Nationalize
App · Contact Management · 4 actions
Predict which country a name most likely comes from. Send a surname like Nguyen or a full name like Sarah Johnson and Nationalize answers with up to five countries, each with a probability and the number of records behind it, so a workflow can route a lead to the right regional team, pick a language to write in, or fill in a likely country on a contact record. Names can be checked one at a time or a hundred at once.
Actions
| Action | What it does |
|---|---|
| Check a name against a country | Check whether a name is likely to come from one particular country: for routing a lead to a regional team or picking a language to write in. |
| Check your remaining names | Check how many names your Nationalize plan has left this billing period, so a workflow can hold off on a large batch rather than run out halfway. |
| Predict nationality | Predict which country a name is most likely to come from. |
| Predict nationality for several names | Predict where each name in a list is from, in a single request: for enriching a list of contacts or a spreadsheet column. |
Check a name against a country
Check whether a name is likely to come from one particular country: for routing a lead to a regional team or picking a language to write in. Answers yes or no with a probability rather than failing, and says which country the name really points to when the answer is no.
Inputs
- Name
nametextRequired - The name to place. A surname on its own works, and a full name works better -- 'Sarah Johnson' is a stronger prediction than 'Johnson'. Titles, initials and nicknames are fine, the order of the parts does not matter, and accents are optional.
- Country
countrytextRequired - The country to check the name against.One of:
AF(Afghanistan),AX(Åland Islands),AL(Albania),DZ(Algeria),AS(American Samoa),AD(Andorra),AO(Angola),AI(Anguilla),AQ(Antarctica),AG(Antigua & Barbuda), and 239 more.
Outputs
countrytext- The country it was checked against.
country_codetext- That country's two-letter code.
is_likelytrue/false- True when Nationalize lists this country among the name's likely origins.
nametext- The name checked.
probabilitynumber- How likely this country is, between 0 and 1. Zero when Nationalize does not list it at all.
ranknumber- Where this country places among the name's countries, 1 being the most likely. Empty when it is not among them.
recordsnumber- How many records the prediction is based on.
top_countrytext- The country the name most likely comes from.
top_country_codetext- That country's two-letter code.
top_probabilitynumber- How likely that country is, between 0 and 1.
urltext- Nationalize's website.
Example output
{
"country": "Japan",
"country_code": "JP",
"is_likely": true,
"name": "Hiroshi Tanaka",
"probability": 0.9312,
"rank": 1,
"records": 41208,
"top_country": "Japan",
"top_country_code": "JP",
"top_probability": 0.9312,
"url": "https://nationalize.io/"
}Check your remaining names
Check how many names your Nationalize plan has left this billing period, so a workflow can hold off on a large batch rather than run out halfway. Nationalize reports this only alongside a lookup, so the check itself uses one name.
Inputs
None.
Outputs
names_allowednumber- How many names the plan allows per period.
names_remainingnumber- How many are left in the current period.
names_usednumber- How many have been used in this period.
resets_attext- When the period resets, in UTC.
resets_in_daysnumber- How many days until it resets.
urltext- Nationalize's website.
Example output
{
"names_allowed": 2500,
"names_remaining": 2371,
"names_used": 129,
"resets_at": "2026-10-01T00:00:00Z",
"resets_in_days": 28,
"url": "https://nationalize.io/"
}Predict nationality
Predict which country a name is most likely to come from. Returns the most likely country on its own, so a step can use it straight away, with up to four runners-up behind it and the number of records the prediction rests on.
Inputs
- Name
nametextRequired - The name to place. A surname on its own works, and a full name works better -- 'Sarah Johnson' is a stronger prediction than 'Johnson'. Titles, initials and nicknames are fine, the order of the parts does not matter, and accents are optional.
Outputs
candidateslist- Up to five countries, most likely first.
countrytext- The most likely country's English name. Empty when not found.
country_codetext- The most likely country's two-letter code.
foundtrue/false- False when Nationalize has no records for this name.
nametext- The name that was looked up.
probabilitynumber- How likely the most likely country is, between 0 and 1.
recordsnumber- How many records the prediction is based on.
urltext- Nationalize's website.
Example output
{
"candidates": [
{
"country": "United States",
"country_code": "US",
"probability": 0.4104,
"rank": 1
},
{
"country": "United Kingdom",
"country_code": "GB",
"probability": 0.1386,
"rank": 2
},
{
"country": "Canada",
"country_code": "CA",
"probability": 0.0494,
"rank": 3
},
{
"country": "Australia",
"country_code": "AU",
"probability": 0.0377,
"rank": 4
},
{
"country": "Nigeria",
"country_code": "NG",
"probability": 0.0295,
"rank": 5
}
],
"country": "United States",
"country_code": "US",
"found": true,
"name": "Sarah Johnson",
"probability": 0.4104,
"records": 2147809,
"url": "https://nationalize.io/"
}Predict nationality for several names
Predict where each name in a list is from, in a single request: for enriching a list of contacts or a spreadsheet column. Takes up to 100 names at a time. Each name counts against your monthly quota, so repeated names are looked up only once.
Inputs
- Names
nameslistRequired - The names to place, one per entry. A surname works, a full name works better. Up to 100 at a time; repeated names are looked up once.
Outputs
found_countnumber- How many of them Nationalize could place.
name_countnumber- How many names were looked up.
resultslist- One answer per name, in the order given.
urltext- Nationalize's website.
Example output
{
"found_count": 2,
"name_count": 2,
"results": [
{
"candidates": [
{
"country": "Vietnam",
"country_code": "VN",
"probability": 0.5612,
"rank": 1
},
{
"country": "United States",
"country_code": "US",
"probability": 0.0353,
"rank": 2
}
],
"country": "Vietnam",
"country_code": "VN",
"found": true,
"name": "Nguyen",
"probability": 0.5612,
"records": 360408
},
{
"candidates": [
{
"country": "Germany",
"country_code": "DE",
"probability": 0.2245,
"rank": 1
},
{
"country": "Denmark",
"country_code": "DK",
"probability": 0.1201,
"rank": 2
}
],
"country": "Germany",
"country_code": "DE",
"found": true,
"name": "Schmidt",
"probability": 0.2245,
"records": 187032
}
],
"url": "https://nationalize.io/"
}