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Detects duplicate records using fuzzy matching and configurable thresholds. Outputs two paths: All Records and Deduplicated - so you can handle duplicates and clean records differently.

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Configuration tab

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Rules & Performance tab

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Advanced tab

Normalization Options - applied before comparison:
  • Trim & lowercase
  • Remove punctuation
  • Unicode normalize
  • Ignore company suffixes (e.g., “Inc”, “LLC”)
  • Phone normalize (E.164)
  • Address normalize
Safety Rails - prevent unintended mass merges:

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Output

Two output paths:
  • All Records - every record with duplicate scores attached
  • Deduplicated - clean dataset with duplicates removed

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Best Practices

  • Start with the Balanced preset and adjust thresholds based on results
  • Use Blocking Keys for large datasets - comparing every record pair is expensive
  • Enable Dry run first to preview results before committing merges
  • Set Max total merges for gradual rollout on critical data
  • Bond Node - matches records across different entities, not within the same dataset
  • Data Normalization - clean field values before duplicate detection for better accuracy