When a faulty CrowdStrike update brought down airports, 911 call centers and hospitals last month, it showed how a defective update could impact critical infrastructure. Now imagine that this update was for something like an autonomous vehicle or a warehouse robot, and the implications of a bad update could be even more severe.
Trace Machina, an early-stage startup, is trying to prevent such scenarios with advanced simulation software that enables developers to test updates in a more realistic simulated environment. The company emerged from stealth on Thursday, announcing a $4.7 million seed investment and an open source tool called NativeLink.
CEO and co-founder Marcus Eagan says his company is developing a native, Rust-based system to help test and validate software for autonomous systems like self-driving cars and warehouse automation equipment before these systems are deployed in the real world.
“The way we solve that is by providing a native link between developers and their autonomous vision,” Eagan told TechCrunch. That is precisely why the company’s first product is called NativeLink.
“When developers go from working on web apps to working on robots, it becomes obvious that the existing developer toolkit with Docker, Kubernetes, etc. does not suffice. Engineers need to be able to run experiments and tests on the local hardware directly,” he said.
“NativeLink bridges that gap and provides engineers with a staging environment that enables them to run simulations in resource-constrained environments like an embedded Nvidia GPU chip that are difficult to source for robots, self-driving cars and edge devices.”
Eagan says that previously companies had to build these environments themselves and that limited them to well-funded self-driving car companies or hyper scalers like Google. He wanted to build a system that is as close to the hardware as possible, what he calls “being close to the metal,” and make it accessible to any company.
“There’s a lot of people who’ve gone down this path, but none of them can run with direct hardware access. There’s always been this virtualized layer, this abstraction layer, that frankly made it easier for those companies to build those systems and iterate. We just had to pay the tax of being close to the metal,” he said.
Eagan’s background includes stints at MongoDB, where he helped develop Atlas Vector Search, the company’s first AI product. His co-founder, Nathan Bruer, worked at Google X, the company’s experimental moonshot project center, and also helped build autonomous vehicles at Toyota Institute.
Eagan, who is Black, has had to deal with racism in his career, but he remains focused on building his company, regardless. “I have had to deal with racism and I don’t care. I’m so focused on my goal. Nobody can stop me, nobody can dictate how things are going to go. And I’m very grateful for that from that vantage point because a lot of people who look like me don’t have that freedom,” he said.
He has also had to overcome obstacles beyond racism in his life. He was in a severe car accident when he was a teen that left him critically injured, unable to walk or talk. But he was able to recover, go to college, become an engineer and eventually begin building this startup.
The $4.7 million seed was led by Wellington Management with participation from Samsung Next, Sequoia Capital Scout Fund, Green Bay Ventures and Verissimo Ventures, along with several prominent industry angels.