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Airparser

Airparser turns order emails, invoices, scans and images into spreadsheet rows from fields you describe in plain words, and can hold low-confidence documents for a person to approve first.

Not tested by JuicyAgents · Reviewed 15 Oct 2026

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Can replace
Manual data entry into Google Sheets or Excel
Integrations
Google Sheets, Zapier
Independent review
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Overview

About Airparser

Airparser turns emails, PDFs, scans and images into structured data. You forward documents to an inbox or upload them, then describe the fields you want in plain words. It reads order emails, invoices, receipts and handwritten forms without templates or training data. The results go to Google Sheets, Excel, CSV or JSON, or on to other apps through Zapier, Make, n8n and webhooks.

A review step can hold a document when any field falls below a confidence level you set, or when your own checks fail. A person then compares the data with the original, corrects it and approves it before it reaches your sheet or other tools. Without that step, data flows straight into connected apps, so turn it on for anything that matters. Airparser says it is GDPR-compliant and does not use your documents to train AI models. The free allowance is small, so test your most important document types first.

What the workflow does

  • Plain-language extraction: Describe the fields in your own words; no templates, training data or code are needed.
  • Wide input support: Reads emails and attachments, native and scanned PDFs, Word files, images, HTML, CSV and handwriting.
  • Human review queue: Holds documents with low-confidence fields or failed checks until a person corrects and approves them.
  • Exports and integrations: Sends data to Google Sheets, Airtable, webhooks and the API, or through Zapier, Make and n8n.
  • Post-processing checks: Custom Python rules can check totals or required fields and send failures to review.

From input to result

How the workflow works

You provide

  • Sample emails, PDFs, scans or images.
  • Plain descriptions of each field to extract.
  • A confidence threshold and any validation rules.
  • A destination such as Google Sheets or a Zapier workflow.

You get

  • Order and invoice details captured as spreadsheet rows.
  • Low-confidence documents checked by a person before export.
  • Less manual copying from emails and PDFs.

  1. Create an inbox, send a few real documents and describe the fields.

  2. Turn on review with a confidence threshold, then correct and approve held documents.

  3. Connect the destination and review held documents and a sample of exports each week.

What it offers

Key features

  • Plain-language extraction

    Describe the fields in your own words; no templates, training data or code are needed.

  • Wide input support

    Reads emails and attachments, native and scanned PDFs, Word files, images, HTML, CSV and handwriting.

  • Human review queue

    Holds documents with low-confidence fields or failed checks until a person corrects and approves them.

  • Exports and integrations

    Sends data to Google Sheets, Airtable, webhooks and the API, or through Zapier, Make and n8n.

  • Post-processing checks

    Custom Python rules can check totals or required fields and send failures to review.

Is it a good fit?

Who it’s for

  • Small businesses that receive orders or invoices by email and log them in a sheet.
  • Teams that want a review step before extracted data reaches other tools.

Strengths

  • Reliable extraction when fields are clearly described, including from scanned PDFs.
  • Webhooks and integrations can remove extra automation steps.

Connected tools

Integrations

  • Google Sheets
  • Zapier

Listed tools for this workflow. Confirm current support and setup with the provider.

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