Datasets for RAG, fine-tuning and training built from public sources
Fix your AI outbound and save your reputation
Stage
Not given
Startup
Founded
Not given
2026
Based in
Not given
Not given
Pricing model
Paid
Paid
Pricing
Web Corpus: starter sets from $49 (250,000-500,000 documents); shelf corpora from $99-$199 per shelf (one-time license); Everything (full crawl) $3,000 one-time; Whole Library (all builds for one year) $5,000 per year. Reference Collections: Encyclopedia $1,200 commercial or $349 research (one-time)
Not given
Key features
Web corpus with 100.7M documents and 105B tokens from Common Crawl
Documents scored with Bear Rank and shelved by subject
15 million encyclopedia entries with sources and model attribution
200 million synthetic Q&A records with metadata
Delivered as JSONL and Parquet formats
Free samples available on Hugging Face
Command-line client with resumable downloads and SHA-256 verification
Statistics collection with 7,000 topics and 140,000 figures
Glossaries for 10,000 topics with one million definitions
Book Q&A dataset with 8,000 titles for reading comprehension
PASS, REVIEW or FAIL verdicts on emails
Detects unsolicited diagnoses and privacy intrusion
Catches generic flattery and fake familiarity
Identifies asymmetric asks and excessive homework
API integration for automated checking
MCP support for AI agents
Gmail and Outlook browser extensions
ChatGPT app integration
Structured JSON verdicts via API
What makes it different
All datasets built only from public sources with full provenance tracking
Every record includes source attribution and licensing information
Eight curated subject shelves for targeted corpus selection
Flexible purchasing: buy individual shelves, slices, or entire library
Over 4 TB of clean, deduplicated text ready for machine learning
Uses deterministic filters with fixed rules before applying AI analysis
Judges message form rather than facts it cannot verify
Scales across workflows and no-code platforms
Integrated with Zapier, Make, n8n, Clay and other tools