> For the complete documentation index, see [llms.txt](https://help.screena.ai/resources/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://help.screena.ai/resources/faq/name-matching/what-are-name-cultures-and-how-does-screena-handle-them.md).

# What are name cultures and how does Screena handle them?

## What is a name culture?

At Screena, "name cultures" refer to the categorization of names based on the cultural context in which they are used, recognizing the diversity in name conventions across different regions and cultural groups. This approach goes beyond simply classifying names by language or alphabet.

## **How does Screena identify and use name cultures?**

Our system uses an automated process to identify individual name cultures, which are represented using romanized scripts and assigned a two-letter code based on the [ISO 639-1](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes) standards. By doing so, we enhance the accuracy of name matching through the deployment of AI models tailored to specific cultural contexts.

For example, the name "Jean Dupont" is typically associated with French culture and is processed using models that are sensitive to French naming conventions. Similarly, "Christopher Harris" would be recognized under an English-speaking culture, using models designed for English names.


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation by asking a question.

Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters:

```
GET https://help.screena.ai/resources/faq/name-matching/what-are-name-cultures-and-how-does-screena-handle-them.md?ask=<question>&goal=<user_goal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.
