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Apify scraping & data

Scraping and data pipelines, done legally

Legal boundaries, rate limits, data hygiene, refresh cadence.

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Pain points

Where you lose money

Legal grey areas ignored

Scraping ships without regard for terms, rate limits or personal-data rules, exposing the business to blocks and legal risk.

Pipelines that rot

Target sites change, selectors break, and the data goes stale, decisions get made on numbers that stopped updating weeks ago.

Dirty, duplicated data

Without hygiene and dedupe, scraped data arrives messy and unusable, and the cleanup costs more than the scrape saved.

No refresh cadence

One-off scrapes age instantly; without a defined refresh schedule the dataset is a snapshot pretending to be live.

Channel mix

What we'd run

Legal & compliance scopingfoundation

We scope terms, rate limits and personal-data rules first, so the pipeline doesn't expose you to blocks or legal risk.

Resilient extractionprimary

Monitoring and maintenance so when target sites change, the pipeline is fixed before the data goes stale.

Data hygieneprimary

Dedupe and validation so scraped data arrives clean and usable, not a mess that costs more to fix than it saved.

Refresh cadencecompounding

A defined refresh schedule so the dataset stays current instead of being a snapshot pretending to be live.

FAQ

Is web scraping legal?

Public data generally is, but it depends on the source's terms and the data type, personal data carries GDPR obligations. We scope each project to what's lawful and flag anything that isn't before building.

What breaks scrapers and how do you handle it?

Site changes, rate limits and anti-bot measures. We build resilient parsers with error handling and monitoring so a layout change alerts you instead of returning garbage.

What format do I get the data in?

Whatever your systems use, CSV, a database, an API feed, or straight into your tool via automation. The data is only useful where you work, so that's where it lands.

Can you keep it running on a schedule?

Yes, scheduled runs with monitoring and alerts, so the dataset stays current without anyone babysitting it.

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