A Dual-Pathway AI Architecture for Tourism Logistics and Support in Low-Resource Environments

Aashish Dhakal; Bibek Ropakheti

doi:doi:10.11648/j.ajai.20261001.14

Research Article |

| Peer-Reviewed

A Dual-Pathway AI Architecture for Tourism Logistics and Support in Low-Resource Environments

Aashish Dhakal^*

, Bibek Ropakheti

Published in American Journal of Artificial Intelligence (Volume 10, Issue 1)

Received: 19 December 2025 Accepted: 4 January 2026 Published: 23 January 2026

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

The tourism industry in high-altitude regions, specifically the Himalayas, faces two critical and distinct challenges: ensuring operational safety amidst volatile weather and traffic conditions, and overcoming commercial inefficiency caused by a lack of 24/7 customer support. Traditional solutions have largely failed to address these issues simultaneously, often relying on fragmented manual processes or static chatbots that lack real-time capabilities. This paper presents a unified Artificial Intelligence (AI) platform designed to address these distinct problems using a novel ”Hybrid AI” architecture. A ”Two-Brain” system is proposed that integrates Retrieval Augmented Generation (RAG) for static, knowledge-intensive customer queries and Tool-Using Large Language Model (LLM) Agents for dynamic, real-time logistical support. By leveraging open source technologies, specifically Django for the backend framework and PostgreSQL with pgvector for high-dimensional vector storage, and implementing semantic caching, a cost-effective, maintainable solution is demonstrated for Small and Medium Enterprises (SMEs) in developing economies. The design mitigates hallucination risks through strict context faithfulness protocols and ensures data sovereignty via a self-hosted infrastructure. Performance metrics regarding average latency, token cost efficiency, and data freshness are analyzed, showing that the dual-pathway approach significantly optimizes resource usage compared to traditional methods. Specifically, the semantic caching mechanism reduces API costs by approximately 60 percent for repetitive queries, while the real-time agent ensures critical safety data is retrieved with a freshness of under 10 seconds. This study concludes that such a hybrid architecture provides a scalable, safe, and economically viable model for modernizing tourism operations in low-resource environments.

Published in	American Journal of Artificial Intelligence (Volume 10, Issue 1)
DOI	10.11648/j.ajai.20261001.14
Page(s)	42-47
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2026. Published by Science Publishing Group

Keywords

Retrieval-Augmented Generation, LLM Agents, Tool Learning, Intent Classification, Tourism Technology, Semantic Caching, Smart Tourism

