NoMaze provides an AI‑driven platform that predicts plant trait performance across diverse environments by modeling genotype‑by‑environment interactions. Leveraging pre‑trained models built on over 100 k environmental datasets and transfer learning, the solution delivers high‑accuracy predictions (0.7 R²) that can increase genetic gain by more than 11 %, while offering cloud, on‑premise, and API deployment within a collaborative workspace.
Funding
Funding not disclosed
Founders
Product
Problem
Plant breeding programs struggle to predict how specific genotypes will perform across diverse, real-world environments due to complex genotype‑by‑environment interactions and the high dimensionality of genetic and phenotypic data. This uncertainty slows the development of high‑yielding, climate‑resilient varieties and reduces genetic gain.
Solution
NoMaze offers an AI‑driven platform that models genotype‑by‑environment dynamics to predict plant trait performance with high accuracy. Their pre‑trained models, built on over 100 k environmental datasets and leveraging transfer learning, generate predictions that lift genetic gain by more than 11 %. The platform provides cloud‑based, on‑premise, and API deployment options, allowing breeding teams to explore data, visualize relationships, and run predictions within a unified workspace. By integrating advanced machine‑learning pipelines with domain‑specific visualizations, NoMaze enables breeders to make faster, data‑backed selection decisions and accelerate the creation of improved crop varieties.
Target Audience
Primary customers are plant breeding companies, research institutions, and agrigenomics teams that need accurate genotype‑by‑environment predictions to accelerate variety development and improve selection efficiency.
Features
- AI models specifically trained on large, noisy genotype‑by‑environment datasets to capture non‑linear interactions affecting yield and other traits
- SmartPlot tool for structured exploration and visualization of genetic, phenotypic, and environmental data
- Unified platform supporting cloud, on‑premise, and API access to meet diverse governance and infrastructure requirements
- Transfer‑learning workflow that leverages 100 k+ pre‑trained environmental contexts for rapid model adaptation to new crops or trials
- Performance metrics showing 0.7 R² prediction accuracy and an 11.4 % increase in genetic gain from accuracy lift
- Collaborative workspace enabling team members to share data, analyses, and prediction results in real time