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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM46384
Volume:
9 |
4 |
Apr 2025
Pages:
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.
This project presents Tender recommendation System, an AI-driven web application designed to streamline the process of discovering relevant tender opportunities through natural language search and adaptive feedback mechanisms. Built using a Flask-based backend, the system integrates OpenAI’s language models for semantic query understanding and employs Elasticsearch for dense vector similarity matching. It supports advanced filtering by country and publication date while incorporating user feedback to iteratively refine the relevance of search results. The application ensures secure access through token-based authentication and features a responsive user interface for an enhanced user experience. By combining natural language processing, vector search, and feedback-driven learning, Tender On Time significantly improves the accuracy and efficiency of tender retrieval, offering a robust solution for both public and private sector procurement needs.
KEYWORDS
Publication Date
10/5/2025
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
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