Most guides on scraping a website hand you Python. You don't need it. The hard part of web scraping was never writing the code to pull text off a page, it was knowing which method fits the page in front of you and having somewhere to put the data once it's out.
No-code tools have closed the first gap, and a table you can enrich closes the second. This is how to scrape data from any public website without writing a line of code, which method to reach for, and how to turn a scraped page into data your team can actually use.
What web scraping is, and what “no code” really means
Web scraping is pulling structured data off a web page automatically, and “no code” means a tool writes the extraction logic so you don't. Instead of hand-copying a directory of 400 companies into a spreadsheet, a scraper reads the page and returns rows. The no-code part matters because it removes the only step that used to require an engineer: telling the computer where on the page each value lives.
The trap to avoid is scraping for its own sake. In Clay's framework, scraping serves two of the four data jobs (find and enrich), and it only earns its place when you know what you're going to do with the result. Start from the question (“which local clinics use this software,” “what does each competitor charge”), not the act of scraping. The objective decides the method, and the method is the easy part.
“We did an analysis of on-prem trends regionalized across the world in 3 days. This market research analysis would have taken my team 3 months to do without Clay.”
Is web scraping legal?
Scraping publicly available data is generally legal; the lines you don't cross are logins, copyright, and terms of service. You can collect data that any visitor can see without signing in. What you avoid: data behind a password or paywall, content that's copyrighted, anything a site's terms of service explicitly prohibit, and personal data you have no lawful basis to process. Regulations like the CFAA in the US and data-protection laws set the boundaries.
In practice the safe path is simple. Scrape public pages, respect a site's terms and rate limits, never collect login-gated data, and don't hammer a server with aggressive request volume. Clay's research tools work on publicly available information by design, which keeps you on the right side of the line as long as your target and intent are sound.
The no-code scraping methods, and when to use each
There is no single best scraper; the right method is decided by the page, not your preference. A clean public table, a page that needs human-style reading, a site that blocks bots, and a niche source like a maps listing each call for a different tool. Picking by habit is how people end up fighting a page that a different method would have handled in seconds.
Match the method to the page you're scraping
Which best describes the page you want to scrape?
Clay's own internal hierarchy is a useful default: start with Claygent for general public pages, fall back to Zenrows when a site fights back, reach for an Apify actor when you need a specialized source in bulk, and use the Chrome extension or native scraper for clean structured pages. You rarely need more than one for a given job.
How to scrape a structured page in two clicks
For a public page that already shows data in a table or list, the Chrome extension is the fastest path. Pages with tabular HTML (a directory, a results list, a pricing table) are the friendliest targets, because the structure is already there for the tool to read. You open the page, run Clay's Chrome extension, and the rows land in a Clay table ready to use.
The pattern scales past one page. For a paginated source, the Clay for Chrome extension captures lists across multiple pages, and a custom recipe with URL-matching variables tells it which pages to read; Clay's library has prebuilt recipes for exactly this, like turning a directory or a set of public listings into a clean account list. The output is not a static export, it's a table you can immediately enrich and act on.
Key contacts Regency Supply found and monitored for job changes after automating research in Clay.
Read the full storyHow to scrape any page with AI (Claygent)
When a page has no clean structure, Claygent reads it like a person and returns the fields you asked for. This is the method that makes “any website” true. Instead of mapping HTML selectors, you add a Use AI column set to Web research, or the ScrapeMagic integration, give it a URL and the output fields you want, and it extracts them from the page. It reaches any publicly available data that isn't behind a login.
A messy page turned into structured fields by AI
Northwind Labs builds industrial IoT sensors for the factory floor, founded in 2017 and based in Austin, Texas.
The team has grown to roughly 120-150 people across engineering and field ops, and just opened a second facility in Q1.
Under the hood they run analytics on Snowflake and manage outreach in HubSpot. There's no public pricing page.
Structured fields
AI scraping reads a page like a person and returns the fields you name, so unstructured pages need no selectors or code.
A reusable Claygent prompt for structured extraction:
Visit {{url}} and extract these as structured fields:{{fields, e.g. "company name, HQ city, employee range, the tools theymention using, and any pricing tiers"}}.Return one value per field, using only what is on the page. If a fieldis not present, return "Not found". Do not guess or pull from otherpages.
How to scrape pages that block bots, and at scale
Some sites actively stop scrapers, and some sources are too specialized for a general tool; that's where Zenrows and Apify come in. When a page throws up anti-bot defenses, Zenrows handles the protections that would stop a simpler scraper. When you need a specific source at volume (business listings, marketplaces, public databases), an Apify actor is a prebuilt scraper for that exact source, plugged straight into Clay.
This is also how the harder “any website” cases get solved: pulling structured data from hundreds of filings, capturing screenshots of pages or past versions from web archives, or extracting a whole paginated database in one run. You pick the source, the actor does the collection, and the results arrive in a table.
How to turn scraped data into something useful
Scraped data sitting in a file is trivia; scraped data in a table you can enrich is an asset. Extraction is step one, not the goal. The reason to scrape into Clay rather than a standalone tool is what happens next: the rows become a table where you waterfall-enrich missing emails, phones, and firmographics, score or filter them, and push the result to a sheet, your CRM, or a sequencer.
From scraped rows to an enriched, exported table
| Company | URL |
|---|---|
| Northwind Labs | northwind.io |
| Cedar & Co | cedarco.com |
| Atlas Freight | atlasfreight.co |
Scrape: Raw rows pulled straight from the page: company name, URL, and the few fields visible on the listing.
Scraping is only useful when the data lands in a table you can enrich and act on; extraction is step one, not the finish line.
That last step is the difference between a one-off scrape and a workflow. Because the data is in Clay, you can also schedule the scrape to re-run, so a competitor price page or a directory stays current instead of going stale the day after you pulled it.
“It's helped us fully automate lead enrichment that previously required expensive and time consuming manual research.”
How to start scraping in Clay
Pick one page and one question, and scrape it end to end before you scale. The first run should be small enough to finish in an afternoon:
- Name the objective: Decide the exact data and what you'll do with it (“contacts at every dental clinic in three cities, for an outbound list”).
- Pick the method by the page: Clean table, use the Chrome extension; unstructured, use Claygent; blocked or specialized, use Zenrows or an Apify actor.
- Scrape into a Clay table: Capture the rows, not a static file.
- Enrich and clean: Waterfall the missing emails, phones, and firmographics; normalize and de-duplicate.
- Export and schedule: Push to your sheet, CRM, or sequencer, and set the scrape to re-run so the data stays fresh.
Start with the page in front of you, get one clean enriched table out the other side, then point the same workflow at the next source.