For billions of years, nature has been running history's longest and most rigorous experiment—refining the best designs through survival, adaptation, and time. The solutions we need already exist and are waiting to be discovered.

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Nature has the answers. AI lets us ask the right questions.

Nature is the search space.

Cambria is an AI-native discovery platform built to accelerate and enhance product development by translating evolution into a programmable design studio.
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Our platform identifies motifs shared across biology, chemistry, and materials—regions where performance consistently breaks down across industries. Within these regions we can generate and explore high-performing variants using proprietary evolutionary and biomimetic data to evaluate targets, provide greater insight, and predict key properties—moving teams from question to decision in weeks rather than months.

An AI-native discovery platform that turns evolution into a validation engine for molecular design.

Evolutionary Data Translation Layer
Cambria is an AI-native molecular discovery platform that treats evolution as a design space.
Instead of generating solutions from scratch, Cambria analyzes how nature has repeatedly solved similar problems across organisms, environments, and timescales — and translates those patterns into actionable design guidance.

At the core of the platform is a new discovery primitive we call Cambrian Regions: conserved functional regions that evolution preserves when performance matters. By identifying and modeling these regions across datasets, Cambria enables AI systems to reason with evolutionary validation — not just statistical likelihood.

From Patterns to Decisions
‍Cambria’s discovery engine maps user-defined constraints — such as stability, affinity, delivery, or resilience — to the regions of a molecule that actually control those outcomes. The platform then recommends targeted, explainable modifications grounded in evolutionary precedent.

Designed to Scale Discovery
‍Cambria coordinates multiple AI systems across discovery, evaluation, and refinement to explore broader solution spaces and converge faster on viable designs. The result is a closed-loop discovery workflow that scales far beyond manual experimentation or single-model approaches..

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