> For the complete documentation index, see [llms.txt](https://docs.truecaller.com/truecaller-for-business/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.truecaller.com/truecaller-for-business/spam-management/how-is-spam-calculated-on-truecaller.md).

# How is spam calculated on Truecaller?

Truecaller uses a smart algorithm powered by machine learning to help identify spam calls. It doesn’t just rely on the number of spam reports—it looks at a combination of factors.

There is no fixed number of reports that automatically marks a number as spam. Instead, our system analyses overall patterns and behavior over time. This helps us make sure that genuine businesses are not wrongly flagged, while still protecting our users from unwanted or suspicious calls.


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# 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 dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://docs.truecaller.com/truecaller-for-business/spam-management/how-is-spam-calculated-on-truecaller.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
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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.
