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.
Together, these allow development to be sustained, perturbed, and predicted.
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.
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.
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.
This turns mammalian development from something we observe after the fact into something we can test and predict.