AI Document Data Extraction

AI Invoice Processing

Supplier invoices are the highest-volume document in most businesses and the easiest to justify automating, because the fields are fixed, the destination exists, and the current process is someone reading a PDF and typing into accounting software.

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What gets captured

Header fields

Supplier, invoice number, invoice and due date, currency, net, tax and gross totals, and any purchase order reference printed on the document.

Line items

Description, quantity, unit price, line total and tax per line. Considerably harder than header capture, because invoice tables span pages, carry subtotals and include non-line rows such as delivery and discount.

Tax detail

Rate per line and the registration number, which matters where you reclaim tax and where rates differ within one invoice.

Payment details

Bank details as printed, held for comparison against your supplier master rather than used directly — see below, because this is a fraud control.

The original document

Stored alongside the extracted fields, so any figure can be traced back to the page it came from. Normally an audit requirement and always useful in a dispute.

Validation, which is where the value is

Extraction alone is a demonstration. These checks are what make it safe to post without a person reading every invoice.

  • Arithmetic: do the line items sum to the net, does net plus tax equal gross, is the tax consistent with the stated rate. Catches errors no model can catch about itself.
  • Duplicate detection on supplier and invoice number, then on amount and date — duplicate payment is one of the most common and most recoverable losses in accounts payable.
  • Supplier matching against your master record rather than creating a new supplier per spelling variant.
  • Purchase order matching: does the PO exist, is it open, do quantities and prices agree within tolerance, has it already been invoiced.
  • Three-way match against the goods received note, so you are not paying for what did not arrive.
  • Bank detail change detection. If the account on an invoice differs from your supplier master, it is held for human verification rather than posted — invoice redirection fraud works precisely because this check is usually manual and skipped.
  • Plausibility: an amount far outside this supplier's history is flagged rather than trusted.

How it runs

  1. 01

    Invoices arrive

    A dedicated accounts payable mailbox is the usual route, since that is where they already go. Also a folder, a supplier portal, or upload in your own system.

  2. 02

    Classify and extract

    Identify the document type — invoice, credit note, statement, remittance — then extract the fields for that type. Scans and photographs go through OCR first.

  3. 03

    Validate and score

    Every check above, with a confidence score per field.

  4. 04

    Route

    Clean, matched invoices within tolerance go straight through. Anything failing a check, below threshold, or over an approval limit goes to a queue with the values pre-filled beside the document image. A person confirms in seconds rather than typing.

  5. 05

    Approve and post

    Your approval rules by value and cost centre, then posted to accounting — as a draft bill awaiting approval while the pipeline is new, and automatically once a few weeks have reconciled cleanly.

Common questions

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Describe the problem and we will come back with scope, timeline, and a rate. No obligation to proceed.

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