The cost of manual data entry
Invoice processing is one of the last places where skilled people still retype numbers from one screen into another. It is slow, it is expensive per document, and the error rate quietly costs more than the labour when a wrong amount reaches a payment run.
Traditional OCR promised to fix this and mostly did not, because it reads characters without understanding the document. A modern AI pipeline understands that a number near the words total due is the total, even on a layout it has never seen.
How the pipeline works
Documents arrive by email, upload or a watched folder. Each one is classified, the relevant fields are extracted, and the values are validated against your rules and your existing records before anything is written anywhere.
- Handles invoices, receipts, purchase orders, contracts and delivery notes
- Reads scans, photos and native PDFs, in multiple languages
- Validates totals, tax lines, vendor names and duplicate submissions
- Flags low confidence documents for a quick human review instead of guessing
- Writes clean records into QuickBooks, Xero, Zoho, a database or a Google Sheet
Who this is for
Finance teams, bookkeeping firms, logistics operators, construction companies and anyone processing more than a few hundred documents a month. Legal and HR teams use the same pipeline for contract and CV extraction.
Accuracy and control
We benchmark the pipeline against a sample of your real documents before go live and report field level accuracy, so you know exactly what to expect rather than trusting a vendor claim.
Everything low confidence goes to a review queue. Your team stops doing data entry and starts doing exception handling, which is a much smaller job.