The story begins almost 12 years ago, when Tayo Adesanya started a career working with microchips and AI processors. He mainly helped large manufacturers decide which chips to use in their hardware. Those years, he told TechCrunch, gave him early insight into where demand in the AI computing market was headed. “Starting Lola Vision Systems was a bet on where the world was headed and what I was seeing,” he said.
In 2024, he launched Lola Vision Systems, an AI infrastructure company that builds software and chips for running AI models on devices. Its core product is software that translates AI models into instructions a specific chip can run. Adesanya calls this software a “compiler toolchain,” and he says it is a massive bottleneck: manually setting up an AI model on new hardware can take “roughly 200 hours” just to begin testing. Lola Vision says it has rebuilt that software layer and is also developing its own semiconductor chips, with the goal of automating more of the process. A client provides its code and the AI model it wants to use, whether custom-built or open source, and the software translates both into instructions the client’s chip can execute.
“Speed is only part of it,” Adesanya said. He explained that faster setup gives aerospace and “other mission-critical companies” time to “run more accurate models on their own data, at a lower power.”
“For these customers,” he said, “accuracy and reliability aren’t nice to have. They determine whether a product passes regulatory review and whether it works reliably in the field.”
Lola Vision, based in Washington, D.C., is one of several startups trying to offer an alternative to NVIDIA’s technology for running AI on devices. Right now, Adesanya said, many companies start with Nvidia’s Jetson, a line of compact computing modules for running AI on devices, or with open-source AI models. Adesanya claimed these “often break or run poorly out of the box, so teams spend days or weeks getting them to run at all, then even more weeks debugging until the models are usable.”
“Even then,” he continued, “power consumption often blows edge computing budgets, or the board can’t deliver enough compute for the medium to large models the product actually needs to run successfully. This leads to the recognition models lagging behind targets or misreading objects.” (Edge computing means running AI directly on a device, such as a camera or drone, rather than in a remote data center. Recognition models are AI systems that identify objects.)
According to the company, a dozen corporate customers have signed letters expressing interest in buying Lola Vision’s chips once they are available, and it already has one signed customer. It has also partnered with SCALE, a microelectronics workforce development program, to work with more semiconductor labs. “To get revenue sooner, we will now license our software on existing hardware,” Adesanya said. (In other words, rather than waiting for its own chips, the company will let customers pay to use its software on chips that already exist.) He added that the company has raised just over $1 million in total funding to date.
Lola Vision was selected for this year’s TechCrunch Battlefield 200, a group of 200 startups chosen for the program. “TechCrunch was a favorite when I was a student at Purdue,” he said. After about a year of building the product and signing its first customer, he said, he felt it was time to apply to Battlefield and get the company in front of a wider audience.
As for what he’s most excited about when it comes to the event, it’s “making meaningful connections and learning as much as I can about what’s happening in and around our space,” he said. “And, to be direct, I’m looking forward to investors writing checks.”
To learn more about Lola Vision Systems and dozens of other highly vetted startups that are joining us at TechCrunch DIsrupt next week (along with the VCs coming to check them out), join us in San Francisco next week, October 13-15.