What is invoice data extraction?

What gets extracted

A useful extraction captures the fields you would otherwise type by hand:

• Vendor name and contact details
• Invoice number and purchase order number
• Issue date and due date
• Subtotal, tax, and total, with the currency
• Line items: description, quantity, unit price, and amount

Why accuracy is the whole problem

Reading an invoice once is easy. Any modern model can do it, and every tool demos well on a clean invoice. The problem shows up on invoice 40: the model guesses wrong on one field, nobody notices, and the books are off.

This is the failure the industry does not talk about. Extraction that still needs a human to re-check every field saved nothing, because checking is the slow part. What matters is whether it knows when it got the invoice wrong, not just whether it can read the invoice at all.

How InvoiceJet handles it

InvoiceJet runs two independent AI models on every invoice and requires them to agree. It also runs deterministic math checks: line items must sum to the subtotal, and subtotal plus tax must equal the total. Only when the models agree and the math holds is a field marked high confidence.

Every value carries a confidence level and cites the exact spot on the document it came from, so you verify a field in one click instead of re-reading the whole page. Clean invoices auto-verify with zero clicks; anything less confident is queued for review with the evidence attached.

A worked example

Take an invoice with line items totaling 1,240.00, tax of 248.00, and a printed total of 1,488.00. The reader pulls the subtotal correctly, but on a slightly skewed scan it reads the tax as 24.80. Subtotal plus tax now comes to 1,264.80, which does not match the printed total. That gap is what gets the tax field flagged and queued instead of quietly posting a wrong number to your books. The math check does the catching; a human only sees the one field that failed.