The problem
In many back offices a large part of the day goes into opening documents, reading them and typing data into a system: supplier invoices, receipts, delivery notes, purchase orders, customer forms, contracts. It is slow work, prone to typing errors and it grows with the business. Document automation turns it into a flow where people review exceptions instead of keying in everything.
What intelligent document processing does
- Receives documents from email, a shared folder, WhatsApp or a form.
- Reads PDFs, images and scans, including different layouts for the same type of data.
- Extracts the required fields: supplier, date, amounts, taxes, items, parties, deadlines.
- Validates against company systems: the supplier exists, the purchase order matches, the totals add up.
- Enters the data into the ERP, accounting system or CRM.
- Routes to human review whatever does not match or has low confidence, highlighting which field raised the doubt.
Why it now works better than classic OCR
Traditional text recognition turns an image into text but does not understand what each number means. Current AI models interpret the whole document: they tell the total from the subtotal even when the layout changes, understand tables and can read documents from suppliers they have never seen. They still make mistakes at times, which is why validation against company data and review of uncertain cases are part of the design, not an add-on.
Common documents by department
- Finance and accounting: invoices, credit notes, receipts, statements and tax withholdings; invoice automation continues the flow after extraction.
- Purchasing and logistics: purchase orders, delivery notes, waybills and certificates.
- Legal: contracts, powers of attorney, notices and filings, with extraction of parties, deadlines and clauses; see AI for law firms.
- Sales: customer onboarding forms, applications and supporting documents.
Invoice automation from end to end
A typical case: the invoice arrives by email, the system reads it, identifies the supplier, matches amounts and quantities against the purchase order and delivery note, records it in the accounting system and leaves it ready for payment approval. If something does not match, such as a price different from the agreed one or a quantity not received, it is flagged for review with the exact difference. Payment is still approved by a person.
Information privacy
Documents often contain personal, tax or confidential data. We define what is sent to external models and what is processed on your own infrastructure, and sensitive fields can be anonymized before processing with Varexis. Original documents stay stored where the company decides.
How it is implemented
- Gather a real sample of documents, including the difficult ones.
- Define which fields are extracted and which validations apply.
- Measure accuracy on that sample before connecting to live systems.
- Go live reviewing every case at first, and reduce review as accuracy is confirmed.
What is measured
Accuracy per field, share of documents processed without intervention, time from receipt to entry, errors caught by validation and manual data entry hours removed.