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This Company Uses Algorithms to Predict When Industrial Tools Will Fail


Screen Shot 2018-02-22 at 3.46.42 PM
Photo Courtesy Predikto

Predikto has a unique mission as a heavy industry predictive analysis startup.

When explaining what the company accomplishes, the company's CEO and co-founder, Mario Montag, began by bringing us to the past.

"In the industrial world — think aviation, rail; think heavy machinery —  [employers] traditionally understood the health of their equipment from an ... engine," he said. It was an easy way for workers to gauge how and when something was off; the temperature would run up or it wouldn't turn on.

As technology advanced and industrial tools became more sophisticated, so did the variables that could ultimately lead to one of these tool's failure. While engineers could develop algorithms that could precisely predict said failures, it would always require an extensive time commitment.

"Once they've failed once or multiple times, then they're realizing, 'I cant do this the traditional way, with humans.' And that’s where we come in."

"One human could probably create five models a year," Montag said. "Software automates the process of creating that model; one of our data scientists could create 5,000 a year instead of five."

And that, in essence, is what Predikto does: "makes software that analyzes the data generated by its clients to study the performance of their industrial equipment to reduce asset failures." It offers assessment, data, implementation and consulting services, as well.

For example: Danish business conglomerate Maersk, a customer of Predikto, "is one of the largest shipping companies in the world," Montag continued. "It has a million assets globally ... [and] the amount of variability is massive. You would need a gazillion data scientists and that’s not possible."

Enter Predikto. "Industrial companies have so much data but they suck at creating insights from them," Montag said. "How can we help them?"

Growing the company into one that could do just that took a bit of work.

"I have a lot of scars; I’ve lost a lot of hair," Montag joked. Since the company's debut five years ago "around a an idea table," Predikto has raised $8 million dollars and grew to a team of 20. Additionally, it rose to become one of the Signature companies at Atlanta incubator, the ATDC. While it's headquartered in Atlanta, it has staff and operations in The Netherlands, London and Dallas, Texas.

That doesn't mean that the company's success wasn't without obstacles. "One of the biggest challenges that we have [is customers asking], 'Why Predikto?" Montag said. "There's a ton of companies marketing this stuff. Right? So, every major consulting company in the word, IBM and their Watson, GE, Siemens, they're all building solutions to do this stuff. But GE is a customer, Siemens is a customer; we love IBM."

Picking Predikto, he said, comes down to this: automation. While other companies are doing the same work, they're doing it manually. "Once they've failed once or multiple times, then they're realizing, 'I can't do this the traditional way, with humans.' And that’s where we come in."

This unique work has brought a host of opportunities for Predikto. On the docket? "We’re really excited about this new entrance into aviation for us," Montag said. "We’re working with one of the largest turbine manufacturers right now, and start working with the second in the next 60 days. We will have two of [the] largest [turbine manufacturers] in the world, which is insane for a 20-person startup."

Montag added that there's stirrings around using Predikto's software in non-industrial fields, like health care.

While the positive reception of Predikto's technology is certainly a boon, Montag emphasizes that an exceptional company culture has been and is integral to its success. "We're incredibly diverse, in every sense of the word," he said. "We really encourage each other's uniqueness and differences. ... Our first company value is, 'our people come first.' It’s not the customer. It's our own people."

And that, he added, makes a difference.

(Image via Wiki Commons)


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