Extract Invoice Data with AI
Invoice number, vendor, dates, total, currency — extracted into clean fields with strict no-inference rules, ready for accounts payable.
Overview
Invoices are semi-structured: values follow printed labels, but layouts vary by vendor. Reliable invoice extraction pins the fields (invoice_number, vendor, issue_date, due_date, total_amount, currency), gives each its own rule — identifiers exactly as written, dates in ISO format, totals as bare numbers with the currency in its own field — and runs strict: an invoice is the wrong place for a model's best guess. This resource loads the full accounts-payable setup with null for missing values, so downstream code sees stable keys.
Workflow
-
Generate and paste above the invoice text
The prompt reads the invoice from the input — paste OCR text or the email body below it.
-
Trust the per-field rules
total_amount comes back as a bare number, currency as an ISO code, invoice_number untouched — each field has its own rule.
-
Keep strict ambiguity for finance
A missing due date returns null; it is never inferred from "net 30 is typical". Wrong data costs more than no data here.
Why This Works
- Source guidance ("values follow printed labels") matches how invoices are actually read
- Splitting amount and currency prevents the classic "$1,840.50" parsing failure
- Strict policy plus null discipline makes absence loud instead of silently guessed
Best for
- Accounts-payable inboxes with many vendor layouts
- Automation flows that file or match invoices downstream
- Anyone whose model keeps "fixing" invoice numbers
Not for
- Deciding whether a document IS an invoice — that's classification
- Line-item tables at full depth — extract header fields first, items in a second pass
Use cases
- Processing emailed invoices into the accounting system
- Keeping totals numeric and currencies separate for clean math
- Extracting reference numbers exactly as printed, never reformatted