Tayo Adesanya spent nearly twelve years working with microchips and processing hardware before launching Lola Vision Systems in 2023. Through his work helping large hardware manufacturers evaluate silicon options, Adesanya recognized a massive bottleneck in the industry: software engineers struggled to translate complex machine learning models into code that specialized microchips could actually execute. Setting up software models on edge silicon manually often consumes roughly 200 hours per build, creating expensive delays for hardware startups.

To solve this compilation bottleneck, Washington, D.C.-based Lola Vision Systems built a specialized software translation layer that acts as a compiler to-chain. The tool automatically takes a client’s source code and software model, translates it into execution instructions, and optimizes the output specifically for the client’s destination chip. The company is also designing its own custom semiconductor chips to offer a complete hardware and software package.

Automating the translation process delivers far more than just time savings. For aerospace defense contractors, robotics teams, and industrial hardware builders, running models locally on edge devices requires strict power limits and low execution latency. Lola Vision Systems gives technical teams the ability to run more complex models on their own hardware using lower power, ensuring reliable operation in mission-critical environments where cloud connectivity is unavailable or unsafe.

Many hardware startups currently rely on Nvidia’s Jetson platform or open-source software packages to deploy models onto edge silicon. However, Adesanya pointed out that off-the-shelf software tools frequently break down, forcing engineering teams to spend weeks debugging code before getting hardware operational. Furthermore, generic edge boards often draw too much power or lack the raw processing headroom needed to run medium and large computer vision models effectively.

Lola Vision Systems focuses heavily on computer vision and object recognition applications, such as identifying targets on autonomous drones or tracking camera feeds on edge devices. The startup has raised just over $1 million in total funding and already secured its first paying customer. Additionally, the team partnered with SCALE, a microelectronics initiative directed by Purdue University, to gain access to advanced chip design labs. While custom silicon remains in development, the team licenses its software layer to generate early commercial revenue.

Selected as part of the TechCrunch Startup Battlefield 200 cohort, Lola Vision Systems represents a growing trend toward specialized edge computing solutions. As autonomous robotics, drone platforms, and smart industrial sensors proliferate, hardware manufacturers cannot rely on power-hungry cloud data centers for real-time processing. Providing direct software-to-silicon translation tools lets hardware developers field smart, responsive devices without spending months writing custom low-level code.