Extract addresses
Up to 4 MB of text. Nothing leaves your browser.
How it works
Decode
Obfuscated forms — name [at] domain [dot] com, @, %40, mailto: prefixes — are turned back into plain addresses before matching.
Match
A strict RFC-shaped pattern finds every candidate. Retina image names, hex hashes and doubled dots are discarded as false matches.
Classify
Each address is tested against role, free-mail and disposable lists, and duplicates are collapsed case-insensitively.
Export
Grouped by domain, with CSV, plain-text and domain-only downloads — ready for the list cleaner and the verifier.
Where addresses hide
A contact page, a conference speaker list, a mail server log, a CRM export with the email buried in column AF — every one of these has addresses in it and none of them will give you a clean list by copy-and-paste. This pulls them out of whatever you have. It reads HTML source directly, so a page that lists the team with mailto: links yields every address in one paste, and it decodes the common obfuscations — "name [at] company [dot] com" and the HTML entity tricks — that sites use to defeat naive scrapers.
What gets filtered, and why
Page source is full of strings shaped like email addresses that are nothing of the kind. Retina image names (logo@2x.png), version strings, hashes with an at-sign in them, template placeholders. Left in, they poison a list with guaranteed bounces, so they are dropped by default. Role addresses — info@, sales@, press@ — are kept but marked, because whether you want them depends on what you are doing; for cold outreach they are a liability, for a partnership enquiry they are the point.
The date on the snapshot
Whatever you extract was true when the page was written. A team page from 2022 has people who left in 2023. An old export has addresses that were changed when the company rebranded. B2B contact data decays at roughly two to three percent a month, so a two-year-old source is close to half dead — and every dead address is a hard bounce against your domain. Extraction is step one. The list cleaner removes what can be removed without asking anyone; the verifier asks the mail server about the rest.
On scraping and consent
This tool processes text you already have, in your browser, and sends nothing anywhere. What you do with the result is governed by the usual rules: business addresses can be contacted for relevant business purposes in most places, with honest identity and an easy opt-out; personal addresses on free-mail domains generally cannot be treated the same way. The free-mail flag is there so you can make that call before the list goes into a sequence.
Frequently asked questions
Does the extractor visit URLs?
No. It processes text you paste or upload. To extract from a web page, view its source (Ctrl+U or Cmd+Option+U), select all, and paste.
Why were some addresses dropped as false matches?
Strings like logo@2x.png, long hex hashes, and anything with consecutive dots match the shape of an address but are not one. They are removed so they do not become bounces. Untick the image-name option if a real address was caught.
What does the obfuscation decoder handle?
Bracketed and parenthesised at/dot — name [at] domain (dot) com — the HTML entities @ and ., the URL encoding %40, and mailto: prefixes.
Is there a size limit?
Four megabytes of text, which is several hundred thousand addresses. Larger files can be split.
Does this verify the addresses?
No — it finds and classifies them. Syntax, duplicates, role and disposable checks are done; mailbox existence needs the verifier, which asks the mail server.
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Start with a call
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Thirty minutes, no pitch
We will run the checks together on your actual domains, and you will leave knowing what is broken, what it takes to fix, and what it should cost. If that is a job you can do in-house, I will say so.