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Current flagship application
Drought-Resilient Cereal Crops
TerraSage AI helps plant science teams prioritize drought-resilient cereal crop candidates by integrating genomic signals, phenotypic observations, climate stress data, biological literature, and active-learning loops. The platform is designed to generate testable hypotheses, rank candidate traits or lines, and improve discovery plans as validation data returns from field, greenhouse, or partner experiments.
Connect genotype, phenotype, climate, and literature evidence into a unified discovery workflow.
Prioritize candidate traits, accessions, or breeding directions with transparent scientific rationale.
Use active learning to decide which experiments or validations should happen next.
Workflows
How the platform supports it
Discovery map
Knowledge graph and evidence model for target crops, traits, stressors, and candidate mechanisms.
Candidate ranking
Hypothesis scoring across genomic, climate, phenotype, and literature-derived signals.
Validation loop
Experiment planning and feedback integration with crop science partners or internal teams.
