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A Read documents action — also called an extract action — has Florent read your files and pull the values you care about into fields your team reviews. Set it up once, test it on a few sample files, then run it over many documents at once or use it as a step inside a playbook. This page covers building and activating an extract action. For what an action is and the other kinds, see Actions overview. To run a finished action over many files and review every result, see Batch action runs.
Building, testing, and activating actions is for Builders and Admins. If you open an action editor without access, you’ll see “You don’t have access to actions.” Ask an admin if you need it. See Roles and permissions.

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Create the action

1

Open the Actions library

In the sidebar, under Building Blocks, click Actions.
2

Start a new action

Click New action. In the New action dialog, under What should it do?, choose Read documents — “AI reads files and fills in fields your team review.”
3

Name it

Give it a clear Name (for example, “Extract key details”). Add an optional Description — this is what teammates see in the actions list. Then click Create action.
You land straight in the action editor with a Draft badge. A draft can’t be run yet — you’ll set its output and instructions, test it, then activate it.

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The extract action editor

The editor is a single page with two setup panels — Output and How it runs — and a Test bench on the right. The header shows the action name, the Draft badge, a Save button (active only when you have unsaved changes), and an Activate button. While the action is a draft, the header lists anything still blocking activation, such as:
  • “Add extraction instructions before activating.”
  • “Pick a record type for the output.”
  • “Add at least one output field.”
Work through both panels until those blockers clear and Activate lights up.

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Set the output — what each run extracts

The Output panel decides what comes out of every document. A toggle picks where the extracted data lands.
Choose Record type to write each result into rows of an existing record type — for example, an “Invoice” record type. Pick one from the Pick a record type dropdown. Each option shows the record type’s name, its field count, and its description, so two similarly named types stay distinct.The fields you extract are the record type’s fields. When the action runs and a result is approved, it becomes a record in the project.
If the dropdown is empty, you have no record types yet. Create one in Building Blocks → Record types first — see Build a record type — then come back.

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Set how it runs — instructions, mode, and model

The How it runs panel tells Florent what to look for and how to process your files.

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Instructions

Write plain-language Instructions describing what to pull from each document — for example, “Extract the title, date, and key details from each document.” This is the single biggest lever on quality. Be specific about what each field means and where it usually appears.

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Mode

The Mode dropdown sets how files map to results:

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Model

The Model preset picker chooses which AI model reads your documents. The default works for most cases; switch presets only if you have a reason to. For an exact model, set the Provider and Model below the preset.

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Test it on sample files

The Test bench on the right is a dry run. It proves your instructions and output fields work before you activate — and nothing it produces is saved. No records, no drafts, no files.
1

Add sample files

Click Add sample files and pick up to three representative documents. Only PDF and DOCX files are accepted. If you add more than three, only the first three are used.
2

Run the test

Click Run test. Florent reads each file and fills a results table with one row per field, showing the Field, the extracted Value, and a Score (how confident Florent is in that value).
3

Read the results and adjust

Check that each value is right. If a file yields nothing, you’ll see “No fields extracted.” If something looks off, refine your Instructions or Output fields and run the test again. Warnings appear in an amber banner above the table.
Test with your messiest, most awkward documents, not your cleanest ones. If the action handles the hard cases, the easy ones take care of themselves.
The score you see here is a preview of the same confidence signal you’ll use during review. When the action runs for real, each value also carries a citation back to the exact page and text it came from, so reviewers can verify before approving. See Reviewing AI work.

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Activate the action

When the activation blockers are gone — you have instructions and either a record type or at least one inline field — click Activate. The badge changes to Published, and the action becomes runnable: as its own batch action run and as an Action task inside a playbook.
A Draft or Archived action can’t be run. It won’t appear in the batch launcher’s action list until it’s Published.

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After it’s live

To edit a published action, open it, make your changes, and click Save. Changing the output or the run settings bumps the action’s version — if a playbook pinned an older version, re-activate the action so that playbook adopts the change. The Draft / Published / Archived lifecycle, versioning, and the archive-not-delete rule are the same for every action and are covered in Actions overview.

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Where to go next

Run it over many files

Launch a batch action run and review every extracted result in a grid.

Review what Florent extracted

Approve, correct, or reject each value using its citation back to the source.

Record types

Shape the record type your extracted values are written into.

Actions overview

See the action kinds and how built-in and custom actions differ.