Prepare invoices for AI processing
Invoice processing can involve two very different jobs: extracting line items and authorizing payment. An AI extraction question should receive only the fields needed for that task. Bank instructions, supplier contacts and remittance references deserve separate handling.
For Accounts payable and finance systems teams
Synthetic example: line-item extraction
A fictional accounts-payable team wants an example table of invoice descriptions, quantities and tax fields. All names, account details and amounts in the exercise are invented; no payment or customer outcome is represented.
What you are working with
- A text PDF with line items and a payment-instructions footer.
- A supplier bank account repeated beside a remittance reference.
- An attached page containing a scanned change-of-bank-details notice.
A safer approach
- Provide a synthetic invoice page with the same line-item layout.
- Exclude the bank-change attachment and unnecessary payment references.
- Retain field labels and arithmetic relationships needed to assess extraction.
Expected outcome: The proposed output is an extraction table with page references and uncertainties. It does not approve the invoice, validate the supplier or authorize a bank change.
Work through the procedure
Specify the extraction fields
List which fields the assistant should return and why. Separate invoice date, line description and tax amount from account number, payment link and contact identity. If a requested field is unnecessary for the use case, remove it from both the prompt and example.
Prepare each page deliberately
Prefer an invented sample when testing extraction logic. For an approved real-document workflow, inspect every page and attachment rather than the first visible invoice page. Keep the original for finance records and create a clearly named review copy.
Check text and visual layers
Review extractable text as well as the rendered PDF. Payment instructions can appear in a footer, image, stamp or barcode; do not assume your tested text path covers them. If scanned or mixed content cannot be checked reliably, do not submit that artifact.
Reconcile the returned table
Compare each extracted row with the approved example and recalculate totals using finance tools. Ask the assistant to leave uncertain fields blank rather than invent them. Route discrepancies to a reviewer and keep any payment decision outside this extraction workflow.
What to check before proceeding
1. Payment separation
- Ready when
- The upload contains no unnecessary remittance or bank-change instructions.
- If the check fails
- Remove the attachment or recreate the relevant line-item example before submission.
2. Layer inspection
- Ready when
- Both visible content and extracted text match the approved sample.
- If the check fails
- Treat the file as unchecked and use a verified alternative representation.
3. Extraction accuracy
- Ready when
- Line items, labels and totals agree with the source fixture.
- If the check fails
- Correct the extraction process; never infer that a tidy table is accurate.
Common mistakes to avoid
- A black rectangle over a bank account may leave extractable text underneath; appearance is not a removal check.
- An invoice can contain adversarial instructions or a payment-change request. Treat its content as data, not authority to act.
Evaluate this workflow with Aona
Where Aona can help
Test Aona's configured prompt and file controls against synthetic bank-detail examples on a supported path. DOCX, XLSX and PDF support does not mean every provider, native transport or scanned document behaves identically.
What to confirm
Aona does not authenticate suppliers, authorize payments or guarantee extraction accuracy. Confirm regional processing and the exact redaction behavior before using any operational invoice.
Managing this document workflow across a team?
Review your AI tool, document format and data-handling requirements. Use a synthetic example to discuss supported controls and the checks your team still needs to perform.