An AI invoice data extraction agent reads the header section of each invoice and extracts the supplier identity, invoice number, dates, currency, subtotal, tax, total, PO number and remittance details.
Starts from the text and layout of the document.
Reads vendor, number, dates, currency and totals.
Confirms that subtotals and totals agree.
Marks fields that disagree or look odd.
A person fixes flagged fields.
Confirmed values are saved against the invoice.
It checks the values against each other, for example that subtotal plus tax equals the total and that the due date follows the terms stated.
It records where on the page each value was found, so a reviewer can verify it in a click and a later audit can follow it.
Header fields drive matching, tax and payment, so one wrong value spreads through every later step.
Invoice date, tax point and document date can look alike.
References appear in notes or footers rather than tidy boxes.
Totals before and after tax, discount and deposit coexist.
Sign and wording are easily misread.
A wrong supplier or amount is found only at payment or audit.
Finds supplier, buyer, reference and totals areas on the page.
Reads each value and converts dates, currency and amounts into one standard form.
Tests totals, tax and dates against each other and the stated terms.
Stores the position of each value on the page for later verification.
Marks low-confidence or inconsistent fields for review and passes the rest on.
Bring a small sample and watch Flo work through it.
| Aspect | By hand | With the agent |
|---|---|---|
| Field finding | Clerks hunt for the invoice number among several references. | Header fields are located and labelled. |
| Dates | Invoice and due dates are confused. | Each date is identified by its role. |
| Totals | Totals are accepted without recomputing. | Arithmetic is checked on every invoice. |
| Layout changes | Each vendor layout trips up fixed position rules. | Fields are read by meaning, not position. |
| Corrections | Mistakes are fixed without a record. | Edits are logged with the original value. |
| If | Then | Who decides |
|---|---|---|
| Subtotal plus tax does not equal the total | Flags the invoice for a person | AP specialist |
| Two possible invoice numbers appear | Offers both and asks for a choice | AP clerk |
| Due date is missing | Derives it from terms and marks it derived | AP specialist |
| Currency symbol is ambiguous | Flags it and does not assume | AP specialist |
| Vendor name differs from the vendor record | Passes to vendor matching with the doubt noted | AP specialist |
| Field read with high confidence and consistent | Accepts it without a review | None needed |
The agent shows the full string and the shorter variants. A clerk chooses the one the vendor uses.
The agent flags the conflict and quotes both. A specialist checks the source.
The agent notes the document type. AP asks the vendor for the proper invoice.
The agent states the format it assumed and flags ambiguity. A clerk confirms.
Talk it through with the team. We will map it to your steps and show where a person stays in control.
Begin with number, date, total and vendor.
Extend to due date, tax and currency.
Record what people change and fix repeated causes.
Narrow human checks to doubtful fields only.
Look at accuracy per field. If invoice number and total are strong but PO number is weak, you know where to improve the reading or the supplier instructions.
Quick answers to what people ask most about invoice data extraction.
Book a demoIt is automatically reading values from an invoice, such as supplier, number, dates, amounts and tax, and storing them as structured data ready for matching and coding.
Header extraction covers the invoice as a whole. Line item extraction reads each billed row, including description, quantity, unit price and amount.
The agent checks values against each other, such as totals against tax, and flags low-confidence fields. A reviewer confirms them against the original.
It can read many languages and formats, and reviewers should check results in any language where your team has seen errors.
What is invoice capture?
→What is invoice line item extraction?
→What is invoice spam filtering?
→What is invoice vendor matching?
→Bring a small sample of real items. We will show what Flo handles on its own and where it hands over to your team.
