Turn Invoices and PDFs Into Clean Data Automatically
Every invoice, delivery note and signed form that lands in your inbox becomes someone's job to read and retype into a spreadsheet or system. It is slow, nobody enjoys it, and it quietly eats hours that should go to actual work.
Manual entry also introduces errors that surface at the worst moments, a transposed total, a wrong date, a supplier name spelled three different ways. By the time anyone notices, the bad number has already flowed into a report or a payment.
How it works
Define fieldsWe agree on exactly which values matter, such as totals, dates, line items, reference numbers, and where each should end up.
Build extractionWe set up the reader on your real documents so it reliably pulls those fields from the layouts your suppliers actually use.
Route dataWe send the extracted values straight into your accounting tool, database or sheet, formatted the way each system expects.
Handle exceptionsWe add a review step so anything the reader is unsure about is flagged for a human instead of guessed.
What it looks likeWhat it producesExample scenario
Invoice_2043.pdf
SupplierBerg & Co. GmbH
Invoice no.2043
Date04 Jul 2026
Total€1,240.00
Deviation → review
Illustrative example. Not real customer data.
Outcome
Teams that automate document entry typically reclaim the hours previously lost to retyping and see fewer of the quiet errors that manual keying tends to create.
Common questions
What file types and formats can it read?
It handles PDFs, scans and photos of invoices, forms and delivery notes, including documents where the layout differs from one supplier to the next.
How accurate is the extraction?
On the recurring document types we tune it against it is highly reliable, and we measure accuracy on your real documents before it goes live rather than promising a generic figure.
What happens when it is unsure about a value?
Instead of guessing, it flags the document for a quick human check, so a doubtful field never silently lands in your system as fact.