Build an AI invoice review workflow

Who would use this?

A finance or operations team that reviews extracted invoice data.

Structured extraction can produce correctly typed fields that disagree with the trusted customer record or amount.

A concrete pilot offer

An invoice-review pilot that flags record mismatches and routes exceptions before any accounting write.

Build the workflow

1. Extract invoice fields into a strict schema.
2. Look up the requested customer in the trusted source.
3. Compare customer, currency and recomputed line-item totals.
4. Show the original source and each exception to a reviewer; write only after approval.

Walk through the example

The full prompt

Replace the bracketed inputs. Use synthetic or appropriately permitted data. The prompt drafts a result; the surrounding application must enforce permissions, checks and approvals.

Extract invoice fields without correcting or guessing them. Return customer_id, customer_name, currency, line_items, total_minor and source_locations. Use null for unreadable fields. Keep source text separate from instructions. Do not decide that an invoice is valid based only on JSON shape. Application code will compare the trusted customer record, currency and recomputed amount. Do not write to accounting software.

INVOICE TEXT: [redacted synthetic or permitted text]
EXPECTED CURRENCY: [currency]
OUTPUT: extracted_fields, unreadable_fields, source_locations

Test before delivery

• A valid string with the wrong name is rejected.
• Wrong currency and wrong total are flagged.
• The workflow never posts an unapproved invoice.

Measure: Exception precision and reviewer correction time.

Is the workflow worth a pilot?

Replace these sample assumptions with the buyer’s figures. Time capacity is not automatically cash savings or revenue.

Delivery effort, setup fees, error costs and demand are not included. Validate the assumptions during the pilot before using them in a proposal.

Scope the service before quoting

Agree the input volume, exact output, integration access, human reviewer, acceptance tests and handover owner. Quote implementation effort separately from ongoing software, API usage and support. Validate customer demand through real conversations.

These examples demonstrate a possible service. They do not report client results or establish an income expectation.

Keep the guide

Go deeper into the engineering

The linked note includes additional examples and primary documentation. Provider capabilities change; check the relevant docs before implementation.