The web is
your training set.
Model-ready data,
from the open web.
Collect web-scale text as clean, deduplicated, license-aware LLM training data. Raw pages in, model-ready tokens out.
Turn the open web into a clean, rights-checked dataset your team can train on, refreshed on a schedule.
Ninety percent of a page
isn't the page.
Nav, ads, cookie banners, related links. One call keeps the content and drops the rest.
Clean text is the start.
Not the finish.
Every serious corpus runs the same gauntlet. Hover a stage.
- 01Dedup
- 02Language
- 03Quality
- 04License
- 05PII
Near-duplicate pages collapse to one, so the model doesn't over-learn a boilerplate paragraph copied across ten thousand sites.
Every document tagged by language and script, so you train on the mix you meant to, not whatever the crawl returned.
A quality score per document, so thin, spammy, and machine-generated pages can be dropped before they reach the run.
Robots and terms read per source, so you keep provenance and can prove what you were allowed to use.
Emails, phones, and identifiers flagged, so sensitive strings can be redacted before training.
Any format. Any scale.
Rights intact.
- HTML, PDF, docsWhatever the source ships, returned as clean text.
- Web scaleMillions of URLs, batched and parallel, not one at a time.
- Provenance keptSource URL, timestamp, and license on every record.
- RefreshableRe-pull for a fresh cut whenever you need one, keeping the corpus current.
The corpus is one call.
Here's the rest.
Same infrastructure, pointed at other jobs.