Symbolia AI introduces TerraSage for climate-resilient biology.Symbolia AI introduces TerraSagefor climate-resilient biology.
TerraSage integrates genomics, biological knowledge, climate data, and active learning to accelerate discovery in crops, trees, and agricultural biotechnology.
We are fusing natural-language reasoning with computational tools to turn climate-biology questions into testable discovery programs.
AI scientist
platform for climate-resilient biology
Closed-loop
knowledge, data, models, and validation
TerraSage
flagship discovery system
Focus Areas
One discovery engine for climate resilient biology.
We are building TerraSage as a closed-loop AI scientist platform: scientific literature, knowledge graphs, multimodal data, domain models, Bayesian optimization, active learning, and validation feedback.
Climate-Resilient Agriculture
Genomics-guided workflows for climate-resilient crops, drought response, water-use efficiency, nitrogen-use efficiency, and field validation evidence.
Read applicationClimate-Resilient Forestry
AI scientist workflows for climate-adapted trees, genotype and provenance discovery, forest biotechnology, and agroforestry resilience programs.
Read applicationAgri-Biotech and Microbial Systems
Discovery support for microbial consortia, soil-crop interactions, bioinputs, and agricultural biotechnology products.
Read applicationClimate resilience needs discovery systems
TerraSage applies Symbolia AI's AI scientist engine to biology for climate resilience.
Scientific orchestration layer
LLM-based scientist workflows connect literature, domain knowledge, datasets, assumptions, and hypotheses in one traceable system.
Proprietary reasoning platform
TerraSage fuses natural-language scientific reasoning with computational tools, structured evidence, and iterative validation workflows.
Multimodal evidence
Genomics, phenomics, climate records, soil variables, field observations, and partner datasets can be reasoned over together.
Better candidate prioritization
The platform ranks traits, lines, genotypes, provenances, organisms, and interventions with explicit scientific rationale.
Closed-loop learning
Bayesian optimization and active learning help decide what to test next and update priorities as validation data arrives.
Partner-ready evidence
Outputs are designed for crop science institutes, plant biotech teams, forestry groups, and product-development partners.
Questions before a partnership conversation.
TerraSage is Symbolia AI's AI scientist platform for climate-resilient biology. It integrates biological knowledge, genomics, phenomics, climate data, and active learning to accelerate discovery in crops, trees, and agri-biotechnology.
TerraSage connects natural-language reasoning with computational workflows, evidence graphs, and validation feedback so teams can move from broad climate-biology questions to ranked hypotheses and next experiments.
The current flagship application is genomics-guided discovery of climate-resilient crops. Near-term extensions include climate-resilient trees, agroforestry, microbial agriculture, and soil-crop systems.
The platform is being designed for scientific literature, knowledge graphs, genomic and phenotypic data, climate and soil variables, field observations, remote-sensing signals, and partner data and validation.
Crop science institutes, agri-biotech teams, forestry and agroforestry groups, food-security programs, and land-system partners working on climate resilience are the best fit.
