AI for predicting how organisms change over time.

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We build robotic systems that sustain mammalian   development externally, paired with AI models that learn to predict how that development unfolds and responds to intervention.

What we believe in

Development is

how biology builds itself.

A single cell becomes a complex organism.

Every biological system, including the brain, emerges from this process. It unfolds over time, shaped by signals and environment.

If we can model development, we gain a new way to understand how biological systems form, fail, and recover.

Every biological system, including the brain, emerges from this process. It unfolds over time, shaped by signals and environment.

If we can model development, we gain a new way to understand how biological systems form, fail, and recover.

Every biological system, including the brain, emerges from this process. It unfolds over time, shaped by signals and environment.

If we can model development, we gain a new way to understand how biological systems form, fail, and recover.

The problem

Most disease is developmental
but our understanding is incomplete.

Many diseases are failures of development over time.

But drug safety and intervention decisions rely on animal models and short-lived experimental systems that fail before longer term development can occur.

Where biology matters most, over long time horizons, we can’t predict what will happen.

But drug safety and intervention decisions rely on animal models and short-lived experimental systems that fail before longer term development can occur.

Where biology matters most, over long time horizons, we can’t predict what will happen.

But drug safety and intervention decisions rely on animal models and short-lived experimental systems that fail before longer term development can occur.

Where biology matters most, over long time horizons, we can’t predict what will happen.

What we built

A platform for long-term, predictive development.

Robotic metabolic exchange systems

Closed-loop hardware that regulates physiology   in real time, delivering nutrients, removing waste,   and controlling gas exchange, to sustain development externally.

Model for virtual organisms

Dynamical AI models trained on sustained, controlled development that learns how systems evolve and diverge under different conditions.

Together, these allow development to be sustained, perturbed, and predicted

The breakthrough

Sustaining development makes it predictable.

As development advances, metabolic demands rapidly increase.

Most experimental systems fail   at this point.

By reproducing key aspects of biological metabolic exchange under controlled conditions, we sustained development  further than previously possible and observed it responding predictably to our system.

The AI leap

Development doesn’t follow
a single path.

Most machine learning assumes fixed trajectories. Development explores a structured space of possibilities.

Impact

Our models learn the rules governing how developmental systems change—predicting how futures branch, and which interventions shift a system from one outcome to another.