Automating Invoice Processing with AI and Make.com

Introduction

Efficient invoice processing is a cornerstone of streamlined financial management for businesses. However, traditional methods often involve manual data entry, which is time-consuming, prone to errors, and costly. To explore the potential of automation, we developed a project that leverages AI and Make.com, a no-code automation platform, to build an automated invoice processing system. This case study outlines the process, implementation, and results of this project.

The Problem

Manual invoice processing poses several challenges:

• Inefficiency: Time is spent entering invoice data into spreadsheets manually.
• High Error Rates: Mis-entered invoice numbers or amounts lead to discrepancies in financial records.
• Scaling Issues: Increasing invoice volume leads to delays and inefficiencies.

This project aimed to design a solution that automates invoice data extraction and storage, reducing manual effort and errors while improving scalability.

The Solution

Using Make.com and OpenAI’s GPT, we developed an automation pipeline for processing invoice data. The solution works as follows:

1. Trigger: New invoices (images or PDFs) are uploaded to a Google Drive folder.
2. File Retrieval: Make.com retrieves the uploaded file and ensures it’s ready for processing.
3. OCR (Image to Text Conversion): Google Cloud Vision extracts raw text from the invoice if the file is an image or scanned PDF.
4. AI Parsing: OpenAI GPT processes the extracted text, identifying key invoice details such as Invoice Number, Date, Total Amount, and Vendor Name.
5. Data Structuring: The parsed fields are organized into a structured format using a Text Parser module.
6. Storage: The extracted data is automatically saved to a Google Sheet for record-keeping and further analysis.

Implementation Details

1. Trigger Module

• Platform: Google Drive
• Action: “Watch Files” detects when a new invoice is uploaded.

2. File Retrieval

• Platform: Google Drive
• Action: “Get a File” fetches the binary content of the uploaded invoice.

3. OCR Processing

• Platform: Google Cloud Vision
• Action: Extracts text from uploaded invoices. Dense text detection was enabled to improve accuracy for invoices.

4. AI Parsing

• Platform: OpenAI GPT
• Action: Analyzes the text and extracts key fields. A carefully crafted prompt ensures consistent output:

Extract the following fields from the invoice text:
- Invoice Number
- Date
- Total Amount
- Vendor Name

Text: [Invoice Text]

5. Data Structuring

• Platform: Make.com Text Parser
• Action: Uses regex to extract key-value pairs from GPT’s response:

- \\*\\*Invoice Number\\*\\*: (?<InvoiceNumber>\\d+)\\s*
- \\*\\*Date\\*\\*: (?<Date>[\\d/]+)\\s*
- \\*\\*Total Amount\\*\\*: \\$(?<TotalAmount>[\\d.]+)\\s*
- \\*\\*Vendor Name\\*\\*: (?<VendorName>.+)

6. Storage

• Platform: Google Sheets
• Action: Adds the extracted data as a new row with columns for Invoice Number, Date, Total Amount, and Vendor Name.

Results and Impact

The project demonstrated the following outcomes:

Efficiency Gains

• Reduced invoice processing time by 80%, freeing up time for other tasks.
• Processed an average of 50 invoices per hour, compared to 10 manually.

Error Reduction

• Eliminated manual data entry errors, achieving 100% data accuracy for extracted fields.

Scalability

• The system easily scaled to handle growing invoice volumes without additional effort.

Cost Savings

• Simulated savings of approximately $2,500/month in labor costs by reducing manual processing.

Conclusion

This invoice processing project demonstrates the potential of AI and no-code platforms like Make.com in addressing common operational challenges. By integrating Google Cloud Vision and OpenAI GPT, the system not only improved efficiency but also showcased a scalable approach to financial workflow automation.

This project serves as a practical example of how automation can simplify repetitive tasks and deliver measurable benefits, making it a valuable addition to any business’s operations.