive VP there, and I told him, “I want to come and form a machine learning team, a team of people that know machine learning so we can put these capabilities into the product.” His question was, at the time, “What is machine learning? We don’t do any educational software for people to learn from. So what does this have to do with me?” That was his answer at the time, and I had to explain that machine learning is not educational software teaching people but rather exactly what it was. This was in 2009. It advanced quickly from there. It was so great to see how things progressed from being very theoretical and just being experimental universities and only a few select companies doing it like Google, Amazon, Microsoft, and others to the industries starting first to embrace it or trying to understand what it is, and then seeing the applications of it. Today, it feels like a lifetime ago where I had to explain what machine learning is. Now I have to actually explain, most of the time, what machine learning cannot do yet because people expect it to be the solution for everything.

    Alejandro: Absolutely. In your case, about seven years ago, you decided that it was time to pack the bags and give your notice. We’re talking about at least 12 years that you spent at HP? Why, after so long, did you give your notice? What happened?

    Ira Cohen: First, the HP experience, because I moved around somewhat, it felt like a few jobs. It didn’t feel like one job. You asked about the enterprise. That’s the advantage of being in a very large company: you can actually move within the company and do something completely new and not leave the company but feel like you are renewing yourself and learning. I felt that I had the glass ceiling in terms of what I could provide and what I could learn. As a researcher, I learned a lot about what works in terms of industrial research. What can you do as an industrial researcher that can make an impact, and what doesn’t work? That’s one of the reasons why I decided to transfer to the software division because I wanted to be much closer to the products themselves to make an impact. I felt, as a researcher, I could do a lot of great things, but it’s super hard to push them into the products. As I moved closer to the products, there were some successes of trying to push things into the various products, but I felt like a lot of times I had to add capabilities to an existing product, and doing that with machine learning, a lot of times hits a lot of barriers. I’ll give you an example. I would have endless discussions and arguments with the DBA of one of the products asking him, “I need this feature to work in the machine learning model. I need to query three months’ worth of data.” He would laugh me off and say, “No way. I can’t let the product do that. It would crash.” Then, as a machine learning developer or expert, I would have to start creating suboptimal algorithms. So the frustration was you come in with what you know, but then you have to make a lot of adjustments and a lot of compromises. In the end, you don’t get the optimal solution. When building your own company with your own product, you’re not limited to that. And, actually, we build the product from the ground up based on all the experiences that they had from what worked and what didn’t work. As a researcher, you have that entrepreneurial mindset because you always try to create new things that were not done before because, otherwise, why be a researcher? So that came naturally to me. All I needed was an adjustment of creating the business around it, the business framework around all of these ideas. That’s a new learning experience, which I’m still enjoying quite a lot.

    Alejandro: So let’s talk about Anodot, your baby. Tell us about the band coming together, the co-founders’ meeting, and then all of you saying, “It’s time. Let’s give our notice. Let’s start this thing.”

    Ira Cohen: It’s a good story of how we started Anodot. I got a message at the end of 2013 on LinkedIn, a cold message from somebody I didn’t know, who is today the CEO and co-founder. He asked me if I wanted to meet. He did a LinkedIn friend request and then, “Let’s meet.” We met a few days later over dinner, and he told me his story. He’s a serial entrepreneur. He sold the company to Akamai a couple of years earlier. He was basically looking for his new thing. At that time, he was working for an Uber competitor that is based in Israel—just to learn more about other domains other than what he did before. He realized he had some problems around getting monitoring to work for his business. This is like an Uber app, but not Uber, and they were monitoring everything in real-time constantly, but they were missing a lot of very costly incidents because even though the data was collected in real-time, it wasn’t telling a story. Somebody had to look at the data to get the story, to get that something’s happening in some city and people are not able to register as new users or not able to call taxis, or taxi drivers are not getting the calls because of all sorts of issues whether technical or nontechnical issues. The data was in real-time, but they were finding it two weeks later, a week later, if somebody accidentally looked at the data on some dashboard. Their main monitoring capability was dashboards, so eyeballs. It drove him nuts that they were losing so much money by not finding them immediately as they were happening even though the data was available immediately. In that meeting, I told them, “Yeah, machine learning can solve this problem. I know how to solve it.” Then he brought the third co-founder, who is VPRD in our company. He knew him before. He didn’t know me before. We created a deck and a small demo and started pitching it. Six months later, we opened the office and started the company officially.

