Automate
Build a RAG knowledge base from a channel
What it does
Raw transcripts make muddy retrieval units. The distilled archive chunks cleanly — claims already carry their sources, entities their relations — so your RAG pipeline starts from structure instead of noise.
How it works
Get the archive
Buy the channel's archive — entities, claims, topics, and transcripts in one zip.
Chunk it your way
Feed it to your pipeline: the structure means clean chunks with real provenance.
Retrieve with citations
Retrieval hits carry their source video and claim — no orphan quotes.
What you'll need
With an AI agent
- A purchased skill archive
- Your RAG or embedding pipeline
Offline, from the zip
- A purchased skill archive
- Python or any JSON-processing toolchain
How to use it
Connect your agent, then start with a prompt like:
Chunk this channel's knowledge graph into retrieval units, preserving claim provenance.