Features
Autonomous growth chambers with environmental control
Real-time plant phenotyping using computer vision
AI-powered growth prediction models
Remote monitoring via web dashboard
Customizable experimental protocols
Data export for downstream analysis
Multi-chamber orchestration
Integration with external sensors
Automated irrigation and nutrient delivery
Climate simulation for location-specific scenarios
Collaborative project sharing
Versioned experiment history
Alert system for anomaly detection
Scalable from lab to greenhouse
API access for third-party automation
Reverse-engineer causal mechanisms of AI models
Reveal internal structure and hidden representations
Detect performative chain-of-thought in LLMs
Identify confounders and debug model behavior
Validate whether models learned real clinical understanding
Trace unstable behaviors to brittle internal features
Reduce hallucinations via features as rewards
Accelerate materials discovery with self-correcting search
Control training precisely with less data and off-target effects
Support for LLMs, life sciences, and robotics/vision models
Harvest activations from trillion-parameter models
SOC 2 Type II certified security and compliance
Analyze latent policy structure in robotics models
Interpret genomic models like Evo 2
Discover novel biomarkers via model reverse-engineering
