Free Tool

Levenshtein Distance

Calculate the minimum number of single-character edits (insertions, deletions, substitutions) needed to transform one string into another.

Tilores uses Levenshtein distance in production — so you can automate matching with rules you configure.

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String A

String B

Edit Distance

Match

How it works

The Levenshtein distance is one of the most widely used string metrics in entity resolution. It measures how different two strings are by counting the minimum number of operations needed to transform one into the other. A distance of 0 means the strings are identical. In entity resolution, Levenshtein distance helps catch typos, misspellings, and minor data entry errors — "Jhonson" vs "Johnson" has a distance of just 1.

Use cases in entity resolution

Typo detection in customer names

Address matching with minor variations

Email similarity checking

Data quality monitoring

Related tools

Jaro-Winkler Distance

Measure string similarity with extra weight for matching prefixes — ideal for name matching in entity resolution.

Soundex Phonetic Algorithm

Encode strings by their phonetic sound — names that sound alike produce the same Soundex code.

Don't implement this yourself

Tilores Studio runs the full matching engine — this algorithm plus configurable rules and real-time entity resolution — locally on your machine. Free, no account, no cloud. Load your own data and see it working in minutes.

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