Invoice processing automation that reads, checks, and posts for you

What the manual process actually costs

Manual invoice entry looks cheap per invoice and expensive in aggregate. A clerk opens the email, reads the PDF, finds the numbers, checks them against the order, keys them into the ledger, and files the document. Multiply that by hundreds of invoices a month and it is a real headcount cost, concentrated painfully around month-end close. Worse, the errors are invisible until they bite: a transposed figure, a duplicated invoice paid twice, a wrong tax code that surfaces at audit.

The invoices themselves are the hard part, because no two vendors format them the same way. The total sits in a different place on every layout, line items wrap unpredictably, and some arrive as clean digital PDFs while others are photos of a printout. This is exactly why fixed-template OCR tools disappoint: they work until a vendor changes their layout, then quietly break. A language model reads for meaning instead of position, so it finds the total whether it is labelled 'Amount Due', 'Total', or 'Rechnungsbetrag', on a layout it has never seen.

• Per-invoice entry time that compounds into real headcount cost
• Painful concentration of work around month-end close
• Duplicate payments and transposed figures that surface late
• Fixed-template OCR that breaks the moment a vendor changes layout

How our invoice pipeline works

Invoices land in one place: a dedicated inbox, a shared drive, or an upload folder. From there the pipeline reads each one, using OCR for scans and photos and native parsing for digital PDFs, then an LLM extracts the fields you post on: vendor, invoice number, date, currency, line items, tax, and total. Because the model reads meaning rather than fixed positions, it handles new vendors and changed layouts without a template rebuild.

Then comes the part that makes it safe to trust: validation. We check the maths, that line items sum to the subtotal and tax to the total. We match the invoice against the purchase order and goods receipt where you run them, so a three-way match either passes cleanly or flags a discrepancy. We check for duplicates against invoices already posted. Only invoices that pass every rule flow straight through to your accounting system; anything that fails, or that the model is not confident about, lands in a review queue for a person to resolve in seconds.

• Single intake: inbox, shared drive, or upload folder
• Extraction of vendor, number, date, line items, tax, and total
• Two- or three-way matching against POs and goods receipts
• Duplicate detection against invoices already in your ledger
• Clean posting into Xero, QuickBooks, DATEV, or your ERP via its API

Validation and the human in the loop

The point of an invoice pipeline is not speed for its own sake, it is speed you can trust with your money. So we never post an invoice that has not passed validation. A total that does not reconcile, a vendor not on file, an amount above a threshold you set, a low extraction confidence: any of these routes the invoice to a person instead of pushing it through. The human sees the original document and the extracted fields side by side and approves or corrects in seconds, and their correction is logged.

That review queue also shrinks over time. As we see which vendors and document types the pipeline handles reliably, you can raise the auto-post thresholds for them while keeping tight review on the rest. You are always in control of that dial. The result is a process where the bulk of clean, matched invoices post themselves and your AP person spends their time only on the genuine exceptions, which is the work that actually needs a human.

It runs in your systems, and you own it

The whole pipeline runs on your accounts and your infrastructure and posts into the accounting system you already use, through its API. We do not insert ourselves as a middleman you have to route invoices through forever. When we hand over, the workflow, the extraction prompts, the validation rules, and the documentation are yours, and your finance team or a future partner can read and change them. Because this touches your books, we build monitoring and an audit trail in from the start: every invoice, every extraction, every human correction is logged, so you can always answer exactly what was posted and why.