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Enhanced Duplicate Detection Using AI & Fuzzy Matching

Feature Request

The current duplicate detection does not always catch contacts with similar but slightly different names. Request to improve the duplication finder by leveraging AI or fuzzy logic to identify likely matches beyond exact string comparisons.

Potential improvements could include:

Comparing across multiple fields (LinkedIn, email, phone number, company)

Using fuzzy matching to flag near-duplicates

Providing suggestions for possible merges instead of only exact matches

This would help users maintain cleaner contact data with less manual effort.

Posted by CJ ·
Status: Open3 comments

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Comments3

  • Arthur

    •

    May 14

    Thanks CJ!

  • Mitchell Starrenburg

    •

    Apr 26

    Seconded. I insert emojis in the google contacts ‘prefix’ field to categorise/tag people without affecting sort order, and that was enough to cause Dex to not match a duplicate despite the first/last names being identical.

    Simple matching algorithm should normalise case and strip out emojis, punctuation, whitespace, etc before matching the remaining characters.

  • Joshua Behar

    •

    Sep 25, 2025

    This is highly needed