References

[1]	H. Soudani, E. Kanoulas, and F. Hasibi, “Fine Tuning vs. Retrieval Augmented Generation for Less Popular Knowledge,” in Proceedings of the 2nd International ACM SIGIR Conference on Information Retrieval in the Asia Pacific, 2024. https://doi.org/10.1145/3637528.3671470
[2]	W. Fan, Y. Ding, L. Ning, S. Wang, H. Li, D. Yin, T.-S. Chua, and Q. Li, “A survey on RAG meeting LLMs: Towards retrieval-augmented large language models,” in Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024, pp. 6491-6501. https://doi.org/10.1145/3637528.3671470
[3]	C. Qu, S. Dai, X. Wei, H. Cai, S. Wang, D. Yin, J. Xu, and J. Wen, “Tool learning with large language models: A survey,” Frontiers of Computer Science, pp. 1-33, 2024.
[4]	G. Mialon, R. Dessi, M. Lomeli, C. Nalmpantis, R. Pasunuru, R. Raileanu, B. Roziere, T. Schick, J. Dwivedi-Yu, A. Celikyilmaz, E. Grave, Y. LeCun, and T. Scialom, “Augmented language models: A survey, ” Transactions on Machine Learning Research, 2023.
[5]	C. Couturier, S. Mastorakis, H. Shen, S. Rajmohan, and V. Rühle, “Semantic caching of contextual summaries for efficient question-answering with language models,” arXiv preprint arXiv: 2505.11271, 2025. https://doi.org/10.48550/arXiv.2505.11271
[6]	W. Zhang and J. Zhang, “Hallucination mitigation for retrieval-augmented large language models: A review,” Mathematics, vol. 13, no. 5, p. 856, 2025. https://doi.org/10.3390/math13050856
[7]	L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. L. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama,A. Ray, J. Schulman, J. Hilton, F. Kelton, L. Miller, M. Simens, A. Askell, P. Christiano, J. Leike, and R. Lowe, “Training language models to follow instructions with human feedback,” in Advances in Neural Information Processing Systems, vol. 35, 2022, pp. 27730-27744.
[8]	P. Lewis, E. Perez, A. Piktus, F. Petroni, V. Karpukhin, N. Goyal, H. Küttler, M. Lewis, W. Yih, T. Rocktsächel, S. 4Riedel, and D. Kiela, “Retrieval-Augmented Generation for knowledge-intensive NLP tasks,” in Advances in Neural Information Processing Systems, vol. 33, 2020, pp. 9459-9474.
[9]	W. X. Zhao, K. Zhou, J. Li, T. Tang, X. Wang, Y. Hou, Y. Min, B. Zhang, J. Zhang, Z. Dong, et al., “A survey of large language models,” arXiv preprint arXiv: 2303.18223, 2023. https://doi.org/10.48550/arXiv.2303.18223
[10]	Y. Gao, Y. Xiong, X. Gao, K. Jia, J. Pan, Y. Bi, Y. Dai, J. Sun, and H. Wang, “Retrieval-augmented generation for large language models: A survey,” arXiv preprint arXiv: 2312.10997, 2023. https://doi.org/10.48550/arXiv.2312.10997
[11]	Y. Qin, S. Liang, Y. Ye, K. Zhu, L. Yan, Y. Lu, Y. Lin,X. Cong, X. Tang, B. Qian, et al., “Toolllm: Facilitating large language models to master 16000+ real-world apis,” arXiv preprint arXiv: 2307.16789, 2023. https://doi.org/10.48550/arXiv.2307.16789
[12]	L. Huang, W. Yu, W. Ma, W. Zhong, Z. Feng, H. Wang, Q. Chen, W. Peng, X. Feng, B. Qin, et al., “A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions,” arXiv preprint arXiv: 2311.05232, 2023. https://doi.org/10.48550/arXiv.2311.05232
[13]	H. Li, Y. Su, D. Cai, Y. Wang, and L. Liu, “A survey on retrieval-augmented text generation,” arXiv preprint arXiv: 2202.01110, 2022. https://doi.org/10.48550/arXiv.2202.01110
[14]	A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in neural information processing systems, vol. 30, 2017.
[15]	H. Touvron, T. Lavril, G. Izacard, X. Martinet, M. A. Lachaux, T. Lacroix, B. Rozière, N. Goyal, E. Hambro, F. Azhar, et al., “Llama: Open and efficient foundation language models,” arXiv preprint arXiv: 2302.13971, 2023. https://doi.org/10.48550/arXiv.2302.13971
[16]	T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, et al., “Language models are few-shot learners,” in Advances in neural information processing systems, vol. 33, 2020, pp. 1877-1901.

Cite This Article

Plain Text BibTeX RIS

APA Style

Dhakal, A., Ropakheti, B. (2026). A Dual-Pathway AI Architecture for Tourism Logistics and Support in Low-Resource Environments. American Journal of Artificial Intelligence, 10(1), 42-47. https://doi.org/10.11648/j.ajai.20261001.14

Copy | Download

ACS Style

Dhakal, A.; Ropakheti, B. A Dual-Pathway AI Architecture for Tourism Logistics and Support in Low-Resource Environments. Am. J. Artif. Intell. 2026, 10(1), 42-47. doi: 10.11648/j.ajai.20261001.14

Copy | Download

AMA Style

Dhakal A, Ropakheti B. A Dual-Pathway AI Architecture for Tourism Logistics and Support in Low-Resource Environments. Am J Artif Intell. 2026;10(1):42-47. doi: 10.11648/j.ajai.20261001.14

