> For the complete documentation index, see [llms.txt](https://docs.feathery.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.feathery.io/platform/document-intelligence/post-processing.md).

# Post Processing

You can set up custom post-processing rules that run once the extracted document data is ready or approved.

## Logic Rules

In the `Logic` tab within your AI extraction, you're able to set up custom logic rules to transform & validate data, call APIs, trigger integrations, and more. They follow the same pattern as [advanced logic in forms](/platform/build-forms/advanced-logic.md).

<figure><img src="/files/pCuedPfjXYFJ76hi1TS6" alt="" width="375"><figcaption></figcaption></figure>

## Integrations

Extracted document data can be mapped and sent in your desired format to a set of over 140 native [integrations](https://feathery.io/integrations).

## API Requests

[API Connectors](/platform/build-forms/advanced-logic.md) let you configure API calls once and reuse them across any logic rule without writing complex code. Simply define your endpoint, method, headers, and parameters in one place, and reference the connector wherever you need it. Requests run securely from Feathery's backend, and if your endpoint requires it, you can whitelist the static originating IP address for an added layer of security. Find the IP address to whitelist [here](/platform/build-forms/advanced-logic/javascript-rule-builder/api-connections-in-code.md#ip-address-whitelist).


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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:

```
GET https://docs.feathery.io/platform/document-intelligence/post-processing.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
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.