Features
Pre-built connectors (Google Drive, Notion, Slack, Confluence, etc.)
Automatic user-data syncing with incremental syncs and auth handling
Multimodal ingestion: text, PDFs, images, audio, video
Hybrid search: vector, keyword, and summary indexes
Entity extraction using plain-language definitions
Ragie Parse: structured data extraction from tables, forms, charts
Agentic OCR (beta) for low-quality scans and complex layouts
Tenant isolation via Partitions
Reranking and recency bias in retrieval
MCP Server and MCP Bridge for agentic knowledge base access
Whitelabel connectors (beta)
Glob-based sync filters to exclude documents by metadata
Webhooks for document status updates (including paused_insufficient_credits)
Base64-encoded image data in document element responses
Multi-language support including RTL
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