    Alejandro: What is the business model of Anodot for the people that are listening to get it?

    Ira Cohen: What we do is autonomous business monitoring, so monitoring that is based on machine learning. We’re a SaaS company mainly, even though we have an on-premise offering as well. The business model is, you monitor your business; you send your monitoring data into our system. Our system continuously analyzes it and sends you notifications and alerts about interesting things that happen in the data that could be incidents and are incidents that you should pay attention to, whether it’s your marketing team, your revenue team, your cost team, or your customer experience team, whichever team it is. The business model is based on the volumes of data that you send us. The more things that you need to monitor, it means you’re a bigger company, the more you pay us. So it’s a SaaS model in that sense.

    Alejandro: Up until now, how much capital have you guys raised to date?

    Ira Cohen: We raised around $70 million to date in three rounds. We will probably raise the next in 2022. We actually closed the last one at the end of March 2020, so right as COVID was hitting. That was the seed round. That’s in terms of the capital funds that we have so far.

    Alejandro: Very nice. The seed round happened very quickly, so why did it happen so quickly?

    Ira Cohen: It’s always hard to exactly know, but I believe the answer is that our CEO, my partner and co-founder is a serial entrepreneur. I think that makes a whole lot of difference. We were able to come, not with paying customers yet, not with a product that is fully baked yet, but rather, he and I as the experts and our third co-founder as the head of R&D, so a strong team, good story, good references. We talked to potential customers already and got their backing that this is an important problem and that this type of solution can be useful for them. That was enough for the seed round, luckily, for us. I don’t think it would have happened if I had come alone without the history of being an entrepreneur.

    Alejandro: How big was the seed round?

    Ira Cohen: At the time, it looked big to me. Now it looks pretty small. I think it was a million and a half.

    Alejandro: What happened with the Chinese investors? Because you had the opportunity early on of getting a big chunk of investment from folks in China, and you decided not to go that route. Obviously, it’s tough because, as an entrepreneur, you’re always raising money. So it’s hard to say no to money, but it turns out that in the long run, it was the right decision to make. Tell us about this story a little bit.

    Ira Cohen: We were in touch with, like you said, a Chinese company that was doing investing. We had very good relationships with them. We visited them in China and felt very good about the people there and the company. There was a lot of back-and-forth, but at the time, this was about a year and a half that we were in business, so we were starting to think about the next round. They were fast in offering an infusion of cash that we knew the company needed. We had a big dilemma. We started talking to a few other investors at the time, but it wasn’t yet mature. This was the first one, and you want to finish it and move on so you can build a business. But there were always talks and chatter around the relationships between the U.S. and China and whether it would be smart to get an investment from a Chinese company or not. We were worried about it. On the one hand, these are great people, a great company, and the fact that they are from China, we’re not political in any way. But we don’t want to hurt the business. It turns out that we said no, and the next round actually came quickly, so some of the other investors matured quickly after that. We saw, just a few months later, in one of our biggest deals that we had, if we would have taken that investment, we wouldn’t be able to get some of the big deals that we got even six months later just because there were all these constraints between the U.S. and China. So that’s the story in a nutshell behind it. I would hope that politics is removed from our business, and we wouldn’t have to think about these kinds of things, but this is reality, and we have to live by the rules of reality.

    Alejandro: Of course. In your case, it’s interesting how, even at the beginning, you were relentless about getting customers, even getting customers in [20:54]. Where is that drive coming from? It’s unbelievable.

    Ira Cohen: It was quite incredible because I never did sales. I was a researcher; I was a developer; I was a very technical person. But as a researcher and somebody who keeps pushing innovation, you always know how to talk about your stuff because you have to market yourself. Nobody can market it for you as a researcher. But it was always within the constraints of the HP company, a little bit of talks with customers, but it never actually had a lot of interactions with real customers. But somehow, there was a flip in my mind, and immediately as we started the company, I started talking about it to anybody, anywhere. I was probably obnoxious to some people, but it’s this drive that pushes you to talk to people. One time, I was sitting in a jacuzzi in the gym where I work out. Somebody sits next to me. Immediately, I started asking questions, and it actually turned out to be somebody very relevant that had the need. We were able to get him to get that company just from a conversation in a jacuzzi. After that conversation, which was in the first six months or so, I realized, “I’d better do this all the time.” So I would fly, sit in a bar, and talk to people; I would do almost any interaction, and one of our biggest deals came out of meeting an old friend that I hadn’t met for a few years, sitting on the beach with a beer. In that instance, I wasn’t planning on talking about Anodot at all, but the person brought it up, and I started describing it because I thought it wouldn’t be relevant to where that person was working. It turns out that it was actually very relevant. Six months later, we signed our biggest deal to date with a very good company, which is a very large social network company that is extremely valuable to us.