Copy | Download

@article{10.11648/j.ajai.20261001.14,
  author = {Aashish Dhakal and Bibek Ropakheti},
  title = {A Dual-Pathway AI Architecture for Tourism Logistics and Support in Low-Resource Environments},
  journal = {American Journal of Artificial Intelligence},
  volume = {10},
  number = {1},
  pages = {42-47},
  doi = {10.11648/j.ajai.20261001.14},
  url = {https://doi.org/10.11648/j.ajai.20261001.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajai.20261001.14},
  abstract = {The tourism industry in high-altitude regions, specifically the Himalayas, faces two critical and distinct challenges: ensuring operational safety amidst volatile weather and traffic conditions, and overcoming commercial inefficiency caused by a lack of 24/7 customer support. Traditional solutions have largely failed to address these issues simultaneously, often relying on fragmented manual processes or static chatbots that lack real-time capabilities. This paper presents a unified Artificial Intelligence (AI) platform designed to address these distinct problems using a novel ”Hybrid AI” architecture. A ”Two-Brain” system is proposed that integrates Retrieval Augmented Generation (RAG) for static, knowledge-intensive customer queries and Tool-Using Large Language Model (LLM) Agents for dynamic, real-time logistical support. By leveraging open source technologies, specifically Django for the backend framework and PostgreSQL with pgvector for high-dimensional vector storage, and implementing semantic caching, a cost-effective, maintainable solution is demonstrated for Small and Medium Enterprises (SMEs) in developing economies. The design mitigates hallucination risks through strict context faithfulness protocols and ensures data sovereignty via a self-hosted infrastructure. Performance metrics regarding average latency, token cost efficiency, and data freshness are analyzed, showing that the dual-pathway approach significantly optimizes resource usage compared to traditional methods. Specifically, the semantic caching mechanism reduces API costs by approximately 60 percent for repetitive queries, while the real-time agent ensures critical safety data is retrieved with a freshness of under 10 seconds. This study concludes that such a hybrid architecture provides a scalable, safe, and economically viable model for modernizing tourism operations in low-resource environments.
},
 year = {2026}
}

Copy | Download

TY  - JOUR
T1  - A Dual-Pathway AI Architecture for Tourism Logistics and Support in Low-Resource Environments
AU  - Aashish Dhakal
AU  - Bibek Ropakheti
Y1  - 2026/01/23
PY  - 2026
N1  - https://doi.org/10.11648/j.ajai.20261001.14
DO  - 10.11648/j.ajai.20261001.14
T2  - American Journal of Artificial Intelligence
JF  - American Journal of Artificial Intelligence
JO  - American Journal of Artificial Intelligence
SP  - 42
EP  - 47
PB  - Science Publishing Group
SN  - 2639-9733
UR  - https://doi.org/10.11648/j.ajai.20261001.14
AB  - The tourism industry in high-altitude regions, specifically the Himalayas, faces two critical and distinct challenges: ensuring operational safety amidst volatile weather and traffic conditions, and overcoming commercial inefficiency caused by a lack of 24/7 customer support. Traditional solutions have largely failed to address these issues simultaneously, often relying on fragmented manual processes or static chatbots that lack real-time capabilities. This paper presents a unified Artificial Intelligence (AI) platform designed to address these distinct problems using a novel ”Hybrid AI” architecture. A ”Two-Brain” system is proposed that integrates Retrieval Augmented Generation (RAG) for static, knowledge-intensive customer queries and Tool-Using Large Language Model (LLM) Agents for dynamic, real-time logistical support. By leveraging open source technologies, specifically Django for the backend framework and PostgreSQL with pgvector for high-dimensional vector storage, and implementing semantic caching, a cost-effective, maintainable solution is demonstrated for Small and Medium Enterprises (SMEs) in developing economies. The design mitigates hallucination risks through strict context faithfulness protocols and ensures data sovereignty via a self-hosted infrastructure. Performance metrics regarding average latency, token cost efficiency, and data freshness are analyzed, showing that the dual-pathway approach significantly optimizes resource usage compared to traditional methods. Specifically, the semantic caching mechanism reduces API costs by approximately 60 percent for repetitive queries, while the real-time agent ensures critical safety data is retrieved with a freshness of under 10 seconds. This study concludes that such a hybrid architecture provides a scalable, safe, and economically viable model for modernizing tourism operations in low-resource environments.

VL  - 10
IS  - 1
ER  -

Copy | Download

Author Information

Aashish Dhakal

Department of Information Technology, Presidential Graduate School, Kathmandu, Nepal

Contact Email

http://orcid.org/0009-0008-8367-2502
Bibek Ropakheti

Department of Information Technology, Presidential Graduate School, Kathmandu, Nepal

http://orcid.org/0009-0009-8395-7417

Download PDF

Submit an Article

Sections

Plain Text BibTeX RIS

APA Style

Dhakal, A., Ropakheti, B. (2026). A Dual-Pathway AI Architecture for Tourism Logistics and Support in Low-Resource Environments. American Journal of Artificial Intelligence, 10(1), 42-47. https://doi.org/10.11648/j.ajai.20261001.14

Copy | Download

ACS Style

Dhakal, A.; Ropakheti, B. A Dual-Pathway AI Architecture for Tourism Logistics and Support in Low-Resource Environments. Am. J. Artif. Intell. 2026, 10(1), 42-47. doi: 10.11648/j.ajai.20261001.14

Copy | Download

AMA Style

Dhakal A, Ropakheti B. A Dual-Pathway AI Architecture for Tourism Logistics and Support in Low-Resource Environments. Am J Artif Intell. 2026;10(1):42-47. doi: 10.11648/j.ajai.20261001.14

