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Alternatives

Products that do what Extract Email Addresses from Text Online does

Turn messy text into clean contact lists instantly.

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    Clean email lists right in your browser for free

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  3. 3

    Scrape emails from socials and maps by location

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    Turn an email into a LinkedIn profile and +30 data points

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  5. 5

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    Extract structured data from text, files and archives.

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    Sift through your LinkedIn connections to find contact data

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  8. 8

    Automatically extract emails from any webpage

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  9. 9

    Extract data from emails. Automate your workflow.

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  10. 10

    Automate and extract contents from any website easily

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  11. 11
    Toofr68

    Find email through pattern matching.

    2014

  12. 12

    Natural-language prospecting: find, enrich and sync leads.

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  13. 13
    MailDump270

    Find, extract and verify emails automatically

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  14. 14

    Extract emails, dates, and URLs instantly and privately

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  15. 15

    Extract anyone's email address in a click.

    2020

  16. 16

    Automate data extraction with AI-powered document parser

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  17. 17

    Find any email from a company in seconds. free

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  18. 18MI
  19. 19

    Extract and validate emails in one click

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  20. 20

    Find quality emails from a domain and verify their validity

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  21. 21

    Find, enrich, clean, and verify prospecting leads instantly.

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  22. 22CF

    2013 · contactsanalytics.com

  23. 23RL

    We've been building data pipelines that scrape websites and extract structured data for a while now. If you've done this, you know the drill: you write CSS selectors, the site changes its layout, everything breaks at 2am, and you spend your morning rewriting parsers. LLMs seemed like the obvious fix — just throw the HTML at GPT and ask for JSON. Except in practice, it's more painful than that: - Raw HTML is full of nav bars, footers, and tracking junk that eats your token budget. A typical product page is 80% noise. - LLMs return malformed JSON more often than you'd expect, especially with…

    Mar 2026 · github.com

  24. 24ES

    This project reconstructs the Epstein email records from the recent U.S. House Oversight Committee releases using only public-domain documents (23,124 image files + 2,800 OCR text files). Most email pages contain only one real message, buried under layers of repeated headers/footers. I wanted to rebuild the conversations without all the surrounding noise. I used an OCR + vision-LLM pipeline to extract individual messages from the email screenshots, normalize senders/recipients, rebuild timestamps, detect duplicates, and map threads. The output is a structured SQLite database that…

    Dec 2025 · github.com

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