AI-Driven Invoice Automation for a Global Vehicle Glass Repair Leader

Executive Summary

A global leader in vehicle glass repair, serving over 10 million customers annually, faced operational challenges in processing invoices manually, leading to delays, high labor costs, and data errors. ITTStar implemented an AI-powered invoice processing solution on Google Cloud Platform (GCP), leveraging Document AI, Generative AI, and Vision API to automate classification and data extraction. The solution reduced processing time by 80%, achieving 95% accuracy in data extraction and scaling to process 150,000 invoices per batch, with potential for 1 million per month. It also ensured full US-region compliance and optimized cost-effectiveness. The implementation streamlined operations, reduced costs, and improved data accuracy, positioning the client for future growth.

Customer Challenges

Despite its global scale, the company faced significant operational challenges with its invoicing process:

• Diverse Invoice Formats: Invoices arrived in various formats (e.g., PDFs, images, Word documents), complicating processing.
• Manual Processing Bottlenecks: Manual invoice handling led to operational delays and bottlenecks, slowing down the process.
• High Labor Costs: Extensive manual review of invoices contributed to escalating labor costs.
• Data Entry Errors: Manual data entry increased the risk of inaccuracies, affecting business data integrity.

The company sought a solution that could automate invoice processing, improve accuracy, and reduce manual labor costs.

ITTStar’s AI-Powered Solution on Google Cloud Platform (GCP)

ITTStar implemented an AI-powered invoice processing solution using Google Cloud Platform (GCP), leveraging Document AI, Generative AI, and Google Cloud Vision API to automate the invoicing process. This solution was designed to handle both:

1. 1. Invoice Classification: Automating the categorization of invoices across diverse formats.
   
2. 2. Data Extraction: Extracting critical data fields from invoices, including header and line-item information.
   

The solution significantly reduced manual work, improved processing speed, and ensured high accuracy in data extraction.

Implementation Approach:

1. 1. Cloud Setup -Created a dedicated Google Cloud Project with secure access and US-region data compliance.
   
2. 2. Invoice Review - Analysed 4,000 invoices, labelling 1,500 manually and 2,500 using GCP Document AI.
   
3. 3. Sorting - Built a GCP-based model to classify invoices into three types with 98% accuracy.
   
4. 4. Data Extraction - Used Document AI to extract 22 key fields (10 header, 10 line-item, 2 others) from varied layouts.
   
5. 5. Processing - Stored invoices in Google Cloud Storage, converted PDFs to images, and removed duplicates.
   
6. 6. Templates - Applied structured templates for clean formats and flexible logic for mixed formats within the GCP pipeline.
   
7. 7. Output - Loaded processed data into Big Query and delivered a detailed performance report.
   

Why GCP?

GCP was chosen for its industry-leading AI/ML capabilities, particularly in document and image processing:

• Pre-Trained Models: GCP’s Document AI and Vision API offered strong out-of-the-box OCR and document layout capabilities, reducing the need for customization.
• AI/ML Tools: With tools like TensorFlow and advanced machine learning research, GCP provided the cutting-edge capabilities necessary for large-scale document processing.
• High Accuracy: GCP’s Vision API and Document AI are renowned for their accuracy in processing multilingual and complex document formats.

Key Outcomes & Impact

• Faster Processing: Cut invoice time by 80%, from ~15 minutes to under 1 minute.
• Higher Accuracy: Reached 95% precision and recall, reducing errors and rework.
• Scalable : Processes 150K+ invoices per batch, with capacity for 1M+ monthly.
• Compliant : Fully meets US-region data storage and privacy regulations.
• Cost-Efficient : Optimized GCP usage for high performance at controlled costs.

This blend of speed, accuracy, scalability, and compliance transformed invoice processing, cutting costs, boosting capacity, and ensuring data integrity at scale, while laying a strong foundation for future growth.

Conclusion

The AI-powered invoicing solution on Google Cloud Platform automated invoice classification and data extraction, eliminating most manual effort. It significantly improved processing speed and accuracy, ensuring faster turnaround times. Built for scalability, the system easily adapts to growing business volumes. The design also ensures full compliance with regional data regulations. Cost optimization within GCP kept the solution efficient and budget-friendly. Overall, it freed the client to focus on core operations while laying a solid foundation for future growth.