Manufacturers face a dizzying array of potential problems surrounding the machines they produce, and pinpointing problems is a challenge. This is not only nice to know. It is crucial information, often manually tracked these days by human auditors in spreadsheets. In some cases, not understanding when there is a faulty part can lead to costly recalls and, in the most extreme cases, deaths and lawsuits.
Enter Axion beam, an early-stage startup that uses machine learning to spot these issues in unstructured data to get a picture of potential problems before they get out of hand. Today, the company announced a healthy $7.5 million seed round.
“What we’ve done is build a new artificial intelligence platform that helps manufacturers get ahead of their big risks, like recalls, by tapping into unstructured data and synthesizing it in new ways that haven’t really been touched until now,” says Axion Ray co. founder and CEO Daniel First told businessroundups.org.
He says the unstructured data comes from human users and sets his company apart from those that came before him.
“With traditional machine learning, and many of the companies that have come before us, the focus in manufacturing has largely been on highly structured datasets, such as installing cameras on the production line or looking at sensor data to predict an engine failure. “
“But what’s exciting about Axion is that we can leverage massive amounts of unstructured data, for example [chatter] comes from service or dealer networks, where most of the data is technician observations, and is found in comments and issues and troubleshooting data that comes from humans.
First worked as a McKinsey consultant for several years before launching the company, seeing first-hand how manufacturers struggled to spot potential problems before they actually blew on them. He also noted that engineers working on these machines saw problems months before the companies realized there was a broader problem, and the idea for Axion Ray began to take shape.
“It became clear that there was a huge opportunity to enable the detection and flagging of the earliest warning indicators, and that could help people detect risks months earlier.”
The company was founded in 2021 and already works with customers such as Boeing, Penn Engineering and Cummins. First didn’t want to share the number of customers just yet, but it’s clear that some big players are interested in what his company is doing.
With nearly 20 employees, the startup mainly hires engineers and employees with a specialty in machine learning. First says building a diverse workforce has been a priority from the start.
“Even though we are a small team, we have dedicated full-time colleagues who ensure we build diverse candidate pipelines and hiring practices from day one. We were also thrilled to partner with Inspired Capital as our co-lead investor, one of the largest female-led venture capital funds in the country,” he said.
Today’s $7.5 million investment was co-led by Inspired and Amplo along with Boeing, Tinicum Venture Partners and industry angels.
