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Use an extraction table when you need structured data out of documents at volume: one file per row, a schema you define, structured values in the columns. It works as a standalone job (export the rows, push them to a webhook), or as the upstream step that feeds a reconcile table where an agent works the actual process.
If you’re building a process — matching, checking, deciding, drafting — start with the agent quickstart instead. Reconcile tables can read files directly; you only need an extraction table when document volume makes pre-structuring worth it.

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1. Create an extraction table

  1. In your project, create a new table and choose Extraction.
  2. Give it a name and create it.
You land in the extraction table interface: your table in the center, the agent chat available for schema work.

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2. Upload files

  • Drag and drop files into the center of the table, or click Add Files to pick them from your computer.
  • API upload — send files directly via the API.
  • Workflow connectors — pull files automatically from connected systems (SharePoint, a shared mailbox, S3). See Integrations.
Uploaded files appear as rows with the status Waiting for run. PDFs, images, spreadsheets, audio, and video are supported.

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3. Define your extraction schema

Each column is one extraction task. You don’t have to write them by hand:
  1. In the agent chat, describe what you need pulled out of these files.
  2. The agent reads your files and your description, then proposes columns.
  3. Click Apply Schema to add them to the table.
Column names and descriptions matter — the model reads them. invoice_total_net with a one-line description beats col_4. Nested lists (invoice line items, order positions) are supported as array columns, so one file can produce a header record plus its line items.

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4. Run the extraction

  1. Select one or more rows.
  2. Click Run AI.
  3. Extracted values appear in the table as each run finishes.

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5. Review and refine

  • File viewer — open a row to see the original document side by side with the extracted values, so you can check any value against its source.
  • Iterate with the agent — ask it to add, remove, or adjust columns, then re-run.
  • Manual edits — add columns directly, or use the column menu to edit an existing extraction task.
  • Re-run after edits — select the rows you want reprocessed and hit Run AI again. Only the selected rows are reprocessed.

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6. Settings worth knowing

  • System prompt — high-level guidance applied to every extraction on this table (e.g. “dates are DD.MM.YYYY”, “amounts are net unless stated”).
  • Pipeline — the model configuration used for runs. See Extraction pipelines for the trade-offs.
  • Bounding boxes — records where in the source document each value came from. Useful for audit and human review; slower on every run.
  • Review mode — adds a human approval gate on extracted rows. See Review & approvals.
  • Bulk actions — select multiple rows to export or delete them.

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7. Get the data out

  • CSV or Excel — export the whole table or just the selected rows.
  • Webhook delivery — push each finished row to an endpoint of your choice.
  • API — poll for results with the async run pattern, or use the synchronous /extract call for one-file-at-a-time integrations.
  • Into a process — feed the rows into a reconcile table, where an agent works them against your master data following an SOP.

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Where to go next

Build a process on top

Structured rows are the input. The agent quickstart shows what works them.

Extraction pipelines

Pick the right pipeline for your accuracy and speed requirements.

Async run pattern

Upload → start → poll, the standard pattern for extraction at scale.

Connect real inputs

Feed the table from a mailbox, SharePoint, or a webhook instead of manual upload.