Lola Vision builds chips and software to speed up AI on devices

Tayo Adesanya spent almost 12 years working with microchips and AI processors, mainly helping large manufacturers decide which chips to use in their hardware — time he says gave him early insight into where demand in the AI computing market was headed. In 2024, he launched Lola Vision Systems, an AI infrastructure company that builds software and chips for running AI models on devices.

The company’s core product is a “compiler toolchain,” software that translates an AI model into instructions a specific chip can run. Adesanya says that bottleneck is massive: 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, aiming to automate more of the process. A client supplies its code and the AI model it wants to use — custom-built or open source — and the software translates both into instructions the client’s chip can execute.

Adesanya argues speed is only part of the benefit: faster setup gives aerospace and other mission-critical companies more time to run more accurate models on their own data, at 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 offering an alternative to Nvidia’s technology for running AI on devices. Adesanya says many companies instead start with Nvidia’s Jetson modules or open-source AI models, which often break or run poorly right out of the box, forcing teams to spend days or weeks just getting them running and more weeks debugging. Even then, he says, power consumption often blows edge-computing budgets, or the board can’t deliver enough compute for the models the product actually needs — leaving recognition models lagging behind targets or misreading objects.

A dozen corporate customers have signed letters expressing interest in buying Lola Vision’s chips once they are available, and the company already has one signed customer. It has also partnered with SCALE, a microelectronics workforce-development program, to work with more semiconductor labs. To generate revenue sooner, Adesanya said, the company will now license its software on existing hardware rather than waiting for its own chips. Lola Vision has raised just over $1 million in total funding to date, and was selected for this year’s TechCrunch Battlefield 200, a program for 200 startups.

Why it matters

Lola Vision’s bet is that as more industries push AI models onto cameras, drones and other edge devices, the software layer that gets a model running on a given chip — not just the chip itself — becomes the harder, more valuable problem to solve.