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FORNOVA Documents with AI

Shrihari Suresh Kasar Suresh Kasar

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Paper Contents

Abstract

An inventive use of artificial intelligence to improve, automate, and streamline document management procedures across industries is represented by FORNOVA-Documents with AI. FORNOVA intelligently classifies, extracts, summarizes, and validates data from a variety of document types, including invoices, contracts, reports, and more, by utilizing machine learning, natural language processing (NLP), and computer vision. The system significantly lowers manual labor, minimizes human error, and speeds up decision-making by incorporating AI technologies. It facilitates smooth interoperability across platforms and departments by supporting multilingual and multiformat document handling. Through constant learning from user interactions, the system guarantees flexibility and gradually increases accuracy. FORNOVA offers a scalable, secure, and intelligent solution that turns conventional document workflows into dynamic, AI-driven processes as businesses deal with increasing amounts of unstructured data. This innovation positions itself as a pillar for the digital transformation of document management by improving compliance, data accuracy, and strategic insights in addition to operational efficiency. Additionally, by providing modular AI components that can be tailored to meet organizational needs, FORNOVA advances the expanding field of Intelligent Document Processing (IDP). The system ensures accessibility and real-time collaboration by integrating with cloud storage and APIs. All things considered, FORNOVA-Documents with AI offers a comprehensive digital framework that substitutes a self-learning, automated, and effective ecosystem for conventional, manual document handling, providing a contemporary method of information management that is intelligent, transparent, and accurate.

Copyright

Copyright © 2025 Shrihari Suresh Kasar. This is an open access article distributed under the Creative Commons Attribution License.

Paper Details
Paper ID: IJPREMS51000020961
ISSN: 2321-9653
Publisher: ijprems
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