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OCRA
Published:
October 12, 2019
Category:
Design / Ideas
Client:
Oceanthemes
How it Works
Manual invoice processing creates significant bottlenecks in accounts payable departments, requiring staff to spend hours on data entry, validation, and verification across multiple systems. The challenge lies in accurately extracting information from diverse invoice formats while maintaining consistency and integrating with existing financial systems. Traditional OCR solutions often fail to handle variations in layout and require extensive manual correction.
OCRA revolutionizes this process through intelligent automation. The system begins by automatically monitoring designated email inboxes for incoming invoice attachments. Using advanced machine learning models, it validates whether documents are legitimate invoices or credit memos, filtering out irrelevant files. For valid invoices, the AI engine extracts key data points including vendor information, invoice dates, line items, and totals with remarkable accuracy.
What sets OCRA apart is its self-learning capability. The system employs reinforcement learning algorithms that continuously improve from human corrections. When accounting staff review and adjust extracted data, the system learns from these adjustments, becoming increasingly accurate for each specific vendor over time. This creates a virtuous cycle where the system adapts to your unique vendor ecosystem, reducing manual intervention with each processing cycle.
The platform seamlessly integrates with your existing ERP and document management systems, automatically transferring validated data and archiving processed documents. This creates a completely automated workflow from email reception through to final posting, with human involvement only required for exception handling and quality assurance.
Process & Results
The development of OCRA began with detailed analysis of accounts payable workflows across multiple organizations. We documented each step of the invoice processing journey and conducted extensive interviews with accounting teams to identify key pain points. Our technical team collected thousands of sample invoices to train the machine learning models that would power the intelligent extraction engine.
We built the platform using a modular approach, starting with document classification before moving to data extraction and reinforcement learning capabilities. The system was designed to handle various invoice formats while integrating seamlessly with existing financial systems. Throughout development, we worked closely with beta clients who provided real-world testing environments and valuable feedback that helped refine the platform’s functionality.
Implementation involved configuring vendor databases, approval workflows, and ERP integrations for each client. We conducted phased rollouts with comprehensive training to ensure smooth adoption. The system’s self-learning capability proved particularly effective, continuously improving accuracy as it processed more invoices from specific vendors.
The results demonstrated significant operational improvements. Clients reduced invoice processing time by 85% and decreased data entry errors by over 90%. One manufacturing company cut their average processing time from 15 minutes to 2 minutes per invoice, while a healthcare provider automated 500+ monthly invoices and shortened approval cycles from 12 days to 3 days. Across all implementations, organizations redirected hundreds of hours from administrative tasks to strategic financial activities, achieving substantial efficiency gains while maintaining exceptional accuracy through the platform’s adaptive learning capabilities.
