Loading article...
Loading article...
Stream/Area:
-
Subject:
-
Paper ID:
IJSREM65451
Volume:
10 |
6 |
Jun 2026
Pages:
1-7
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
Zeal College of Engineering and Research
Computer Engineering
Zeal College of Engineering and Research
Computer Engineering
Zeal College of Engineering and Research
Computer Engineering
Zeal College of Engineering and Research
This paper presents Mentesa v1.0, an improved version of the Mentesa no-code AI chatbot creation platform. The platform allows non-technical users to create, manage, and deploy personalized AI chatbots using their own documents as a knowledge source, without requiring any programming skills. The system continues to use Google's Gemini-2.5-Pro language model with a Retrieval-Augmented Generation (RAG) approach to provide accurate and context-aware responses. Firebase is used for secure data management and user authentication, while Render provides cloud-based deployment support. Compared to the previous version, Mentesa v1.0 introduces several key improvements focused on security, accessibility, and scalability. These enhancements include Firebase-based authentication for secure user accounts, individual API key generation for controlled chatbot access, an API-authenticated embeddable widget that allows chatbots to be integrated into external websites, a billing and usage quota management system, and an improved document processing pipeline that supports DOCX and plain text files in addition to PDFs. The frontend has also been redesigned and migrated from Streamlit to React, providing a more responsive, organized, and scalable user experience.
KEYWORDS
This paper presented Mentesa v1.0, an enhanced version of the original no-code AI chatbot creation platform. The system continues to build upon its core architecture, which includes a Retrieval-Augmented Generation (RAG) pipeline, multi-tenant Firebase-based data management, and the Gemini-2.5-Pro language model. This iteration focuses on improving the platform's readiness for practical usage by introducing important features such as Firebase Authentication for secure user management, API key-based chatbot access, an authenticated embeddable widget system, usage monitoring through billing and quota management, and expanded document support for PDF, DOCX, and TXT formats. The introduced improvements directly address the limitations identified in the previous version and enhance the platform's security, flexibility, and usability. These additions allow Mentesa to support more realistic deployment scenarios while maintaining its original goal of enabling users without technical expertise to create and manage AI-powered chatbots. The migration from Streamlit to React further improves the platform's structure, maintainability, and ability to support future enhancements. Mentesa v1.0 demonstrates that improving accessibility and improving deployment capabilities can progress together. By combining a user-friendly no-code approach with essential production-oriented features, the platform moves closer to making advanced AI chatbot technology practical and accessible for a wider range of users.
[1] R. Yang et al.,RAGVA:Engineering RetrievalAugmented Generation an Experience Report, Elsevier (ScienceDirect), 2025 [2] Z. Li, Retrieval-Augmented Generation for Educational Application, Elsevier (ScienceDirect), 2025 [3] Y. Liu, Reducing Hallucinations of Large Language Models via HSP, Springer, 2025 [4] M. S. Swift, One Chatbot Safety Benchmark To Test Them All, IEEE Spectrum, 2024 [5] C. Batista et al.,Towards a Multi-Tenant Microservice Architecture: An Industrial Experience,IEEE, 2022 [6] M. Arif, A Literature Review on Model Conversion, Inference, and Optimization for Efficient Deployment, Elsevier (ScienceDirect), 2025 [7] R. K. Sharma & A. Dutta, AI-Powered Conversational Agents for Adaptive Learning Environments, IEEE (Conference), 2024 [8] N. F. Mir, AI-Driven Management of Dynamic MultiTenant Cloud Networks, IEEE (Conference), 2023 [9] G. Gamage et al., Multi-Agent RAG Chatbot Architecture for Decision Support in Net-Zero Emission Energy Systems, IEEE (Conference), 2024 [10] S. U. Singh, A Survey on Chatbots and Large Language Models: Testing and Evaluation, IEEE (Conference), 2025 [11] B. Suzic and M. Latinovic, "Rethinking Authorization Management of Web-APIs," 2020 IEEE International Conference on Pervasive Computing and Communications, Austin, TX, USA, 2020, doi: 10.1109/PerCom45495.2020.9127384.
Publication Date
6/25/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
Documents
Article Views
0
Total views since publication
PDF Downloads
4
Total downloads since publication
Geographic Reach
5
Digital Preservation: This article is permanently archived on Zenodo and complies with all data preservation standards across major indexing platforms.
Plagiarism Screening
This manuscript has been checked for plagiarism using TurnitinPlagiarismChecker software.
Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
Related research in the same subject area
Loading similar articles...