Client Overview
Our client, an innovative company, sought to automate document processing through a user-friendly SaaS platform. They required accurate extraction of tabular and key-value data from both structured and unstructured documents. The goal was to reduce manual data entry, improve extraction accuracy, and streamline document digitization.
Challenges
- Develop a user-friendly SaaS platform for key data extraction.
- Accurately extract information from scanned documents of various formats.
- Address extraction issues across diverse document compositions.
Benefits
- Achieved ~91% extraction accuracy.
- Reduced turnaround time (TAT) by ~86%.
- Lowered manpower costs by ~73%.
Objectives
- Provide a seamless user experience for data extraction processes.
- Automate document digitization, reducing reliance on manual extraction by ~86%.
- Implement a QA correction logging process to refine extraction accuracy using reinforcement algorithms.
- Increase extraction accuracy to ~91% and reduce manual digitization costs by ~73%.
Tech Strategy
- Machine Learning Models: For tabular and key-value data extraction.
- Cloud Infrastructure: Leveraged for scalability and cost efficiency.
- Microservices Architecture: Facilitated modular development and easy integration.
- Reinforcement Learning: Applied to continuously improve data extraction accuracy using QA logs.
- API Integration: Enabled easy connectivity with other enterprise systems for data processing.
Conclusion:
The development and implementation of the intelligent document processing SaaS platform successfully met the client’s objectives. By automating data extraction and utilizing reinforcement learning, the platform significantly improved accuracy and efficiency. The client now benefits from faster document processing, reduced manual effort, and substantial cost savings, positioning them for greater scalability and success in their operations.
