Fuzzy Name Matcher
Paste person or organization names, then cluster duplicates or list matched pairs with Jaro-Winkler, Levenshtein or Soundex scoring. Runs locally in your browser.
About this tool
Fuzzy Name Matcher helps clean lists of people or organizations when the same entity appears under slightly different spellings. It is useful for CRM dedupe, sanctions or customer-list review, survey cleanup, vendor master data, and spreadsheet joins where exact equality is too strict.
Paste one name per line (or a single-column CSV where the first field is the name). The tool compares every unique name against the others, then either clusters them into canonical groups or lists every matched pair with a score. Duplicate identical rows are counted, so the most frequent spelling can become the canonical name for a group.
Algorithms
- Jaro-Winkler is the default. It is good for short names because it rewards a shared prefix and catches typos like
Jonathan SmithvsJonathon Smith. - Levenshtein uses a normalized edit-distance ratio. It is stricter and easy to explain: fewer insertions, deletions, or substitutions means a higher score.
- Soundex compares phonetic keys. It can catch names that sound alike, such as
Smith/SmythorRobert/Rupert, even when the letters differ.
Modes and outputs
Use groups mode when you want a deduplicated mapping table: each input spelling is mapped to a canonical name. Use pairs mode when you want review candidates: every pair at or above the threshold is listed with its score, best first.
The default table output is readable Markdown. Choose CSV when you want to join the mapping back to a spreadsheet, or JSON when another script needs structured groups or scored pairs.
Worked example
Input:
Dr. John Adams
John Adams Jr
Jon Adams
Maria Garcia
With Jaro-Winkler, threshold 85, case normalization on, and title/suffix ignoring on, the first three rows are grouped together and Maria Garcia stays separate. The output includes a mapping table from each original spelling to the canonical spelling.
Limits
This is deterministic fuzzy matching, not identity proof. A high score means two strings look or sound similar, not that two people are the same. Review borderline groups before merging production records. Very low thresholds can merge unrelated names; very high thresholds can miss typos. For large lists, pairwise comparison is O(n²), so thousands of unique names can take noticeably longer in the browser.
FAQ
What threshold should I start with?
Start with 85 for Jaro-Winkler. Lower it toward 80 when you want more recall and are willing to review false positives; raise it toward 90 or 95 when unrelated names are merging. For Soundex, 100 means the phonetic keys are exactly the same.
Should I use groups or pairs?
Use groups when your end goal is deduplication: it creates a canonical name and a mapping for every input spelling. Use pairs when you are reviewing candidate matches: it lists every pair above the threshold with its score, which is easier to audit before deciding how to merge.
How are titles and suffixes handled?
When Ignore titles and suffixes is enabled, common honorifics such as Mr, Ms, Dr, and Prof are removed from the beginning of a name, and suffixes such as Jr, Sr, III, PhD, and MD are removed from the end before comparison. The original spelling is still preserved in the output.
Can this deduplicate organizations too?
Yes, but organization names often need domain-specific cleanup. This tool will catch many aliases like Acme Corp and ACME Corporation, especially with Jaro-Winkler, but it does not know that Ltd, Limited, Inc, and LLC are legal-form variants unless their overall string similarity clears the threshold.
Will a matched group always be correct?
No. Fuzzy matching is a review aid. Common names, abbreviations, initials, and phonetic matches can create false positives. Treat the output as candidate groups, review the mapping, and keep an audit trail before merging records.
Developer & Automation Access
Run it from the terminal
Same engine as this page, headless — via the gizza CLI:
gizza tool fuzzy-name-matcher "Paste one name per line, or a single-column CSV:
Dr. John Adams
John Adams Jr
Jon Adams
Acme Corp
ACME Corporation"New to the CLI? Get gizza →
Open it by URL
Pre-fill and auto-run this tool with query parameters — the names match the API/CLI:
https://gizza.ai/tools/fuzzy-name-matcher/?names=Paste%20one%20name%20per%20line%2C%20or%20a%20single-column%20CSV%3A%0ADr.%20John%20Adams%0AJohn%20Adams%20Jr%0AJon%20Adams%0AAcme%20Corp%0AACME%20Corporation&algorithm=jaro_winkler&mode=groups&threshold=85&normalize_case=true&ignore_titles=true&output=tableMachine-readable descriptor: tool.json — title + parameters JSON Schema for agents.