Features
Agentic search with reasoning
1.9x better recall than embedding-only methods
24x faster than traditional embedding approaches
Test-time compute for dynamic query adaptation
RL-based search optimization with 1k+ QPS rollouts
Outperforms frontier models on complex search tasks
Training to beat GPT-5 at search (ongoing)
Designed for context-aware AI system retrieval
Waitlist access for research phase
Solar-powered rover with controlled illumination
Per-plant high-resolution imaging across growing season
AI-powered pest, weed, and disease detection per plant
Real-time plant growth monitoring
Automated yield forecasting from imagery and environmental data
Crop phenotyping via computer vision (leaf size, biomass)
Quality inspection for fruits and vegetables
Food waste reduction analytics
Integration of satellite, weather, and soil data layers
Multi-angle image capture with controlled rolling speeds
Machine learning to identify plant-environment interaction patterns
Data collection in muddy fields and bright sunlight
Terabyte-scale image datasets per field
Controlled rolling speeds and sophisticated sensors
