ACCOUNTS PAYABLE
Invoice data extraction built for accounts payable
Turn incoming invoices into validated records your ERP can post. Talonic captures header and line-item fields, reconciles totals and tax, matches against purchase orders, and routes only the genuine exceptions to a person. The result is fewer keystrokes, a shorter exception queue, and a clear audit trail on every value.
The accounts payable bottleneck
An AP team does not have a reading problem. It has a keying-and-checking problem. Invoices arrive as PDFs, scans, and email attachments from hundreds of vendors, and someone has to type the numbers into the ERP and verify they are right. That manual step is where cost, delay, and error accumulate, and it is the step that does not scale when volume grows.
Invoice data extraction removes the keying and front-loads the checking. Captured fields are validated at the moment of extraction, so the invoices that need a human get one and the rest flow straight through. This is the use-case view; if you want the mechanics of how parsing turns a document into fields, see invoice parsing.
Header and line-item fields, fully typed
Header
- Vendor name, address, tax ID
- Invoice number and PO number
- Invoice date and due date (ISO 8601)
- Currency, net, tax, and gross totals
- Payment terms, IBAN, remittance
Per line item
- Description and SKU or item code
- Quantity and unit of measure
- Unit price and line total
- Line-level tax rate and amount
- Cost center or project code
Validation catches errors before they post
Totals reconciliation
Net plus tax must equal the gross total, and the sum of line items must equal the net. A mismatch is flagged before the invoice posts, not after a payment run.
PO and quantity match
Extracted PO numbers, quantities, and unit prices are checked against the purchase order so over-billing and quantity drift surface at capture time.
Duplicate and confidence gates
Repeated invoice numbers from the same vendor are caught, and any field below your confidence threshold is held for a quick human review instead of flowing through unchecked.
Each validation result is attached to the record with the source page and region, so a reviewer sees not just that something failed, but where on the invoice to look. On a 930-document logistics benchmark, a customer measured accuracy rising from 75% to 92% across review cycles using this confidence-gated, validated approach.
From extraction to your ERP
Validated invoice records are delivered to a system of record over webhook, S3, SFTP, or the API. Whether you post to NetSuite, SAP, Microsoft Dynamics, or a data warehouse, the output is the same schema-validated shape, so the integration does not change when a new vendor appears. Processing runs on EU-resident infrastructure in Germany West Central and is GDPR aligned, which matters for finance teams under data-residency requirements.
Frequently asked questions
What does invoice data extraction capture?+
Both header and line-item data. Header fields include vendor, invoice number, PO number, invoice and due dates, currency, tax, and net and gross totals. Line items capture description, quantity, unit price, and line total per row. Everything comes back as typed JSON: dates normalized to ISO 8601, amounts as numbers, each mapped to a stable key so the shape stays constant across vendors.
How does it fit accounts payable automation?+
Invoice data extraction is the capture step of an AP workflow. It replaces manual keying: a received invoice becomes a validated record that posts to your ERP or accounting system over webhook, S3, or SFTP. Because output is schema-validated and reconciled, fewer invoices fall into the exception queue, and the ones that do arrive with a clear reason and a pointer to the source region.
What kinds of errors do the validation checks catch?+
Totals that do not reconcile, line items that do not sum to the net, tax computed incorrectly, quantities that exceed the matching purchase order, and duplicate invoice numbers from the same vendor. These checks run at extraction time, so a bad invoice is flagged before it reaches your payment system rather than after.
Does it work across many vendors without setup per vendor?+
Yes. Extraction runs against a schema rather than a per-vendor template, so a new supplier maps to the same output fields without a template build. The Field Registry remembers fields seen across your corpus, so recognition of vendor-specific quirks compounds as you process more invoices.
Where is the data processed, and how do I start?+
Processing runs on EU-resident infrastructure in Germany West Central, GDPR aligned. Start free with no credit card in the dashboard, or call the extraction API directly. Paid usage is credit-based at 1,000 credits per euro.
Automate invoice capture
Send a batch of real vendor invoices and see the validated records come back. Start free with no credit card, then scale on usage-based pricing.
Building this into software? Use the data extraction API or the free invoice extraction tool.