How to Automate Invoice Processing with AI (Step-by-Step)

Invoices typically arrive through two channels: email and manual upload. Your automation needs to handle both.

Email Capture

Set up an n8n email trigger that monitors a dedicated inbox (e.g., invoices@yourcompany.nl). The trigger fires whenever a new email arrives with a PDF attachment.

Configuration tips:

• Use a dedicated email address, not your general inbox
• Filter for emails with PDF attachments to avoid processing irrelevant messages
• Store the sender's email address as metadata for later matching

Manual Upload

For invoices that arrive by post or need to be uploaded manually, create a simple web form or use a shared folder that n8n monitors. A webhook endpoint works well here: your team scans the invoice, saves it to a folder, and the automation picks it up.

Practical note: You do not need both channels on day one. Start with email capture, which handles the majority of invoices, and add the upload path later.

Before AI can analyze an invoice, you need to extract the text content from the PDF. There are two approaches.

For Digital PDFs (Text-Based)

If the invoice was created digitally (which is increasingly common), the text is already embedded in the PDF. Use n8n's PDF parsing capabilities to extract it directly. This is fast, free, and highly accurate.

For Scanned PDFs (Image-Based)

If the invoice was scanned from paper, you need OCR (Optical Character Recognition) to convert the image to text. Options include:

• Google Cloud Vision API: Excellent OCR accuracy, handles tables well, pay-per-page pricing
• Azure Computer Vision: Similar quality, good if you are already in the Microsoft ecosystem
• Tesseract (open source): Free, can run locally, but lower accuracy on complex layouts

Our recommendation: For most businesses, Google Cloud Vision or Azure provides the best balance of accuracy and cost. At typical MKB volumes (under 1,000 invoices/month), the cost is negligible, usually under EUR 10/month.

Implementation note: In practice, you will receive a mix of digital and scanned PDFs. Build your workflow to try text extraction first, and fall back to OCR only when the text extraction returns insufficient content. This saves on OCR costs and processing time.

This is where the AI comes in. You have raw text from the invoice. Now you need to extract structured data fields.

What to Extract

For each invoice, you want to capture:

The AI Prompt

The key to reliable extraction is a well-structured prompt. Here is a prompt pattern that works consistently:

You are an invoice data extraction system. Extract the following fields from
this invoice text. Return your response as a valid JSON object.

Required fields:
- vendor_name (string)
- invoice_number (string)
- invoice_date (string, format: YYYY-MM-DD)
- due_date (string, format: YYYY-MM-DD, calculate from payment terms if not explicit)
- subtotal (number)
- vat_amount (number)
- total_amount (number)
- currency (string, ISO 4217 code)

Optional fields (include if found):
- iban (string)
- po_number (string)
- payment_terms (string)
- line_items (array of objects with: description, quantity, unit_price, total)

If a field cannot be determined, set it to null.
Do not hallucinate values. Only extract what is explicitly stated in the invoice.

Invoice text:
{extracted_text}

Which AI Model to Use

For invoice extraction, you do not need the most powerful (or expensive) model.

For most businesses, GPT-4o-mini or Claude Haiku provides sufficient accuracy at a fraction of the cost. At 300 invoices per month with GPT-4o-mini, your AI costs are roughly EUR 0.30-1.50 per month. Yes, that is cents, not euros.

Tip: Start with a cheaper model and only upgrade if accuracy is insufficient for your invoices. Some vendors use complex layouts or unusual formats that benefit from a more capable model.

Never trust AI output blindly. Add validation checks to catch errors before they propagate.

Basic Validation Rules

• Total check: Verify that subtotal + VAT = total amount (within a small rounding tolerance)
• Date sanity: Invoice date should not be in the future or more than 90 days in the past
• Required fields: Ensure all required fields are present and non-null
• Format checks: IBAN follows the correct pattern, invoice number is not empty
• Duplicate check: Look up the invoice number in your system to prevent double-processing

Confidence Scoring

Build a simple scoring system that assigns confidence to each extraction:

• All validations pass + all required fields present = High confidence (auto-process)
• Minor issues (e.g., missing optional fields) = Medium confidence (auto-process with flag)
• Validation failures = Low confidence (route to human review)

In practice, 80-90% of invoices will be high confidence and can flow through without human intervention.

If your business uses purchase orders, matching invoices to POs is a critical step.

The Matching Logic

1. Direct match: If the invoice contains a PO number, look it up in your system. If found and amounts match (within tolerance), approve.

2. Vendor match: If no PO number, find open POs from the same vendor. If there is exactly one with a similar amount, suggest it as a match.

3. Fuzzy match: For cases where neither direct nor vendor match works, use AI to compare invoice line items against PO line items and suggest the best match.

4. No match: Route to the appropriate person for manual PO assignment or approval.

Tolerance Settings

Set reasonable matching tolerances:

• Amount tolerance: 2-5% (accounts for minor price adjustments, shipping costs)
• Date tolerance: 30-60 days from PO date
• Line item matching: 80%+ overlap on descriptions

These tolerances should be configurable because different businesses have different needs.

Once an invoice is validated and matched, post it to your accounting system.

Common Integrations for Dutch Businesses

What to post:

• Vendor (create if new, match if existing)
• Invoice header (number, date, due date, amounts)
• Line items with correct GL coding
• Attachments (the original PDF)

GL coding: This is where things get interesting. You can use AI to suggest the correct general ledger codes based on the invoice description and historical patterns. For example, if invoices from "Bakker Office Supplies" have historically been coded to "Office Expenses," the system can apply that coding automatically.

No automation handles 100% of cases. Design your exception handling carefully.

Exception Types

Notification Design

For each exception, send a clear notification to the right person with:

• The original invoice PDF
• What the AI extracted
• What specifically went wrong
• A direct link to resolve the issue

Good notifications reduce the time to resolve exceptions from minutes to seconds.