AI deep research API that checks every cited claim against its source
Stage
Not given
Startup
Founded
Not given
2026
Based in
US
US
Pricing model
Paid
Paid
Pricing
Usage-based pay-as-you-go pricing with hourly compute charges; committed contracts available for volume discounts. Hosted option with Anyscale-managed infrastructure or Bring Your Own Cloud deployment available.
Not given
Key features
Multimodal data curation at scale
Distributed model training with elastic scaling
Batch embedding generation at scale
Multi-cloud deployment and orchestration
Cluster-backed development environments
Workload-specific observability and debugging
Production-grade managed Ray clusters
Access control and governance
GPU budget controls and cost attribution
Post-training with SkyRL and veRL support
Searches 45+ structured sources including SEC filings, statistics agencies, scholarly indexes, books and government records
Workspace vault with searchable full-text source storage and independence clustering
Integrated MCP server for Claude, Cursor, ChatGPT and other clients
Contradiction mapping identifies and investigates disagreements in evidence
Automatic report generation with verified citations before shipping
Citation verification API for external documents
Markdown import and export for vault documents
Integrated Answers MCP with ask and search tools for AI agents
What makes it different
Built by creators of Ray, the world's most widely adopted AI compute engine
Multi-cloud deployment without code changes
Feels local but runs distributed
Unified GPU pooling across clouds and regions
Every cited claim is verified against the original source before report delivery
Searches structured sources like SEC filings and scholarly databases alongside the open web in one call
Delivers answers with word-for-word quotes so readers can verify in one click
Open-source CLI that powers the hosted service, maintaining feature parity
Pay-as-you-go pricing with no minimum commitment or sales team required
Hyperresearch