becoming.ai is building a platform that predicts how organisms change over time by sustaining development outside the body using closed-loop robotic systems. The platform combines hardware that regulates physiology in real time with AI models that map developmental trajectories, enabling researchers to test and predict biological outcomes. This approach turns mammalian development from a post-hoc observation into a testable, predictable process.
Funding
Funding not disclosed
Founders
Product
Problem
Most diseases are developmental in nature, yet our understanding of how organisms change over time is incomplete. Traditional experimental systems fail to sustain development as metabolic demands rapidly increase, limiting the ability to study and predict biological outcomes.
Solution
becoming.ai provides a platform for long-term, predictive development by combining robotic metabolic exchange systems with AI models of virtual organisms. The closed-loop hardware regulates physiology in real time—delivering nutrients, removing waste, and controlling gas exchange—to sustain development externally. As development advances, the system generates continuous data that feeds into machine learning models, which learn the structured space of developmental possibilities rather than assuming fixed trajectories. These predictions then guide the next experiments in real time, creating a self-improving loop that turns mammalian development into something that can be tested and predicted.
Target Audience
Primary users are researchers and scientists in developmental biology, disease modeling, and pharmaceutical R&D who need to study and predict how organisms change over time.
Features
- Robotic metabolic exchange systems that sustain development externally through real-time nutrient delivery, waste removal, and gas exchange control
- Closed-loop hardware that regulates physiology in real time to maintain development as metabolic demands increase
- AI models of virtual organisms that map the structured space of developmental trajectories rather than assuming single fixed paths
- Self-improving experimental loop where sustained development generates data, predictions guide next experiments, and the system learns as it experiments
- Integrated approach combining AI, robotics, and biology to enable predictive testing of developmental outcomes