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Closed-loop discovery engine
Bayesian Optimization and Active Learning
TerraSage is designed as a closed-loop AI scientist platform. Bayesian optimization and active learning help convert scientific uncertainty into a practical sequence of experiments, simulations, measurements, or partner validations. The system supports hypothesis generation, candidate prioritization, and iterative learning as new data returns.
Rank experiments by expected information gain, feasibility, and scientific value.
Update candidate priorities as validation results and new evidence arrive.
Make uncertainty visible so teams can choose better next actions.
Workflows
How the platform supports it
Experiment planning
Next-best-test recommendations across field, greenhouse, lab, or simulation options.
Optimization loop
Bayesian ranking of candidates, traits, interventions, and validation paths.
Decision support
Readable rationale for scientific, operational, and partner-facing decisions.
