We had a candid session with Mario Montag, CEO and Co-Founder at Predikto, discussing their primary areas of focus, how they can offer superior results compared to custom solutions, challenges of data integration, why predictive maintenance is crucial for big industrials, transportation and logistics companies. Below you can find our podcast edited for clarity and brevity. To have a background on predictive maintenance before diving into details, you can read our article on predictive maintenance.
Cem: What are the big challenges you are facing with vendors? What are the companies that you are finding that are easy to work with? What sets you apart from competition? I guess these are the typical questions you get.
Mario: We are exclusively focused on big industrial transportation, primarily equipment and helping them move unplanned maintenance to planned maintenance. So, obviously, these big industrials have years of experience maintaining their equipment. If they have any predictive analytics it’s usually a rules-based engine. So, the expert knows that if the temperature of the motor goes above 500, something’s wrong, right? There’s a rule built for that and we’re taking a data AI based approach through machine learning algorithms. There’s a lot of companies out there that are messaging that way. You can, you know, buy Microsoft Azure which comes with ML libraries and then other things. What makes us different is that we have automated about 80% of the process of creating that actual machine learning algorithm. So, the process of creating features, we do that automatically in a massive scale. So, in a deployment, we might create 30,000 features. Normal data scientist would create maybe 50 or 100 features right with the data sets. We massively scale that and we automatically score those features and we find the top 100 or 500 features for that particular piece of equipment in that particular environment. We have every known modeling technique out on the planet plus a few we have for them that we’ve created. We’re heavy users of classification algorithms that predict if the failure will or will not happen in the next day to 30 days. So, for a customer that has already hired IBM and failed, has a lot of data and wants to create a lot of algorithms, the manual approach doesn’t scale very easily. So, that is our sweet spot. For us, customers are large industrials like GE is a customer and we are about to start with two manufacturers of aircraft engines, turbines. They have very sophisticated smart teams and scientists but they just can’t scale their analytics with custom approaches and our software is able to do this right. We go from project kickoff to go live about 90 days.
Cem: That includes of course data integration as well.
Mario: There is no magic to that. The data ETL is a challenge. we’ve built our own ETL engine that enables us to go incredibly fast. It’s purposely built for preparing that data for pancaking that and preparing it for machine learning. In a 90-day deployment, the first 30 are the data ETL
consolidation into our environment. We use elastic search as the technology in our platform where that data is stored. The second month is the machine learning automation, the algorithm creation. We call that the max engine, and then the third month of a deployment is the configuration of Predikto Maintain. It’s a front-end GUI application that takes daily predictions and turns them into notifications in a way that a user can actually operationalize the real-life environment. So, that’s usually what a deployment for us looks like.
Cem: Just to understand the exact transportation setting, is it planes or trains or something else?
Mario: Yeah, we started with rail. Working both with freight locomotives diesel and electric, the German DB freight railroad system is a customer with their Bombardier electric locomotives. We then did a pilot with bullet trains as well in Germany. We then expanded to aviation with non-engine
parts like landing gears and hydraulics. Now we’re getting into the engines with two of the largest OMEs in the world. Then we landed Maersk as a customer. So, we’re working with them on cranes that load containers from the port to the ship’s. So, technically our software is not specific to just locomotives or aircrafts. We can predict anything. From a sales perspective we’re focusing on transportation first. We’re about to potentially get into wind turbines as well. Big, heavy equipment, that’s spread across the vast area, that’s kind of our sweet spot.
Cem: Maintenance is critical for optimal performance and reducing downtime. In the crane case, I guess failure is something extremely rare, right? Because then you lose the crane operator and it’s a big disaster. I mean even before predictive technologies, they could achieve operations without accidents, right? So, what is the value add there? Because I don’t know much about that business,
Mario: Great point. So, if you think about a port, ports may have twenty or twenty-five cranes, and the ships come and park next to the port and they get unloaded and loaded. If you have one of those cranes that’s not working, you reduce the speed by which you can unload and load the containers and a backlog of other vessels are waiting to come into the port. So, it creates a very big disruption in the supply chain.
Cem: Yeah, that’s very clear but what’s the frequency?
Mario: Yeah, you know, one port could have probably 800 to a thousand hours a year in downtime. It’s expensive. So, if a gantry motor does not work, the crane can’t move from left to right or the boom hoist might not able to pick up the container. I mean, it’s a complex big piece of equipment and it’s like a locomotive. It’s just performing different functions. Instead of pulling, it’s moving and carrying it has a lot of moving parts and those moving parts fail. They are exposed to the weather, near the ocean, salty air. There’s a lot of things that can go wrong with them.
Cem:Final question. This may be confidential but I wonder the pricing. If you have an idea about what you are worth to the companies and if you can be opaque about your pricing then it is a great opportunity.
Mario: Yeah, we actually tried to do value-based pricing in the beginning. Struggled significantly with that because customers are really smart. They’ve been buying technology for years. Value-based pricing naturally creates opaqueness and lack of transparency and that was not working for us. So, our price is pretty straightforward, we charge 250 K dollars a year for the base platform. Then we charge per prediction endpoint. So, depending on how many parts you want us to monitor and how many locomotives or cranes or engines you have, you multiply the two and that gives you the total number of prediction endpoints. That allows us to start small and grow with the customer as their needs and their use of a software grows.
Cem: You were extremely open. Thank you for your time.