Datasets for RAG, fine-tuning and training built from public sources
Turn a podcast into a blog post — free SEO draft
Stage
Not given
Not given
Founded
Not given
Not given
Based in
Not given
Not given
Pricing model
Paid
Freemium
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)
Free: 3 packs per day per IP, last 5 saved packs. Pro: first month $1.9, then $12/month, or $99/year billed annually. First 50 annual subscribers get 2 bonus months (14 months total).
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
Converts podcast transcripts and show notes to SEO blog drafts
Generates FAQ blocks and schema markup
Creates social media scripts
Includes publish checklist with canonical and sitemap guidance
Accepts audio file uploads for automatic transcription
Markdown and HTML download options
Pack history and recovery with email backup
BlogPosting and PodcastEpisode JSON-LD schema copy
Free podcast show notes template
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
Podcast-specific pack format instead of general AI rewriting
Works without a full transcript using structured show notes
Focuses on search intent and problem-solving rather than episode archive titles