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International Journal of Science, Strategic Management and Technology

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MEETINSIGHT: AI-POWERED MEETING INTELLIGENCE

AUTHORS:
Raj Jaiswal
Mentor
Dr. Ritesh Rastogi
Affiliation
B.Tech (Information Technology) NIET, Greater Noida
DOI: 10.55041/ijsmt.v2i5.053
CC BY 4.0 License:
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Abstract

MeetInsight is a smart system designed to make meetings more useful and easier to understand. It automatically records meetings, converts speech into text, and allows users to ask questions about what was discussed. Instead of taking manual notes, users can simply interact with the system to get answers.


The system uses modern AI technologies like speech recognition, embeddings, and language models to process meeting data. It follows a Retrieval-Augmented Generation (RAG) approach, which ensures that answers are based on actual meeting content rather than guesses. Overall, MeetInsight helps save time, improves productivity, and makes information from meetings easily accessible.

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PUBLICATION DETAILS
Review Type:
Double-Blind Peer Review
License:
CC BY-NC 4.0
Volume/Issue:
Volume 02, Issue 05
Review Rounds:
2
Article Type:
Research Article
Publication Date:
May 05 2026
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Jaiswal, R. (2026). Meetinsight: AI-Powered Meeting Intelligence. International Journal of Science, Strategic Management and Technology, 02(05). https://doi.org/10.55041/ijsmt.v2i5.053

Jaiswal, Raj. "Meetinsight: AI-Powered Meeting Intelligence." International Journal of Science, Strategic Management and Technology, vol. 02, no. 05, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i5.053.

Jaiswal, Raj. "Meetinsight: AI-Powered Meeting Intelligence." International Journal of Science, Strategic Management and Technology 02, no. 05 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i5.053.

References
1.Radford et al., “Robust Speech Recognition via Large-Scale Weak Supervision,” OpenAI Whisper, 2022.

2.Reimers and I. Gurevych, “Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks,” Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019.

3.Lewis et al., “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks,” Advances in Neural Information Processing Systems (NeurIPS), 2020.

4.Devlin et al., “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” NAACL-HLT, 2019.

5.Kuchaiev et al., “NeMo: a toolkit for building AI applications using neural modules,” NVIDIA, 2019.

6.A. Paszke et al., “PyTorch: An Imperative Style, High-Performance Deep Learning Library,”
Ethics and Compliance
✓ All ethical standards met
This article has undergone plagiarism screening and double-blind peer review. Editorial policies have been followed. Authors retain copyright under CC BY-NC 4.0 license. The research complies with ethical standards and institutional guidelines.
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