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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.

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  • GDPR
  • HIPAA
  • ISO 27001 / 42001 aligned
  • DIN SPEC 91491
  • EU-resident infrastructure

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.

Where extraction sits in the AP workflow

Invoice data extraction is one stage in a longer pipeline, and automation projects succeed or fail on the hand-offs between stages. A working accounts payable setup runs in five:

  1. Stage 1

    Capture from every channel

    Invoices arrive by email, vendor portal, scanner, and API upload. Each lands in one intake queue regardless of channel, so a photographed paper invoice and a born-digital PDF follow the same path from the first minute.

  2. Stage 2

    Extract and validate in one pass

    Talonic reads the document against your invoice schema and returns typed fields. Validation runs in the same pass: totals are reconciled, tax is recomputed, and every value carries a confidence score, so the record that leaves this stage has already been checked.

  3. Stage 3

    Match against the purchase order

    Two-way and three-way matching compares the extracted invoice to the PO and, where you use one, the goods receipt. Price and quantity drift is flagged per line rather than per invoice, so a reviewer sees exactly which row disagrees.

  4. Stage 4

    Route approvals and exceptions

    Clean invoices move straight toward posting. The rest go to a person with the failure reason and the source region attached, which turns exception handling from a re-keying job into a look-and-confirm job.

  5. Stage 5

    Post and archive

    The validated record posts to the ERP over webhook, S3, SFTP, or the API, and the original document stays linked to every extracted value for audit.

The number to watch across the whole pipeline is the touchless rate: the share of invoices that post with no human touch. Extraction quality, validation, and PO matching are the three levers that raise it.

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

Line items are where extraction earns its keep

Header fields are the easy half. Most stalled AP automation projects die on the line items: tables that continue across pages, description cells that wrap over three lines, credit rows with negative amounts, and freight or surcharge lines that are not products at all. Talonic reads the table as a table. Rows that span a page break are stitched back together, wrapped descriptions stay one row, and repeated column headers never show up as data.

Each line comes back typed: quantity as a number with its unit of measure, unit price and line total as decimals in the invoice currency, and the line-level tax rate where the vendor prints one. Because the sum of extracted lines has to reconcile against the extracted net total, a missed or duplicated row is caught arithmetically at capture time instead of surfacing weeks later in the general ledger. That is the difference between invoice data you can post and text you still have to check.

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.

How strict to be is a dial, not a constant. Teams typically start with a conservative confidence threshold, watch the exception queue for two or three cycles, and raise the auto-accept line as measured accuracy climbs. Fields carry independent scores, so you can auto-accept a vendor name at 0.93 while routing every IBAN below 0.99 to a person: the cost of a wrong value differs per field, and the gate should too.

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.

Extract invoice data through the API

The same extraction that runs in the dashboard is one HTTP call. POST the document with your invoice schema and read back typed JSON with line items and confidence per field. Batches run asynchronously with webhook callbacks, so an AP system can submit a full night of intake in one job and receive validated records as they finish.

Request

curl -X POST https://api.talonic.com/v1/extract \
  -H "Authorization: Bearer $TALONIC_API_KEY" \
  -F "file=@vendor-invoice.pdf" \
  -F 'schema={"invoice_number":"string","invoice_date":"date",
      "vendor_name":"string","currency":"string","net_total":"number",
      "tax_total":"number","gross_total":"number",
      "line_items":[{"description":"string","quantity":"number",
      "unit_price":"number","line_total":"number"}]}'

Response

{
  "extraction_id": "b7e2a9c4-31d8-4f6e-9a02-5c7d1e8f4a21",
  "request_id": "req_x7y8z9a0b1c2d3e4",
  "status": "complete",
  "document": {
    "id": "3f8c1d2e-9a4b-4c5d-8e6f-7a0b1c2d3e4f",
    "filename": "vendor-invoice.pdf",
    "pages": 1,
    "size_bytes": 182044,
    "type_detected": "Invoice",
    "language_detected": "de"
  },
  "data": {
    "invoice_number": "RE-2026-4417",
    "invoice_date": "2026-08-14",
    "vendor_name": "Berger Logistik GmbH",
    "currency": "EUR",
    "net_total": 2840.00,
    "tax_total": 539.60,
    "gross_total": 3379.60,
    "line_items": [
      { "description": "Pallet transport Berlin-Hamburg",
        "quantity": 4, "unit_price": 610.00, "line_total": 2440.00 },
      { "description": "Fuel surcharge",
        "quantity": 1, "unit_price": 400.00, "line_total": 400.00 }
    ]
  },
  "confidence": {
    "overall": 0.97,
    "fields": { "gross_total": 0.99, "line_items": 0.96 }
  }
}

The endpoint reference, SDKs, and async patterns live in the data extraction API overview and the API docs.

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.

Choosing invoice data extraction software

If you are evaluating invoice data extraction software, most demos look alike: a clean invoice goes in, fields come out. The differences that decide whether the rollout survives contact with real vendor mail are these:

Schema-based, not template-based

A template per vendor is a maintenance contract you sign with yourself. Extraction against a schema handles a supplier you have never seen and survives a redesign from one you have.

Line items, not just headers

Plenty of tools capture the gross total and stop. If the output cannot represent a 40-row table with credits and surcharges, PO matching and cost-center allocation stay manual.

Validation in the product, not in your code

Totals reconciliation, tax arithmetic, and duplicate detection belong at extraction time. If you have to build those checks yourself, you bought a text reader, not an extraction product.

Confidence you can act on

A single accuracy claim says nothing about the invoice in front of you. Per-field confidence with provenance lets you auto-accept the safe values and review only the rest.

Delivery to the systems you already run

Webhook, S3, SFTP, and API delivery into NetSuite, SAP, or Dynamics should be configuration, not an integration project.

Residency and pricing that scale

EU-resident processing where regulation requires it, and usage-based pricing that does not punish seasonal volume spikes.

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.

What is the difference between invoice data extraction and invoice parsing?+

Parsing is the mechanical step: reading a document and turning its content into structured fields. Invoice data extraction, as AP teams use the term, is the workflow around that step: capture from every channel, validation, PO matching, and delivery into the ERP. If you are evaluating the mechanics, start with the invoice parsing page; if you are automating accounts payable, this page is the right lens.

Can I extract invoice data through an API?+

Yes. The invoice data extraction API is a single POST endpoint: send the document and your schema, get back typed JSON with line items and per-field confidence. Async batch processing with webhook callbacks handles high volumes, and a Node SDK and MCP server wrap the same endpoint for application and agent builders.

What should invoice data extraction software include?+

Four things separate production-grade software from demos: schema-based extraction that needs no per-vendor templates, line-item capture that survives multi-page tables, validation with per-field confidence at extraction time, and delivery into your ERP over webhook, S3, or SFTP. Data residency matters too: if your invoices must stay in the EU, verify where processing actually runs.

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.

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