# AI training data

> Collect web-scale text as clean, deduplicated, license-aware LLM training data. Raw pages in, model-ready corpus out, refreshed on your schedule.

## The web is your training set.

Collect web-scale text as clean, deduplicated, license-aware LLM training data. Raw pages in, model-ready tokens out.

## 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.

- 01 Dedup

- 02 Language

- 03 Quality

- 04 License

- 05 PII

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, docs Whatever the source ships, returned as clean text.

- Web scale Millions of URLs, batched and parallel, not one at a time.

- Provenance kept Source URL, timestamp, and license on every record.

- Refreshable Re-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.

## Stop cleaning crawls by hand. Ship the corpus.

_Canonical HTML: https://www.zenrows.com/solutions/ai-training-data_

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