Features
Normalized output score calibrated to expert benchmarks
AI vs human contribution attribution
DORA metrics: deployment frequency, lead time, MTTR, change failure rate
Code intelligence: output, code quality, code reviews, code turnover
AI-specific metrics: AI health, AI impact, AI ROI, AI insights, AI usage
Agent observability for AI coding agents (Cursor, Claude Code, Copilot)
Wooly AI agent for natural language queries about engineering activity
PR drill down with individual and team stats
Dev FinOps tracking engineering cost to value
Real-time team tracking and dashboards
Role-based user permissions and SSO
SOC 2 Type II, GDPR, HIPAA compliant
SCIM provisioning for user management
Integration with version control, project management, and communication tools
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
