Features
Expert-curated datasets for frontier AI models
Custom data development pipelines
Curriculum-structured Data Series with rubrics and difficulty tiers
Agentic AI system development and deployment
Benchmark design and evaluation (e.g., Continual Learning Bench, Agents' Last Exam)
Open-source benchmark grants and funding
Weak supervision and data programming capabilities
Rubric Failure Mode Taxonomy (RIFT) for evaluation diagnostic
Domain-specific agent benchmarking (e.g., insurance underwriting)
Collaboration with expert contributor community
Calibrated expert review with gold sets and per-task calibration
Programmatic graders and evaluator models at scale
Full audit trails with adjudication and provenance
Templated generation for edge-case coverage across difficulty bands
Eval harnesses with task-specific rubrics and deterministic graders
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
