VOYNEX: Intelligent Travel Assistant for Personalized Trip Planning

This paper described VOYNEX, a multi-tenant travel management system featuring AI-assisted itinerary planning, resource management by agencies, book-ing, and payment signature validation in a modular monolithic structure. The implementation makes use of Next.js, NestJS, TypeORM, PostgreSQL, the Groq API, and Llama 3.1 8B along with Razorpay test-mode payments. There are four roles available in the appli-cation, with organization-specific service checks being performed to achieve logical tenant isolation. Functional testing revealed that about 82% of out- puts for 28 scenarios of itinerary generation were con-sidered relevant and usable after manual inspection. No unauthorized access was detected during 45 attempts at cross-tenant access and 60 tests involving role permis-sions. For all 30 valid and 10 invalid payment signature inputs, the appropriate responses were received. This suggests the possibility of integrating an AI-assisted solution into a traditional travel management system. However, automated inventory matching, row-level se-curity in the database, full processing of webhooks, and performance testing are still left undone.

[1]     U. Gretzel, M. Sigala, Z. Xiang, and C. Koo, “Smart tourism: foundations and developments,” Electronic Markets, vol. 25, no. 3, pp. 179–188, 2015. [2]     D. Buhalis and R. Law, “Progress in information technology and tourism management: 20 years on and 10 years after the Internet—the state of eTourism research,” Tourism Manage-ment, vol. 29, no. 4, pp. 609–623, 2008. [3]     T. B. Brown et al., “Language models are few-shot learners,” in Advances in Neural Information Processing Systems, vol. 33, pp. 1877–1901, 2020. [4]     J. Bezemer and A. Zaidman, “Multi-tenant SaaS applications: maintenance dream or nightmare?” in Proc. 4th Int. Joint ERCIM/IWPSE Symp. Software Evolution, pp. 88–92, 2010. [5]     Microsoft, “Architect multitenant solutions on Azure: tenancy models overview.” [Online]. Available: https://learn. microsoft.com/en-us/azure/architecture/ guide/multitenant/approaches/overview [6]     PostgreSQL Global Development Group, “Row security poli-cies.” [Online]. Available: https://www.postgresql. org/docs/current/ddl-rowsecurity.html [7]     J. Borra`s, A. Moreno, and A. Valls, “Intelligent tourism recom-mender systems: A survey,” Expert Systems with Applications, vol. 41, no. 16, pp. 7370–7389, 2014. [8]     A. Vaswani et al., “Attention is all you need,” in Advances in Neural Information Processing Systems, vol. 30, pp. 5998–6008, 2017. [9]     M. Fowler, Patterns of Enterprise Application Architecture. Addison-Wesley, 2002. [10]    S. Newman, Building Microservices, 2nd ed. O’Reilly Media, 2021.