How AI by Zapier Cracked the Email Invoice Problem Without OCR
The Client Situation
A national cannabis company operating under multiple business entities came to us with an unusual invoicing challenge. While most vendors sent PDF invoices, a core group of their vendors were sending Quickbooks invoices directly in email bodies - no attachments, just raw text embedded in emails.
"When it comes in from their Quickbooks account... the email is from notification@quickbooks.com instead of from the vendor's actual email," our automation specialist explained. These emails needed to be turned into bills in their own Quickbooks system, but traditional automation methods were failing.
The Hidden Complexity
What seemed like a simple email-to-Quickbooks sync revealed deeper challenges:
- Vendors appeared under different names across systems (e.g., "Connex Inc" vs "Connex", or using doing-business-as names)
- Each vendor's Quickbooks email format was slightly different
- Duplicate invoices were common as vendors resent bills
- The company needed to maintain proper audit trails across divisions
"We need to verify the name, pull the invoice number, get the total amount... but every format is different," noted their accountant. Manual processing was error-prone and time-consuming.
The Technical Challenge
Most invoice automation solutions handle PDF attachments well, but struggle with emails where invoice details are embedded in the body text. This national company faced exactly that problem - vendors sending Quickbooks invoices directly in email bodies, with no standard format.
Traditional approaches would require:
- Complex regex patterns
- Custom rules per vendor
- Constant maintenance as formats change
- Manual handling of exceptions
The AI Solution
Using AI by Zapier's Analyze and Return Data action, we built a system that intelligently extracts invoice data from unstructured email content. The key innovation was using multiple extraction attempts with different contexts:
Prompt sample (simplified): Extract the following from the email: - Invoice number - Total amount - Vendor name - Due date (if present) Check these locations in order: 1. Subject line 2. Email body 3. From name field Return values in specified format...
Three-Layer Validation
The system runs three parallel extractions:
- Subject line parse: Looks for clear invoice markers
- Body content analysis: Deep scans for invoice details
- Sender analysis: Cross-references sender info
Results are then merged with precedence rules to ensure accuracy.
Vendor Name Matching Innovation
A particularly clever use of AI by Zapier was handling vendor name variations. The prompt instructs the AI to:
- Remove common suffixes (LLC, Inc, etc.)
- Handle missing/extra punctuation
- Match abbreviated forms
Results in Production
After implementation:
- Successfully processed 7 invoices in the first day
- 100% accuracy on data extraction
- Zero maintenance needed for new formats
- System learns from each new vendor style
Why This Matters
Traditional OCR and rule-based systems struggle with email body invoices because there's no consistent visual layout to parse. AI by Zapier's natural language understanding makes it possible to reliably extract structured data from unstructured text.
Technical Lessons Learned
- Multiple extraction attempts provide better reliability than single passes
- Prompt engineering is critical - careful instruction of the AI model improves accuracy
- Validation rules should still safeguard AI output
- Feedback loops help improve accuracy over time
Would you like me to expand on any of these technical aspects?
