Feed your models a clean corpus, at volume
Collect readable text and structured records across the web for training and retrieval, without standing up a scraping team.
LightStandard
Models are only as good as their corpus, and raw HTML is full of nav, ads and boilerplate. Building the collection layer yourself is a project on its own.
01
Get readable text, not markup
Take the readability-cleaned article and Markdown output so what lands in your corpus is content, not chrome.
02
Scale the crawl
Fan out with idempotent jobs and webhooks so millions of pages collect reliably, with a dead-letter queue catching the ones that never load.
03
Keep provenance
Every run carries a manifest with source URL, timing and integrity hashes, so your dataset is traceable and reproducible.
How it works underneath
- Readability + Markdown output tuned for content extraction
- Idempotent fan-out with webhooks and a dead-letter queue
- Per-run manifest with source, timing and content hashes
What you walk away with
- A clean, deduplicated corpus instead of raw pages
- Reliable collection at millions of pages without a scraping team
- Traceable provenance for every document you keep