Cambria is an AI-native molecular discovery platform that turns evolution into a design interface for industrial R&D. Across billions of years, evolution has solved the hardest molecular and material challenges—repeatedly optimizing for stability, efficiency, and performance under real-world constraints. Yet these solutions remain fragmented across species and domains, making them difficult to use in today's discovery workflows. Cambria introduces a new evolutionary data layer and AI primitive that captures conserved functional patterns across life and matter, and translates them into structured inputs AI systems can design with. This allows models to reason not just over raw sequences or structures, but over the underlying constraints evolution has already validated. By grounding discovery in this evolutionary data layer, Cambria dramatically reduces search space and uncertainty, enabling faster, more reliable design of tailored molecular solutions. What once required years of trial-and-error can now be explored in months, starting from designs proven by nature rather than generated in isolation. We first applied this approach to develop heat-resilient crops, redesigning core photosynthesis proteins to maintain function under extreme temperatures. That same discovery engine now extends naturally into pharma, materials, and energy, where molecular performance, robustness, and real-world validation are critical. Cambria is building a new foundation for R&D— one where evolution is no longer inspiration, but validated infrastructure.

Cambria is an AI-native molecular discovery platform that turns evolution into a design interface for industrial R&D. Across billions of years, evolution has solved the hardest molecular and material challenges—repeatedly optimizing for stability, efficiency, and performance under real-world constraints. Yet these solutions remain fragmented across species and domains, making them difficult to use in today's discovery workflows. Cambria introduces a new evolutionary data layer and AI primitive that captures conserved functional patterns across life and matter, and translates them into structured inputs AI systems can design with. This allows models to reason not just over raw sequences or structures, but over the underlying constraints evolution has already validated. By grounding discovery in this evolutionary data layer, Cambria dramatically reduces search space and uncertainty, enabling faster, more reliable design of tailored molecular solutions. What once required years of trial-and-error can now be explored in months, starting from designs proven by nature rather than generated in isolation. We first applied this approach to develop heat-resilient crops, redesigning core photosynthesis proteins to maintain function under extreme temperatures. That same discovery engine now extends naturally into pharma, materials, and energy, where molecular performance, robustness, and real-world validation are critical. Cambria is building a new foundation for R&D— one where evolution is no longer inspiration, but validated infrastructure.