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Field note · Customer education

Invoice processing AI pilot: from PDF to reviewed record

Test invoice AI using field accuracy, duplicate checks, exceptions and finance review. Includes a worked pilot and criteria for approving draft records.

Source
Editorial guidance with 1 retrieved source documents for context. Recommendations are our judgement.
Evidence
none on this page — it links to the pages that hold it

By Is There an AI for This? · Editorial guidance · Reviewed · How we use evidence

01Separate reading an invoice from approving a payment

Define the first deliverable as a reviewed invoice record ready for your existing accounting process. Specify the required fields: supplier, invoice number, date, currency, line items, total and purchase-order reference where applicable. Keep payment approval and supplier bank-detail changes outside the initial trial. A system that reads a number has not established that the amount is owed.

Collect a representative sample with different suppliers, credit notes, duplicate attachments, rotated scans and multi-page invoices. Include the document types finance rejects. Write the expected output for each field and mark genuinely missing values as missing. Do not reward a plausible guess when the source contains no answer.

02Build a workflow finance can inspect

  1. 01Receive and identify the originalAssign an intake reference and preserve the file. Repeated delivery of the same document should not silently create another accounting record.
  2. 02Extract with a source locationDisplay each proposed field beside the page or passage it came from. Allow empty fields with a reason. Keep tables and line items available for checking.
  3. 03Apply explicit checksValidate required fields, arithmetic, currency, supplier identity, purchase-order matching and potential duplicates through defined rules. Route failures to finance with a clear reason.
  4. 04Review a draft importFinance corrects or rejects the record. Log the final values and approval, then pass only the approved record into the existing posting process.

03Report accuracy by field and by business consequence

  • Measure supplier, invoice number, currency, totals and line items separately. An overall document score can conceal the field finance most needs to trust.
  • Measure the percentage requiring correction and the time to complete that correction. A draft that takes longer to repair than retype should count against the result.
  • Create named challenge cases for duplicates, credit notes, mismatched totals and altered bank instructions. The expected result may be an exception rather than an extracted record.
  • Test a failed import and a retry. Finance should be able to identify what was accepted and resume without posting twice.

Structured output is a format requirement, not a correctness score. An invoice record can contain the right field names and still carry the wrong currency or supplier. Use document evidence, business rules and review together. Treat a model’s self-reported confidence as something to evaluate against labelled examples, not as a purchasing guarantee.

04Account for the exception queue

Review exceptions by cause and supplier. A damaged scan suggests a capture problem; a missing purchase-order reference may need a business process change; an incorrect table extraction may need a different conversion method. Use the largest recurring cause to choose the next fix instead of repeatedly changing the wording of the prompt.

05Keep the first approval at the draft-record boundary

  • RECOMMENDATION

    For a finance team starting invoice automation, we recommend extraction into draft records with independent validation and human approval before posting.

    BasisThis depends on financial consequences and existing accounting controls. The vLLM structured-output documentation supports a formatting option; it does not prove extracted values are correct or an invoice is payable.

Illustrative acceptance criteria are a 25% reduction in total handling time, every designated duplicate and credit-note challenge handled correctly, all required source locations available and a successful retry without a duplicate import. Set field-specific accuracy thresholds with finance, and identify fields where any unreviewed error stops the pilot.

Stop if payment or bank details change without the existing approval, a duplicate record is posted, or the team cannot trace a value to the original invoice. Preserve the manual fallback and investigate the cause. Any later proposal for automatic posting needs its own restricted scope, authorisation controls and evidence; it is a separate release decision.

06Related

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Ask this as a question

  1. 01We receive hundreds of supplier invoices as PDFs. How can finance test AI extraction without allowing incorrect invoices or bank details into the payment process?

07Evidence

1 record
  1. 01
    Tier 2official vendor legal documentationVendor legal and technical documentation

    Structured Outputs - vLLM

    https://docs.vllm.ai/en/latest/features/structured_outputs/

    FreshRetrieved 25 Aug 2026sha256:1d9d9841283c

    1 record

08Primary-source reading

Consult these primary documents when checking a proposed product. Reading links remain available when evidence has not been verified; they do not establish that your planned use is approved or that a recommendation has been proven.