Becoming
Becoming AI builds robotic systems that sustain mammalian development externally, paired with AI models that learn to predict how development unfolds and responds to intervention. The platform combines closed-loop hardware regulating nutrient delivery, waste removal, and gas exchange with dynamical AI models trained on sustained development data. This creates a self-improving experimental loop that turns mammalian development into something that can be tested and predicted.
- Biotechnology
- Robotics
- Artificial Intelligence
- Hardware
- Healthcare Technology
Funding
Funding not disclosed
Founders
Founder details are not available yet.
Product
Problem
Most diseases are developmental in nature, yet our understanding of how organisms change over time is incomplete. Many diseases are failures of development over time, but 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 dynamical AI 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 from something observed after the fact 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 and respond to interventions.
Features
- Robotic metabolic exchange systems that sustain mammalian 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
- Dynamical AI models trained on sustained, controlled development that learn how systems evolve and diverge under different conditions
- Self-improving experimental loop where sustained development generates data, models learn from it, and predictions guide the next experiments in real time
- Integrated approach combining AI, robotics, and biology built together to enable predictive testing of developmental outcomes