Operations

Extract Invoice Data with AI

Invoice number, vendor, dates, total, currency — extracted into clean fields with strict no-inference rules, ready for accounts payable.

Open in Extraction Prompt Generator

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.

How to use this resource

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

FAQ

What fields does the invoice extraction prompt return, and in what format?

It returns six fields as a single JSON object: invoice_number, vendor, issue_date, due_date, total_amount, and currency. Each has its own rule — the invoice number is kept exactly as written, dates come back in ISO YYYY-MM-DD, the total is a bare number with no symbols or thousands separators, and the currency sits in its own field as an ISO code like USD.

What do I paste into the invoice extraction prompt?

Paste the invoice text — OCR output or the email body — directly below the prompt; it reads the invoice from the input. You don't reformat anything first: the prompt's reading guidance knows values follow printed labels like "Invoice #" and "Total Due," and that the grand total is the final amount after tax. One invoice per run keeps the fields clean.

Does this guarantee the extracted invoice data is correct?

No — it pulls only what is explicitly printed in the source and never infers, so accuracy depends on the invoice being legible and its labels clear. Anything not found comes back as null rather than a guess, which makes gaps loud instead of silent. Run it in your own AI tool and reconcile the fields against the invoice before they reach accounts payable.

Can it extract line items, or add fields beyond the six?

Not line items — this setup extracts the header fields; for a full line-item table, run a second pass focused on the rows. To capture different fields, regenerate the prompt in the Extraction Prompt Generator with your own field list and per-field rules. Deciding whether a document even is an invoice is a separate classification job, not this extraction.

Customize This Resource

Opens this setup in Extraction Prompt Generator. Generate to get the full extraction prompt — then adjust the fields, missing-data behavior, and ambiguity policy.

Open in Extraction Prompt Generator

Prompt Template

Copy it as-is, or use Open in Extraction Prompt Generator to load it pre-filled and customize it with your own context.

TASK
Extract billing details from the invoice in the input for our accounts payable system.

SOURCE
The input is an invoice — semi-structured billing text where values usually follow printed labels.
Reading guidance: Values follow labels like "Invoice #" and "Total Due"; the grand total is the final amount after tax.

FIELDS TO EXTRACT
- invoice_number (required): Invoice reference number
- vendor (required): Issuing company name
- issue_date (required): Date the invoice was issued
- due_date (optional): Payment due date
- total_amount (required): Grand total after tax
- currency (required): Currency of the total

EXTRACTION RULES
- invoice_number: Extract the identifier exactly as written — do not reformat or pad it.
- vendor: The organization's name as written, without surrounding commentary.
- issue_date: Use ISO format (YYYY-MM-DD) when the date can be determined; otherwise extract it as written.
- due_date: Use ISO format (YYYY-MM-DD) when the date can be determined; otherwise extract it as written.
- total_amount: Extract the numeric value only — no currency symbols or thousands separators.
- currency: Use the ISO currency code (e.g. USD, EUR).
- Extract values from the source text only — do not use outside knowledge to fill fields.
- Do not summarize or interpret the document — return only the fields listed above.

MISSING DATA
- If a field is not found in the source, set it to null — do not omit the key.
- Never invent or guess a value for a missing field.

AMBIGUITY POLICY
- Extract only information explicitly stated in the source text — do not infer values.
- If a value is implied but not stated, treat the field as missing.

OUTPUT FORMAT
Return the extracted data as a single valid JSON object using the field names below.

EXAMPLE OF A VALID EXTRACTION
{
  "invoice_number": "INV-2041",
  "vendor": "Acme Corp",
  "issue_date": "2026-06-10",
  "due_date": "2026-06-10",
  "total_amount": 1840.5,
  "currency": "USD"
}

OUTPUT RULES
- Return only the JSON object — no text before or after it.
- Do not wrap the output in markdown code fences.
- Use the field names exactly as defined — no renaming, no extra fields.

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