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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM61990
Volume:
10 |
4 |
April 2026
Pages:
1-5
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Computer Engineering
Matoshri College of Engineering and Research Centre, Nashik, Maharashtra, India.
Computer Engineering
Matoshri College of Engineering and Research Centre, Nashik, Maharashtra, India.
Computer Engineering
Matoshri College of Engineering and Research Centre, Nashik, Maharashtra, India.
Computer Engineering
Matoshri College of Engineering and Research Centre, Nashik, Maharashtra, India.
Computer Engineering
Matoshri College of Engineering and Research Centre, Nashik, Maharashtra, India.
Abstract - This paper represents FORNOVA, an AI-powered document automation platform developed to efficiently process, organize, and analyze large volumes of unstructured data across multiple domains such as healthcare, finance, education, legal, industrial, and human resources. The system supports document uploads in formats like PDFs and images and extracts structured information using a hybrid approach combining rule-based parsing and AI techniques. Key data fields are presented with confidence scores to ensure reliability and transparency. It also provides validation checks and risk indicators for better analysis. A domain-specific dashboard displays performance metrics, while role-based access control enables document review and approval. The platform enhances efficiency, reduces manual effort, and supports data-driven decision-making for small and medium enterprises effectively.
KEYWORDS
Fornova is a comprehensive document automation system that integrates user management, document processing, analytics, and export functionalities into a single platform. The system successfully automates the extraction and analysis of data from various document types, reducing manual workload and improving efficiency. The modular architecture ensures flexibility and scalability, allowing easy integration of advanced AI models in the future. The system provides a user-friendly interface along with powerful backend processing, making it suitable for both technical and non-technical users.
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Publication Date
4/30/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
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