Tor-Inge Eriksen, Clarify PostedMonday, March 14, 2022 Q In your opinion, what's the biggest challenge facing the automation marketplace today and in the future? A The first one I'd like to point to is that there's this gap between automation of not being in PLCs. You need a lot of control. You need to be sure that things are working the way that you want them to work. I mean, it's often critical. These are machines and things that are spinning and there's lots of ways things can go wrong. And then on the other hand, you have the software world programming, and there's high speed and the Facebook mentality of move fast and break things. These things really collide into each other both technologically but also in organizations, these are two very different backgrounds and two different kinds of teams. Finding ways where you can have a security and certainty that things will keep working the way you want it on the automation side, while maybe there's something to be gained from being able to adopt some of the speed and just the raw innovation happening on the programming side. And maybe there's a bit of learning to be at the other way as well. And just finding a good way for this to work, because I think as automation wants to move upwards, as we want to integrate automation systems with resource planning or your factory management systems, we need to find a way where these aren't two competing teams that are butting heads, but rather find good ways of working together. And there's probably compromises to be made on both sides to make that happen but it's a key thing that we need to find ways to solve. At the core of that is basically balancing security with innovation and speed and finding a good way for that to happen. Q What trend are you seeing in industrial automation right now? A The thing that we have been seeing in the last couple of years, and it's really picking up now, is companies are getting a lot more conscious about ownership of data. Just getting that out of the way, in Clarify, we really think that customer's data is customer's data. And what that means is that customers pay us to store their data, it's not ours. So whatever data you put into Clarify, you can also extract from Clarify. The customer sends us a year's worth of sensor data, well, they can read out that sensor data, and we don't either get it for them unless they want to, we don't do anything with it so that they– what they get out is different from what they sent it. And if they add, for example, in Clarify, you can add these calculations where you might take two individual time series and create formula that modifies the two and creates a third time series that that does something special. Once you've done that in Clarify, and you can visualize in Clarify, you can also export it from Clarify. If you want to use that in some third-party tool, we're not going to stop you. We encourage it. We think there's a lot of space for other products and other companies to be good at other areas of it. So, we'd rather work well with other products. I don't know how common this is now, I mean it seems to be less and less common. But I know when I used to work in industrial automation with a meat manufacturer, where you can't even get data, while you bought this piece of machinery, if you want the data coming from it, you must pay extra. And I'm not a fan of it, and I can tell you that customers really aren't as well. The other one is, there's a lot of focus on how to get data out of systems machines in general, a lot more interest in data. And I think the manufacturers and the products that make it very easy to get data out of the sensors and really work to make the data pipelining work easier are the ones who are succeeding, at least as far as we can tell. And then, I think, in general, just so much more focus on data. And with that comes a ton of new problems. How do you organize great amounts of data? Who does it? Where is it stored? When you're getting data from multiple manufacturers, how do you standardize the data and where? So that we're able to say that a temperature from one piece of machine, we can compare against another machine? What if they're coming from two different manufacturers? What if they're labeled differently? I think there's just a lot more focus and some interesting products and companies springing up that are trying to solve this. Clarify has a role to play in that. We, as a company, really see our future in working together with some key players in the industry rather than trying to be a full solution in every aspect of it. Q Talk about your SI partner program. A We provide support and instructions on how to use Clarify. We work with SIs to build new integrations to find solutions. Basically, our engineers can work with a system integrator to find solutions where the system integrator might not have software development capabilities internally or might not know exactly how to be able to solve a problem. And there we can often step in and help guide them through that process. SIs are good at seeing the value that could be gained from getting data. And using Clarify gives them a way to show that to a customer. Let's say you're working with an IoT gateway. Without the visualization software, I mean, rationally, we can all kind of deduce that, "OK, we need this gateway in order to move data from A to B." But if no one sees the data, neither A nor B, then it's hard for the end customer to remember why are we making these investments? Why are we bringing these gateways into our system and extracting data? And what Clarify gives is a tool where very quickly and in a visual way, you can start to work with data, you can gain visibility into it. It really strengthens the argument for system integrators in integrating more data. And also gives them ways of showcasing to the customer why it has value. And for the end customer, looking at your data on a regular basis really helps improve the data quality. It's easy to have data going into some bucket, no one's looking at it. And if a sensor stops sending data, or sends the wrong data, if no one's looking at it, no one's going to see it. And for some of our system integrators who provide more long-term ongoing relationships with customers, we've had some of them who are using Clarify to communicate with their customer. So, they're doing support requests, they are doing remote maintenance of systems and debugging things where otherwise you might have needed to send an engineer out in the field. Or you might have needed to install logging equipment, data loggers, and then tried to fix the problem afterwards. And we've had some really use Clarify to great effect, to help customers out in a much shorter time span. Q Talk about a project that was challenging and what you did to solve the customer's problem. A We had a system integrator come to us with a project that was a bit of a tricky project to get started. This end customer wanted a setup where they would log time series data, and their team would be able to go in and see it. Now, the problem