Gilad Almogy, CEO & Founder, Ultima Genomics
Gilad Almogy is the CEO and Founder of Ultima Genomics, a Fremont, California-based company reimagining the economics and scale of DNA sequencing. Before launching Ultima, Almogy spent nearly two decades in the semiconductor industry — including senior leadership at Applied Materials — where he oversaw large-scale manufacturing and technology development. It was that background in precision engineering and cost-driven scaling that gave him the template for a fundamentally new approach to genomics.
In this Q&A, Almogy traces Ultima's origin story from a semiconductor analogy to a sequencing revolution, explains how the company's open-flow-cell architecture enables dramatically lower costs and higher throughput, and describes how those capabilities are being deployed across clinical oncology, population genomics, and large-scale biological AI. He also reflects on the culture he is building, the challenges that remain, and why he believes the convergence of sequencing, AI, and multi-omics makes this a genuine inflection point for biology and human health.
July 27, 2026
Q1. What was the defining moment or insight that led you to start Ultima Genomics?
My background in semiconductors shaped how I think about scale, precision, manufacturing, cost, and quality. The defining insight for Ultima came from seeing a clear parallel between those two fields. In both, there is an almost endless demand for more data, higher throughput, and lower cost.
In semiconductors, the industry transformed by learning how to scale complex technologies with extraordinary precision and cost efficiency. In genomics, DNA sequencing had already become one of the most important technologies in biology and medicine — but it was still constrained by cost and scale.
We started Ultima around a simple question: what would sequencing look like if it were built from the ground up for industrial-scale data generation? Existing architectures were powerful, but they were not designed to deliver the dramatic throughput and cost improvements that biology would ultimately require. That opportunity was too important to walk away from.
Q2. In plain terms, what does Ultima do — and why does it matter for patients?
Ultima builds ultra-high-throughput DNA sequencing technology that generates vast amounts of biological data at dramatically lower cost. In simple terms, we enable scientists and clinicians to read DNA — and other biological molecules — at extremely high throughput and at an extremely low cost per data point.
Sequencing has become the most powerful generator of multi-omics biological data, information essential for understanding disease, detecting cancer, selecting therapies, monitoring whether treatment is working, and discovering new drugs. But historically, cost, accuracy, and scale have limited what researchers and clinicians could do. Our goal is to remove those constraints.
For patients, lower-cost, higher-throughput sequencing can enable earlier disease detection, better-informed care, and deeper biological insight. In oncology, for example, our ppmSeq technology enables ultra-sensitive liquid biopsy and minimal residual disease (MRD) testing — finding rare tumor DNA signatures in blood that indicate disease at very low concentrations.
Our second-generation platform, the UG200 Series with Solaris 2.0 chemistry, delivers higher output, faster turnaround, better genomic coverage, and a more compact footprint. It builds on a straightforward belief: the more affordable and scalable sequencing becomes, the more broadly it can shape patient care.
Q3. What sets Ultima's platform apart from others working in this space?
The most important difference is that Ultima's platform was designed from the beginning for scale. Our sequencing architecture is fundamentally different: we use an open-flow-cell architecture based on a semiconductor wafer, which provides a large, low-cost open surface area that we combine with fast chemistry, high-speed optics, and machine-learning-powered analysis. This approach gives us multiple degrees of freedom to push data output higher and cost lower over successive generations.
Our first-generation product, the UG 100, proved the new architecture could support a new cost-and-throughput curve. We introduced it with the $100 genome in 2024, then reduced that to $80 with Solaris chemistry in 2025. The UG200 Series, launched in early 2026, advances that curve further — higher throughput, faster turnaround, reduced footprint, and improved genomic coverage while maintaining our SNVQ-60 accuracy standard.
That scale matters because the next era of biology will be driven by large, high-quality datasets that enable AI and machine learning. AI models in biology need data the way language models need text — but unlike language, where the internet already provided vast training sets, foundational biological datasets still need to be built. Ultima's role is to make that data generation economically and operationally possible.
Q4. What is your most important milestone to date, and what comes next?
Commercializing the UG 100 was critical because it moved Ultima from a technology vision to a platform customers could deploy. Launching the UG200 Series in February 2026 — with faster run times, a smaller footprint, improved usability, and greater configuration flexibility — was the next meaningful step.
On the clinical side, our AACR 2026 data was significant. We announced results across six abstracts, including work from TRACERx, Labcorp, and DELFI Diagnostics. TRACERx data showed low single-digit parts-per-million sensitivity for circulating tumor DNA detection using ppmSeq. Labcorp presented analytical data demonstrating specificity above 99.9% and a limit of detection below 3 ppm across commercial cancer cell lines. These are important steps from research-grade performance toward clinical-grade reliability.
