Retrievix: An AI-Based Platform for Finding and Returning Lost Items

Retrievix is an AI-based lost and found platform designed to improve the process of identifying and recovering misplaced items. The system uses the CLIP model for multi-modal semantic matching between images and textual descriptions, enabling more accurate retrieval compared to traditional keyword-based methods. The platform integrates secure authentication, smart match suggestions, vector similarity search using FAISS, and real-time communication features to provide an efficient and user-friendly recovery system for institutions and public environments.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-05-17
DOI
https://doi.org/10.5281/zenodo.20258526
Primary Topic
Digital and Traditional Archives Management
Type
article
Field-Weighted Citation Impact
0.00
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article

Retrievix: An AI-Based Platform for Finding and Returning Lost Items

Swati Ambi, Soham Bagade, Satyajit Ghatage, Shridhar Badger et al.
Zenodo (CERN European Organization for Nuclear Research)
Digital and Traditional Archives Management
article

Retrievix: An AI-Based Platform for Finding and Returning Lost Items

Swati Ambi, Soham Bagade, Satyajit Ghatage, Shridhar Badger, ketan Patil, Swayam Dakare
article en

Abstract

Retrievix is an AI-based lost and found platform designed to improve the process of identifying and recovering misplaced items. The system uses the CLIP model for multi-modal semantic matching between images and textual descriptions, enabling more accurate retrieval compared to traditional keyword-based methods. The platform integrates secure authentication, smart match suggestions, vector similarity search using FAISS, and real-time communication features to provide an efficient and user-friendly recovery system for institutions and public environments.

Zenodo (CERN European Organization for Nuclear Research)
D.Y. Patil Education Society (IN), D.Y. Patil University (IN)
Reduced inequalities
Openalex Percentile: Top 4%
Digital and Traditional Archives Management
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