    Alejandro: Nice. Now, how does the life of a fish relate to the health of the company?

    Ira Cohen: My title is Chief Data Scientist, but also the VP Fish Care, which I put on my LinkedIn title. It turned out it had another benefit I’ll tell you at the end, but what happened is, the first week when we started the company, my younger son got a birthday gift from one of his classmates—a small fish. We had never had a fish, and I didn’t want to have a fish in the house. So I decided to bring the fish to the office and put it in the office and see what happens. Then we started creating a whole narrative around the fish about how this fish is our lucky charm and how the fish supports the engineering systems. And if the fish is not healthy, the engineering systems are not healthy, so it’s like if you monitor the fish, you know what’s going on with Anodot. The fish died several times. It went through multiple iterations. Today, we have ten fish in a large aquarium. We have a camera that is attached to that aquarium that’s tracking the fish. This is monitoring, so it goes to the Anodot system. We created a special account for what we call the Anofish account. We’re alerted if the fish are not moving as they did before or moving more than they did before. We have a lot of sympathy for the fish.

    Alejandro: Good stuff. It’s just amazing, the level of detail, and how you guys are relentless on everything, whether it’s the customers, the fish, and I love it. In terms of the vision and the mission, imagine you went to sleep tonight, as well as all of your co-founders, and all of you wake up five years from now in a world where the vision and mission of Anodot are fully realized. What does that world look like?

    Ira Cohen: I’ll say it’s from the perspective of our customers, not from our perspective, and in terms of the product. When we complete the vision of the product, let’s say it’s completed five years from now, they would have a monitoring system that also fixes all problems by itself. So it’s a completely autonomous, self-healing environment that monitors your business, and it recognizes when there is an issue of people. Let’s say I’m a gaming company—an issue of ads not being displayed in a game because of some problem with interfaces with Facebook. It will detect it, and we’ll fix it, and deploy the fix or it does the remediation, and nobody in the company will even know that it happened or need to know that it happened. So having a system that completely automates everything around monitoring, which is similar to what happens in a lot of other environments that are more traditional environments like some factories or systems that fix themselves or perform remediation themselves, so do it for the digital world as well.

    Alejandro: Got it. Now, imagine I put you into a time machine, and I’m able to bring you back seven years before the time that is now where you’re able to sit yourself and your other two co-founders down, your younger selves, and based on what you’ve experienced and all the lessons learned and everything, you’re able to share with them, one piece of advice before going at it with this business. What would that be, and why?

    Ira Cohen: The advice would be: focus and focus on the use cases that we actually solve for today. When we started, we built a capability; we built a platform with a generic capability. The directions that we tried to sell it into in the beginning were all over the place—so lack of focus. The reason they were all over the place was because this was a new capability that we had to bring to the market. We didn’t know where it would stick or whether it would bring the most value and resonate the most with the companies that we were going after. So I would tell them, “Focus, and focus on these use cases, and don’t think about anything else. Don’t bother.” I think we wasted a lot of cycles and time—not wasted. We spent a lot of cycles and time to hone in on the right things, the right people, the right companies, and the right use cases to sell for. Again, this was because we weren’t coming at it from a very narrow focus of solving narrow problems, whether a much wider problem—building a capability that can solve a lot of different problems.

    Alejandro: For the people that are listening, what is the best way for them to reach out and say hi?

    Ira Cohen: Definitely, LinkedIn. I can give my email at Anodot. [email protected]. If you want to get to the company and not specifically to me, then our website is anodot.com. I think things pop up there all the time to ask you to fill in your details. So if somebody fills in their details, they’ll get a response quickly.

    Alejandro: Amazing. Well, thank you so much for being on the DealMakers show today.

    Ira Cohen: Thank you very much for having me.

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    Neil Patel

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