was, this isn't for something that they do in their day-to-day business. It's a research and development project that had quite a limited budget and a time constraint. They didn't want to invest a ton of resources in getting it up. They didn't quite have the infrastructure in place already to handle it. And the system integrator was tasked with finding a solution that could be delivered in a very short amount of time and at a very low cost. And it's typically not a good combination to be looking for. We went through the project with them. And it's one of the projects I'm most happy about. It's not a giant project, it's quite a small project. But it shows one of the strengths of Clarify is the ability of us to bring in this pilot. We had them basically from talking with a system integrator to the customer being able to look at their time series data on their phones in half a day. It came in at about 10% of the price, they already had been quoted by another provider. And it's using MQ TT, which we haven't fully launched publicly but we've been testing with some select partners for about last two months. We recently saw this pilot project scale. It’s doubled in size. And it's going to increase five times size in the next couple of months. A good strength of Clarify is the ability to come in at the floor, very low cost, very short amount of time required to get it up and running. And then once you're in, you can scale, and you can do it yourself. And with our pricing structure, it's very transparent you... there's no "gotchas" at the end of it. You can see right at the beginning what is this going to cost me as I scale, which feels good to the customer to know that you're not getting tied at the end. Q How has Clarify grown and changed in the past year and what are your expectations for the company in the next 12 to 24 months? A We've basically been concentrating on the Norwegian market. We've spent a lot of time in the field, a lot of time working very, very closely with our customers to make sure that the core of the product makes sense. And so, in past years, that's where our focus has been. And then the last year we've shifted gears and said, "OK, now we have a strong core at the company, and now there's time for us to go out with it." And so, the last year we've spent time working on our self-service model and spending time with system integrators to build out the partner program that provides value to the system integrator and that enables them to provide value to the end customer. For the coming years, so the next year or two, I'd say product wise, we are working with more ways of visualizing data. We're trying to be very attentive to our customers. We're gathering feedback and looking at how they're using the product, where they're getting value, where their journey in Clarify ends, where they might have wanted to do one more thing, or add-in one more data source or do things a bit differently. And we try to choose strategically where we develop the product. So that it's really a complete product for our customers. That means more ways of visualizing data. It also means more ways of interacting with the data and with each other. Are there other kinds of context we could be adding? Are there other methods of adding it? Or, in bringing colleagues, are there other ways where teams can work with data together? We're trying to stay true to the core of the products. I don't want this sprawling product that's trying to be a bit of everything. We'd rather build something really strong in the areas where we have our strengths. And the final part, I'd say on the product side, is we're constantly looking at ways we can make it easier to integrate data into Clarify. And that's something where we're working very closely with our system integrator partners to make sure that that's possible. And then on the company side, we are really looking toward going towards the SMB market internationally. We're working to find good system integrators in key areas and key industries, to help us expand globally. Q What's unique about the software? A Clarify is very much born out of the experience that we had working in industrial automation ourselves. We were asked so many times to basically get data out of various systems. In our field, that's very often time series data, so graphs and charts. And it turns out that you often spend an inordinate amount of time doing that, which means less time to analyze that data and use it in your daily decisions. And the other thing we saw was that, in a lot of cases, you extract data, and you work on it alone. You might take a screenshot, maybe you'll draw an arrow on it and email it to your colleague, but none of the tools are really built for teams to work on this together. And we think there's a lot of value in having people with different backgrounds, or in different domains inside of the business, being able to work together. With Clarify, what we're trying to do is have it be accessible, so that anyone in an organization who has access to the data can go in and ask a colleague, "Hey, can I chime in here? I've seen this spike, what was this about?" And you can add pictures and texts and videos and documents, you just add all this context. And there's a lot of talk about data. And data, truly has great value but not by itself. You can't just gather a ton of data and automatically have some value provided. You really need to work with it. Adding that human context is a huge part of that. Q Talk about your co-founders. A There were four us. We met while studying. Three of us come from a field called engineering cybernetics. But we specialized in different areas. We speak a common language, but we have quite different areas that we're most interested in and most focused on. And then we were very lucky to have a has brought a lot of strength to our focus on the actual end customers. It's easy for engineers to get really, really interested in technology and forget that what we're doing is providing a service and value to the end customer. Having that from the very beginning has strengthened our company and the product that we build. And we're in this era where we're working both with end customers, so the guys in the field using our product, but we also need to be able to talk to in our product that works great for system integrators, who are really relying on to bring data and to bridge this, to find good cases for customers and to make the data work. Q What is the history of the company? How did it begin? A We started out as consultants, working on very different projects, just really trying to see if there was common trend throughout all the work we were doing. About 5 years ago, we gradually wound down the contracts that we had for consulting contracts and started building an organization that would be this product company. And since then, we've added 21 full-time employees. One of the things I'm most happy about is the team we've built, which I think is just a group of extraordinary people. We went from being a consultancy to becoming a product company.