Ramona gets $25M to scale multi-camera microscopy

Ramona Optics, a US-based startup working on a “massively parallel” approach to quantitative microscopy, has closed a series A round of venture finance worth $25 million.

The funding, described by CEO and co-founder Gregor Horstmeyer as the firm’s first official capital raise, will be used to scale commercial operations of its proprietary multi-camera array microscopy (MCAM) technology, which is based around 24 cameras.

“More scientists will be able to put our technology to work on the questions that matter most to them, and to the patients waiting on treatments,” wrote Horstmeyer in a LinkedIn post.

“We're powering a new, more efficient, more quantifiable way of understanding the world we live in, by measuring the very small things that can make the biggest difference.”

And in a blog post detailing the technology, the Ramona team added: "We built an instrument that watches hundreds of living experiments at once, at cellular resolution, for as long as the biology takes to unfold, and the software that turns what it sees into measurements a scientist and a model can both use."

Making massive datasets meaningful

Analogous to massively parallel computing, where multiple processors are used to optimize computation speed in a distributed system, Ramona’s MCAM architecture promises to do the same with optics, illumination, sensors, and computation to deliver the performance of a microscopy lab from a single benchtop or palm-sized unit.

The startup says that its approach, which overcomes the long-standing trade-off between field-of-view and resolution, is now available in benchtop systems for assays in multi-well plates, including its high-speed “Vireo” product for live cell imaging, and the “Kestrel” system for small model organisms.

Both systems combine imaging with embedded AI to make large-scale biological data “actionable” in real time, with the MCAM technology deemed capable of capturing up to 8 billion pixels per second. Earlier this year Vireo was short-listed for an SPIE Prism Award in the category of Biophotonic Instruments.

“For centuries, microscopes have been designed for the human eye,” explains the CEO, who founded Ramona in 2019 alongside CTO Mark Harfouche and Duke University associate professor Roarke Horstmeyer. “Our MCAM technology is built for computation - because while seeing is believing, it isn’t quantitative.

“Today, advancing drug candidates and making accurate diagnoses requires statistically meaningful data: many measurements, not just one, to make sound decisions. This fundamental shift enables us to use AI to extract insights from massive, high-definition datasets that are simply beyond the reach of human observation.”

On-the-fly analysis

The funding round, which was led by Chicago-based ARCH Venture Partners, appears to be detailed in a March 2026 filing with the US Securities & Exchange Commission (SEC) suggesting that $31 million was being sought overall. Also supporting the round were Stealthpoint Ventures, ND Capital, Fall Line Capital, Hamamatsu Ventures, Murchison Capital Partners, and Overlap Holdings.

ARCH is known for a number of successful exits in biotechnology and pharmaceuticals, among them the DNA sequencing hardware developer Illumina.

Patrick Weiss, a partner at ARCH and now chairman of the board at Ramona, commented: “At ARCH, we look for breakthrough science paired with stellar teams that can build versatile platforms and deliver real-world impact.

“Ramona has developed a fundamentally new class of microscopy technology and translated it into commercial systems that are already being adopted by customers in academia and industry. We believe the company is well-positioned to scale deployments of its products and expand its role in driving progress in biomedical research and drug discovery globally.”

In their blog post Ramona's senior team summed up the potential of the approach thus: "The microscope will stop being the bottleneck. When it does, everything accelerates. A question asked in the morning has a whole plate's answer by the afternoon. A disease model plays out across hundreds of conditions at once, watched rather than sampled, whole enough for AI to learn from.

"We build the instrument and the software. What scientists discover with them is theirs. The faster we help them see, the sooner discovery becomes medicine for the people waiting on it. If you build optics, analysis pipelines or assays, and you want to work on this, we are hiring."