ListMatchGenie

International matching

International list matching across 20 regions — built for Madrid, Munich, and Manchester, not just Manhattan.

Most matchers were built for US data and break on international files. ListMatchGenie handles international matching across 20 regions natively — Spanish two-surname conventions, Dutch van der particles, German umlauts, Brazilian Portuguese surname chains, Scandinavian Å/Ø/Æ — without the 25-40% false-negative rates US-only matchers produce.

Built for: SaaS companies expanding beyond the US, multinational data teams, CRM cleanup projects spanning multiple countries, and anyone whose customer list has more than ASCII in it.

20

Regions validated end-to-end

6

Language families covered

1 engine

Auto-routed per row, no config

0 hacks

No regex tricks or string substitutions

Supported regions

20 regions, validated end-to-end.

Each regional module ships with validated handling of naming conventions, particles, diacritics, and postal-code formats. No config — the engine routes each row through the right module automatically.

English-speaking

6
  • United States
  • United Kingdom
  • Ireland
  • Canada
  • Australia
  • New Zealand

English-language naming conventions, regional postal formats (US ZIP, UK postcode, Canadian postal code, Irish Eircode, Australian postcode, NZ postcode), and Québécois St-Pierre ↔ Saint-Pierre equivalence.

Western Europe (DACH + Benelux)

4
  • Germany
  • Austria
  • Switzerland
  • Netherlands

German umlaut folding (Müller ↔ Mueller, Schloß ↔ Schloss), Swiss multilingual data (German/French/Italian), and Dutch particle handling (van, van der, van den, de).

Southern Europe

4
  • France
  • Spain
  • Italy
  • Portugal

Spanish two-surname convention (García López), Portuguese compound surnames with particles (da Silva, dos Santos), French accent folding (é/è/ê/ë → e), and Italian regional particles (di, della).

Nordic

3
  • Sweden
  • Norway
  • Denmark

Region-aware folding for Å/Ø/Æ — Åke ↔ Ake in Swedish, Søren ↔ Soren in Danish — and Nordic postal formats.

Eastern Europe

1
  • Poland

Polish diacritic folding (ł/ń/ś/ż/ć/ż/ź) and Polish postal format (NN-NNN).

Latin America

2
  • Mexico
  • Brazil

Mexican two-surname Spanish convention, Brazilian Portuguese particle matching (da Silva, dos Santos, de Oliveira), and local postal formats (CP, CEP).

Canonical list (20): US, UK, Ireland, Canada, Australia, New Zealand, Germany, Austria, Switzerland, Netherlands, France, Spain, Italy, Portugal, Sweden, Norway, Denmark, Poland, Mexico, Brazil.

Buyer's checklist

Where off-the-shelf matchers fail.

Six specific patterns to test on any matcher you're evaluating. Each one quietly destroys match quality if it's not handled — and most US-built tools don't.

Spanish full-name field with 4-5 tokens

A Spanish customer file often has a single column containing 'María Isabel García López Hernández'. The engine recognizes this pattern, identifies given names vs. paternal/maternal surnames, handles particles like 'de la' and 'del', and matches correctly against a master file with separate first_name and last_name columns.

What it costs you: Without this: a single column gets sliced at the wrong space, paternal and maternal surnames split into different keys, and the same person appears as 2-3 'unmatched' records. We've seen 25-40% false-negative rates on Spanish data with US-only matchers.

Dutch particle 'van der' preserved and matched

'Johan van der Berg' in one file matches 'J. van der Berg' in another, and both match 'Van der Berg, Johan' when a sort-order convention differs. The particle chain ('van', 'van der', 'van den', 'de') is preserved in display and folded appropriately for the match key.

What it costs you: Without this: the particle is treated as a token and 'van der Berg' becomes a soft fuzzy match against thousands of unrelated records — your 50K Dutch customer file balloons into a 300K-row review queue.

German umlaut folding per convention

Müller ↔ Mueller ↔ Muller — the engine applies German-specific folding (ä/ö/ü/ß → ae/oe/ue/ss) so all three records agree on the same match key. Display preserves the original character; matching operates on the folded form.