On the research side, we are supporting some of the largest BioAI projects underway — including the Chan Zuckerberg Initiative's Billion Cell Project, the Arc Institute's virtual cell model, and the UK Biobank Pharma Proteomics Project.
Looking ahead, the next milestone is translating that scale into broader clinical and biological impact: expanding UG200 adoption, advancing whole-genome MRD toward global clinical deployment, and helping establish the foundational datasets that AI-powered biology will require.
Q5. What is the biggest challenge you are facing right now, and how are you tackling it?
One of the biggest challenges is that we are building technology for fields that are changing very quickly. The needs of researchers, clinical labs, and AI developers are evolving almost in real time.
On the scientific side, the most important applications require a combination of scale, sensitivity, accuracy, and cost efficiency simultaneously. In MRD, the challenge is finding tumor-derived DNA that may be present at extremely low concentrations in a background of normal DNA. In BioAI, the challenge is generating datasets large enough, diverse enough, and high-quality enough to train meaningful models of biological systems.
On the business side, the challenge is scaling responsibly while maintaining speed. We are still a relatively young company, but we operate across major clinical labs, pharmaceutical companies, population-scale genomics initiatives, and cutting-edge research institutions. We need to move quickly without compromising quality, reliability, or customer trust.
Our approach is to focus on the applications where scale fundamentally changes what is possible: liquid biopsy, whole-genome sequencing, single-cell biology, methylation, proteomics, and large-scale BioAI dataset generation.
Q6. What has surprised you most about leading a life sciences company?
What has surprised me most is how much of the role is about connecting very different worlds. Ultima sits at the intersection of engineering, chemistry, biology, clinical medicine, data science, and manufacturing. Coming from semiconductors, I had a deep appreciation for scaling complex technologies. But in life sciences, technical performance alone is not the measure of success — the question is whether the technology can help researchers ask better questions, help clinicians make better decisions, and ultimately improve outcomes for patients.
I have also been struck by how consistently conversations with scientists and clinicians come back to the same barrier: they know the experiment they want to run or the assay they want to build, but cost or scale prevents them from doing it. Every time we can help remove that bottleneck, it is genuinely motivating — and it reinforces why this work matters beyond the engineering.
Q7. Where do you see Ultima in three years, and what has to go right to get there?
In three years, I expect Ultima to be recognized not only as a sequencing platform company, but as core infrastructure for large-scale biology. I expect broader adoption across clinical oncology, population genomics, single-cell biology, proteomics, methylation, and BioAI — and I expect whole-genome approaches to liquid biopsy and MRD to be much more mature, with stronger evidence for global clinical deployment.
For that to happen, several things need to go right. First, we need to continue scaling the platform while making it easier for customers to deploy in real production environments. The UG200 Series is an important step: higher throughput, faster library-to-data turnaround, flexible configurations, and reduced footprint.
Second, the clinical field needs more evidence. The next phase requires larger studies, more real-world context, and continued validation across use cases.
Third, BioAI needs foundational datasets that are far larger and more biologically rich than what exists today. We are already supporting programs like CZI's Billion Cell Project and Arc's virtual cell model, but the field is still early. If we can help make large-scale data generation routine, AI models will become far more powerful tools for understanding biology and disease. Ultimately, our success depends on whether we can keep turning scale into impact.
Q8. What is one thing about the life sciences industry you would change — and what gives you optimism?
If I could change one thing, I would reduce the degree to which cost still limits scientific ambition. There are many areas in biology where researchers know the right study would require more samples, more time points, deeper sequencing, or richer multi-omics data — but the economics force compromise. In clinical diagnostics, cost and operational complexity can also slow adoption of more comprehensive approaches.
That is unfortunate, because biology is extraordinarily complex. If we want to understand cancer evolution, immune response, or neurodegeneration, we need datasets that reflect that complexity. If we want AI to transform biology, we need to give AI models the data required to learn from biology directly.
What gives me optimism is that the field is finally converging. Sequencing, single-cell technologies, proteomics, spatial biology, cloud computing, and AI are all advancing simultaneously. We are moving from a world where biology was data-starved to one where we can begin generating data at industrial scale. I believe BioAI can do for the 2020s what genomics did for the 2000s — but only if we build the infrastructure to generate the data. That is the role Ultima wants to play.
About The Big4Bio CEO Weekly Q&A
Every Monday, Big4Bio spotlights a life sciences CEO from one of our eight coverage regions — Boston, San Francisco Bay Area, San Diego, Philadelphia, New York City, the Capital Region, Los Angeles, and Seattle. Each feature is promoted across all eight Big4Bio daily newsletters, reaching 30,000+ life sciences professionals. CEO participation is complimentary and editorial — every CEO approves the final Q&A before publication.
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