What is invoice data extraction?

What gets extracted

The fields you would otherwise type into your accounting system by hand:

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

Why accuracy is the real problem

Any modern model can read a clean invoice. Errors show up later, on the skewed scan or the vendor with an odd layout. One field is wrong, nobody notices, and the books are off by a few hundred dollars.

If you still have to re-check every field, the tool saved you nothing. Checking is the slow part of keying invoices. What you want from extraction is a clear signal for which fields need a second look.

A worked example

An invoice shows line items of 1,240.00, tax of 248.00 and a printed total of 1,488.00. On a slightly skewed scan, the tax is read as 24.80. Subtotal plus tax now comes to 1,264.80, which doesn't match the printed total. A math check catches the gap and flags the tax field. You look at one number instead of re-reading the page.

How InvoiceJet handles it

Two independent AI models read every invoice and have to agree. Then the math is checked: line items must add up to the subtotal, and subtotal plus tax must equal the total. Each field gets a confidence level and shows the text it was read from. Invoices where everything checks out are marked verified with no clicks. The rest go to a review queue with the evidence attached.