What it costs you: Without this: the same person appears three times because each spelling lives in a different match bucket. Your CRM thinks you have 3,000 customers when you actually have 1,000.

Québécois hyphenated saint-names

St-Pierre, Saint-Pierre, and St. Pierre all refer to the same Québécois surname. The engine normalizes saint-prefix abbreviations in French-Canadian data so these three variants land on the same match key without false positives against unrelated 'Pierre' records.

What it costs you: Without this: every Saint-/St-/St. variant gets fragmented OR the matcher over-corrects and merges 'St-Pierre' with 'Pierre Dubois'. Either way, your Québec data is unreliable.

Brazilian Portuguese da/dos/de particles

'Ana Paula da Silva dos Santos' in one file matches 'Ana P. Da Silva Dos Santos' in another. The engine treats Portuguese connector particles as surname-chain elements rather than independent tokens, so particle casing and spacing variance doesn't fragment the match.

What it costs you: Without this: case and spacing differences (' da ' vs ' Da ' vs 'da') split the surname chain across multiple keys. da Silva is the most common surname in Brazil — a 5% miss rate compounds fast on a 500K record file.

Scandinavian Å/Ø/Æ regional folding

Swedish, Norwegian, and Danish use overlapping but distinct folding conventions. The engine applies region-aware tables — Åke ↔ Ake in Swedish, Søren ↔ Soren in Danish, Ærø ↔ Aero in Danish geography — rather than a lossy one-size-fits-all ASCII strip.

What it costs you: Without this: a generic 'strip diacritics' approach turns Søren into Soren but also collapses unrelated names into the same bucket. You get under-matching AND over-matching at the same time.

See it in action

A real Spanish match, three rows.

Source file has full names in one column. Master has separate first_name + last_name. Most matchers fail on row 1.

Source file

spanish_customers.csv · full_name

Master file

customer_master.csv · first_name + last_name

María Isabel García López Hernández

María Isabel | García López

matched

Recognized 'María Isabel' as compound given name; treated 'García López' as paternal+maternal surname pair

Juan Carlos de la Vega Ruiz

Juan Carlos | de la Vega

matched

Preserved the 'de la' particle as a surname element rather than splitting it as three tokens

Ana del Río

Ana | Del Río

matched

Particle 'del' folded to lowercase for the match key; case difference preserved in display

All three matched in a single pass with no per-row config. Try the same file on a US-only matcher and at least row 1 will come back unmatched — "García López" gets treated as the last name and the engine never finds the match.

How does this stack up against other matchers?

Most competitors only validate US data. See side-by-side breakdowns of how we compare on international support, accuracy, and pricing.

One engine, no config

The engine auto-detects each row's region from postal code, country column, or name signals — and routes through the right module without you setting a flag. A single match job can span all 20 regions.

Region modules, validated

Each region ships with a validated test suite — known customer-data patterns, naming conventions, postal formats. Adding a new region means writing the module + the test data, not just sprinkling regex.

GDPR-ready data residency

EU customer data stays in Frankfurt. UK stays in London. US stays in the US. Our account database holds zero PII — only file references and aggregate stats — so regional isolation is a property of the architecture, not a checklist.

Roadmap

What's not in today's release.

We're transparent about scope. The following are on the roadmap but aren't in the product yet — we'll ship them when we can do them as well as we handle the current 20 regions:

  • CJK (Chinese, Japanese, Korean) — name-order conventions, transliteration ambiguity, and script composition need dedicated product work.
  • Right-to-left scripts (Arabic, Hebrew, Persian) — bidi rendering, multiple accepted romanizations, and patronymic conventions.
  • Indic languages (Hindi, Bengali, Tamil, and others) — transliteration complexity and multi-script realities in typical data.
  • Thai and Vietnamese — tone marks, name-order, and syllable segmentation.
  • Finnish — specific linguistic structure; may add later if demand warrants.

FAQ

International matching questions

Let the Genie handle the grunt work.

Free tier is real. No card. No forms. Just upload your first list and see the Genie clean and match it in under a minute.