Tayo Adesanya’s journey into microchips and AI processors started almost 12 years ago. This experience provided valuable insights into the changing needs of the AI computing market, leading him to launch Lola Vision Systems in 2024. Based in Washington, D.C., this AI infrastructure company builds software and chips for running AI models on devices.
The standout feature of Lola Vision is its software, which Adesanya refers to as a “compiler toolchain.” This core product translates AI models into instructions tailored for specific chips. Traditionally, setting up an AI model on new hardware can take around 200 hours just to reach testing stages. By automating much of this process, Lola Vision aims to significantly reduce the time and effort needed for deployment. Clients can submit their code alongside the AI model they want to use – whether custom-built or open-source – and the software translates everything into executable instructions for the client’s chip.
Speed isn’t the only benefit. Adesanya points out that faster setups allow aerospace and other mission-critical companies to run more accurate models on their data while using less power. This efficiency is vital in a landscape where many companies currently depend on NVIDIA’s Jetson modules for AI deployment. While these solutions are popular, Adesanya notes that many users face challenges right from the start. Teams often spend days or weeks getting the models operational, followed by even more weeks debugging until the models are usable.
Adesanya also noted that power consumption often blows edge computing budgets, or the board can’t deliver enough compute for the medium to large models a product actually needs – causing recognition models to lag behind targets or misread objects. (Recognition models are AI systems that identify objects.)
Lola Vision has already attracted interest from a dozen corporate clients, with one signed customer on board. To accelerate revenue growth, the company plans to license its software for use on existing hardware while developing its own chips. So far, Lola Vision has raised just over $1 million in total funding to date, reflecting early investor confidence in its vision.
Additionally, Lola Vision was selected for the TechCrunch Battlefield 200, a program highlighting 200 promising startups. Adesanya, who admired TechCrunch while studying at Purdue, sees this as a chance to expand the company’s reach and connect with potential investors. He expressed enthusiasm about forging meaningful connections and is looking forward to investors “writing checks.” This candid approach captures the dynamic environment where startups like Lola Vision are working to innovate and challenge the status quo in AI model deployment.
