The function that processes supplier invoices and pays them.
AP teams receive invoices in dozens of layouts, code them to GL accounts, route for approval, and pay. Document extraction collapses the data-entry step — vendor, invoice number, amount, due date all parsed in one upload — letting the team focus on coding and approvals instead of typing.
Converting a PDF bank statement into a clean transaction table.
Statement formats vary across banks — column orders, date formats, balance/credit/debit conventions. Reliable parsing extracts every transaction (date, description, amount, running balance), opening and closing balances, and account metadata, normalizing them into a single schema regardless of source bank.
Regulatory verification of customer identity, often using passport or ID document extraction.
Financial-services and crypto onboarding requires verified identity data — name, DOB, document number, expiry. Passport extraction feeds the KYC pipeline by turning a phone-photo upload into the structured fields a verification system can match against sanctions lists and existing customer records.
Individual rows on an invoice, receipt, or order — one per product or service.
Each line item typically has a description, quantity, unit price, and amount. Extracting line items reliably is harder than extracting top-level fields like vendor or total because they live in tables that span pages, wrap rows, or include subtotals — and because the schema must accommodate variable counts.
The two- or three-line code at the bottom of a passport, encoded for automated reading.
The MRZ encodes name, passport number, nationality, date of birth, expiry, and a check digit in a fixed-width OCR-B font. Border-control kiosks read it. So does ExtractFox's passport extractor — pulling MRZ values out of a phone photo or scan and exposing the parsed fields as structured data.
Matching transactions across systems — typically bank statements against the general ledger.
Reconciliation is the close-process bottleneck for most accounting teams. Extracting bank statements into a clean transaction table — date, description, amount — turns reconciliation from manual data entry plus matching into matching alone.
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