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 2026-10-15
- Can replace
- Manual data entry into Google Sheets or Excel
- Integrations
- Google SheetsZapier
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
A good fit 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.
Not the best fit for
- Buyers who want a long free test; the free allowance may cover only a few documents.
- Teams that need all processing kept inside the EU.
Strengths
- Reliable extraction when fields are clearly described, including from scanned PDFs.
- Webhooks and integrations can remove extra automation steps.
Limitations
- Some reviewers find it pricey, and one ran out of trial credits after one or two PDFs.
- Poor scans can give wrong data, and advanced rules are thinly documented.
- Samples are small: the Capterra figure comes from a search snapshot, and Product Hunt has only 4 reviews.
CONNECTED TOOLS
Integrations
Listed tools for this workflow. Confirm current support and setup with the provider.
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