Copy | Download

@article{10.11648/j.ajai.20261001.14,
  author = {Aashish Dhakal and Bibek Ropakheti},
  title = {A Dual-Pathway AI Architecture for Tourism Logistics and Support in Low-Resource Environments},
  journal = {American Journal of Artificial Intelligence},
  volume = {10},
  number = {1},
  pages = {42-47},
  doi = {10.11648/j.ajai.20261001.14},
  url = {https://doi.org/10.11648/j.ajai.20261001.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajai.20261001.14},
  abstract = {The tourism industry in high-altitude regions, specifically the Himalayas, faces two critical and distinct challenges: ensuring operational safety amidst volatile weather and traffic conditions, and overcoming commercial inefficiency caused by a lack of 24/7 customer support. Traditional solutions have largely failed to address these issues simultaneously, often relying on fragmented manual processes or static chatbots that lack real-time capabilities. This paper presents a unified Artificial Intelligence (AI) platform designed to address these distinct problems using a novel ”Hybrid AI” architecture. A ”Two-Brain” system is proposed that integrates Retrieval Augmented Generation (RAG) for static, knowledge-intensive customer queries and Tool-Using Large Language Model (LLM) Agents for dynamic, real-time logistical support. By leveraging open source technologies, specifically Django for the backend framework and PostgreSQL with pgvector for high-dimensional vector storage, and implementing semantic caching, a cost-effective, maintainable solution is demonstrated for Small and Medium Enterprises (SMEs) in developing economies. The design mitigates hallucination risks through strict context faithfulness protocols and ensures data sovereignty via a self-hosted infrastructure. Performance metrics regarding average latency, token cost efficiency, and data freshness are analyzed, showing that the dual-pathway approach significantly optimizes resource usage compared to traditional methods. Specifically, the semantic caching mechanism reduces API costs by approximately 60 percent for repetitive queries, while the real-time agent ensures critical safety data is retrieved with a freshness of under 10 seconds. This study concludes that such a hybrid architecture provides a scalable, safe, and economically viable model for modernizing tourism operations in low-resource environments.
},
 year = {2026}
}

Copy | Download

TY  - JOUR
T1  - A Dual-Pathway AI Architecture for Tourism Logistics and Support in Low-Resource Environments
AU  - Aashish Dhakal
AU  - Bibek Ropakheti
Y1  - 2026/01/23
PY  - 2026
N1  - https://doi.org/10.11648/j.ajai.20261001.14
DO  - 10.11648/j.ajai.20261001.14
T2  - American Journal of Artificial Intelligence
JF  - American Journal of Artificial Intelligence
JO  - American Journal of Artificial Intelligence
SP  - 42
EP  - 47
PB  - Science Publishing Group
SN  - 2639-9733
UR  - https://doi.org/10.11648/j.ajai.20261001.14
AB  - The tourism industry in high-altitude regions, specifically the Himalayas, faces two critical and distinct challenges: ensuring operational safety amidst volatile weather and traffic conditions, and overcoming commercial inefficiency caused by a lack of 24/7 customer support. Traditional solutions have largely failed to address these issues simultaneously, often relying on fragmented manual processes or static chatbots that lack real-time capabilities. This paper presents a unified Artificial Intelligence (AI) platform designed to address these distinct problems using a novel ”Hybrid AI” architecture. A ”Two-Brain” system is proposed that integrates Retrieval Augmented Generation (RAG) for static, knowledge-intensive customer queries and Tool-Using Large Language Model (LLM) Agents for dynamic, real-time logistical support. By leveraging open source technologies, specifically Django for the backend framework and PostgreSQL with pgvector for high-dimensional vector storage, and implementing semantic caching, a cost-effective, maintainable solution is demonstrated for Small and Medium Enterprises (SMEs) in developing economies. The design mitigates hallucination risks through strict context faithfulness protocols and ensures data sovereignty via a self-hosted infrastructure. Performance metrics regarding average latency, token cost efficiency, and data freshness are analyzed, showing that the dual-pathway approach significantly optimizes resource usage compared to traditional methods. Specifically, the semantic caching mechanism reduces API costs by approximately 60 percent for repetitive queries, while the real-time agent ensures critical safety data is retrieved with a freshness of under 10 seconds. This study concludes that such a hybrid architecture provides a scalable, safe, and economically viable model for modernizing tourism operations in low-resource environments.

VL  - 10
IS  - 1
ER  -

Copy | Download