> ## Documentation Index
> Fetch the complete documentation index at: https://agents.nanonets.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Extract data

> Use agents to read documents and turn them into clean, structured data.

Data extraction means the agent reads a document and pulls out the information you care about.

For example, an agent can read an invoice and return the vendor, invoice number, date, line items, and total.

<Frame caption="A Data Extraction result: structured fields (invoice number, vendor, dates, total) plus a line-items table you can review and download.">
  <img src="https://mintcdn.com/nanonets-fb7e8f2a/eksLpltcNl29FEi2/images/extraction-result.png?fit=max&auto=format&n=eksLpltcNl29FEi2&q=85&s=da221612bc2051df767d3b52816f4806" alt="Data Extraction result panel showing extracted invoice fields and a line-items table" width="575" height="580" data-path="images/extraction-result.png" />
</Frame>

## What files can be used

Agents can work with common business files, including:

* PDFs
* Images
* Word documents
* Excel spreadsheets
* CSV files
* PowerPoint files
* Text and Markdown files

## How extraction works

<Steps>
  <Step title="Add the document">
    Upload a file or let the agent receive it from email, a folder, or another connected app.
  </Step>

  <Step title="Tell the agent what to find">
    List the fields you need, such as invoice number, vendor name, due date, total, and line items.
  </Step>

  <Step title="Review the result">
    The agent returns the data in a table or structured result. When available, it also shows where the value was found in the document.
  </Step>

  <Step title="Send the data where it belongs">
    The agent can add the data to a spreadsheet, create a file, update a business system, or return it to the user.
  </Step>
</Steps>

## Define the fields you need

Be specific about the fields the agent should extract.

Good examples:

* "Extract invoice number, vendor name, invoice date, due date, subtotal, tax, total, and currency."
* "Extract each line item with description, quantity, unit price, and line total."
* "If a purchase order number is missing, mark it as missing and ask the user."

<Tip>
  Use the field names your team already uses. This makes the result easier to review and export.
</Tip>

## Use source checks

For many documents, the agent can show where a value came from in the original file.

This helps reviewers quickly answer:

* Did the agent read the right value?
* Is the value missing from the document?
* Did the agent confuse two similar fields?

## Add quality rules

Rules help the agent catch common problems.

Examples:

* "The invoice total must equal subtotal plus tax."
* "The due date must be after the invoice date."
* "Every line item must have a quantity and unit price."
* "Ask for review if the total is above \$10,000."

## Improve over time

When you correct an extraction, turn that correction into a rule.

For example, if a vendor writes their name in three different ways, teach the agent the final name your team wants to use.

Learn more in [Rules](/docs/guide/rules).