Introduction
A major banking institution embarked on a large-scale digital transformation to modernize its legacy document archives. With over 5 million documents spanning 30 years—including both OCR-readable and non-OCR formats—the bank sought to streamline operations, enhance data accessibility, and ensure compliance through a next-gen Document Management System (DMS). By leveraging Generative AI, layered Local LLMs, and Autogen techniques, the initiative aimed to unlock metadata insights, automate document handling, and future-proof document workflows.
Key Challenges
Efficiently digitizing non-OCR documents with complex formatting and poor scan quality.
Implementing AI-driven inferencing to surface contextual insights and support advanced search queries.
Extracting accurate, rich metadata from diverse content types for effective DMS categorization.
Ensuring regulatory compliance and secure access control for sensitive documents.
Solution Provided
Developed a digitization pipeline accommodating both OCR and non-OCR documents, ensuring end-to-end coverage.
Used Generative AI to extract, structure, and index metadata to support intelligent classification.
Deployed Autogen techniques with three-layer Local LLMs for deep inferencing and contextual understanding.
Automated the complete workflow—from scan to archive including storage, security tagging, and search enablement.
Business Impact
80% reduction in document retrieval time, dramatically improving information accessibility.
Achieved 100% document security and compliance via role-based access control in the DMS.
70% improvement in metadata accuracy and richness, supporting smarter categorization and analysis.
Technology Highlights
- Implemented a three-layer Local LLM architecture for robust inferencing and intelligent understanding of digitized content.
- Automated end-to-end document categorization and storage, fully integrated with the modern DMS.
- Employed Generative AI to extract deep metadata like titles, dates, authorship, summaries, and content themes.
- Delivered a user-friendly interface within the DMS for seamless search, access, and content discovery.
Next Steps Planned
- Expand digitization to include ongoing and future documents, ensuring consistent.
- Further enhance inferencing capabilities to support complex multi-document queries and cross-referencing.
- Integrate advanced AI-driven tools for topic modeling, historical analysis, and trend.
- Identification to derive deeper insights from the archive.
