Ozette Technologies, a technology-driven life sciences startup engineering advances in cell discovery and annotation of the human immune system, announced its $26 million Series A funding, led by Madrona Ventures. Other participating investors include Cercano Management (formerly Vulcan Capital), M12, Microsoft’s venture fund, Alexandria Venture Investments, OCV Partners and Duke University.
Ozette’s intelligent computational analysis technology unlocks the full potential of single-cell data. The AI-powered analysis, rooted in research pioneered at the Fred Hutchinson Cancer Center and incubated at the Allen Institute for Artificial Intelligence (AI2), reveals high-resolution, reliable insights an order of magnitude faster than the gold standard manual analysis. Ozette’s early partners include small to large biopharma developing therapies in oncology, autoimmune disorders and infectious disease. The speed and resolution of Ozette’s automated insight generation unlocks new frontiers in computational immunology, accelerating the next wave in biomarker and drug discovery.
The new funding will fuel company growth and enable Ozette to expand its capabilities across a multiomic platform, integrating single cell proteomics and transcriptomics. A new state-of-the-art immunology lab in development will soon offer another asset and avenue for partners to leverage Ozette’s technology, further enabling vertically integrated, automated analysis for immune insights.
“Ozette’s technology alleviates partners from labor intensive, manual analysis methods and propels us towards a future with integrated, searchable immune data that transforms therapeutic discovery and development,” said Dr. Ali Ansary, CEO and Co-founder of Ozette. “We’ve engineered a machine learning-based computational platform that completes months of analysis work in a matter of days, and we’re working on reducing that to minutes. Ozette represents the first line of innovation towards a fully resolved portrait of the immune state at the cellular level, providing transparent, verifiable, and reproducible high-resolution insights that scientists can trust.”
