# Web Data for LLMs – Clean Markdown & RAG-ready Context | Quantum Proxies

Turn any URL into LLM-ready Markdown with token accounting and RAG chunking. Grounding search and AI extraction through residential proxies.

Turn any URL into LLM-ready Markdown with token accounting and RAG-friendly chunking. Web Data for LLMs gives AI agents and pipelines clean, grounded web context through residential proxies.

## LLM-ready output

Pages come back as clean Markdown stripped of navigation and boilerplate, with token estimates and optional overlap-aware chunking so you can drop them straight into a RAG index or prompt.

## Grounding search

Run a search, fetch the top results in parallel as Markdown, and get a citation-ready context block under a token budget — ideal for answer engines and research agents.

## AI extraction

Describe the data you need and receive structured JSON, extracted from real, unblocked pages through residential IPs.

## Sources

- [The /llms.txt file (llmstxt.org)](https://llmstxt.org/)
- [RFC 9309: Robots Exclusion Protocol](https://www.rfc-editor.org/rfc/rfc9309.html)
- [Retrieval-augmented generation (Wikipedia)](https://en.wikipedia.org/wiki/Retrieval-augmented_generation)
- [Overview of OpenAI Crawlers](https://developers.openai.com/api/docs/bots)
- [Common Crawl - Overview](https://commoncrawl.org/overview)