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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. \u201cStarting Lola Vision Systems was a bet on where the world was headed and what I was seeing,\u201d he said.<\/p>\n
In 2024, he launched Lola Vision Systems<\/a>, 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 \u201ccompiler toolchain,\u201d and he says it is a massive bottleneck: Manually setting up an AI model on new hardware can take \u201croughly 200 hours\u201d 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\u2019s chip can execute.<\/p>\n \u201cSpeed is only part of it,\u201d Adesanya said. He explained that faster setup gives aerospace and \u201cother mission-critical companies\u201d time to \u201crun more accurate models on their own data, at a lower power.\u201d<\/p>\n \u201cFor these customers,\u201d he said, \u201caccuracy and reliability aren\u2019t nice to have. They determine whether a product passes regulatory review and whether it works reliably in the field.\u201d<\/p>\n Lola Vision, based in Washington, D.C., is one of several startups trying to offer an alternative to Nvidia\u2019s technology for running AI on devices. Right now, Adesanya said, many companies start with Nvidia\u2019s Jetson, a line of compact computing modules for running AI on devices, or with open source AI models. Adesanya claimed these \u201coften 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.\u201d<\/p>\n \u201cEven then,\u201d he continued, \u201cpower consumption often blows edge computing budgets, or the board can\u2019t 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.\u201d (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.)<\/p>\n According to the company, a dozen corporate customers have signed letters expressing interest in buying Lola Vision\u2019s 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. \u201cTo get revenue sooner, we will now license our software on existing hardware,\u201d 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.<\/p>\n Lola Vision was selected for this year\u2019s TechCrunch Startup Battlefield 200<\/a>, a group of 200 startups chosen for the program. \u201cTechCrunch was a favorite when I was a student at Purdue,\u201d 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 Startup Battlefield and get the company in front of a wider audience.<\/p>\n As for what he\u2019s most excited about when it comes to the event, it\u2019s \u201cmaking meaningful connections and learning as much as I can about what\u2019s happening in and around our space,\u201d he said. \u201cAnd, to be direct, I\u2019m looking forward to investors writing checks.\u201d<\/p>\n To learn more about Lola Vision Systems and dozens of other highly vetted startups that are joining us at TechCrunch Disrupt<\/a> next week (along with the VCs coming to check them out), join us in San Francisco<\/a>, October 13-15.<\/p>\n<\/div>\n When you purchase through links in our articles, we may earn a small commission<\/a>. This doesn\u2019t affect our editorial independence.<\/em><\/p>\n