Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{194591,
        author = {AANGEE ALPESH JAIN and Harry joshi and Rutvik Ghadigaokar and Priyanka Bhilare},
        title = {J.A.R.V.I.S: A Tri-Tier Architecture for Low-Latency, Context-Aware Desktop AI Assistants},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {5170-5177},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=194591},
        abstract = {The rapid advancement of artificial intelligence has significantly transformed the interaction between humans and computing systems. However, existing voice assistants such as Siri, Alexa, and Google Assistant are primarily optimized for mobile or smart-home environments and often fail to meet the latency, privacy, and contextual processing requirements of professional desktop workflows. This paper presents J.A.R.V.I.S (Just A Rather Very Intelligent System), a desktop AI assistant designed using a Tri-Tier Gateway Architecture that balances the advantages of local execution and cloud-based intelligence. The proposed architecture integrates three layers: a rule-based command execution layer for zero-latency system operations, a local machine learning layer based on a Bidirectional Long Short-Term Memory (Bi-LSTM) intent classifier for natural language understanding, and a cloud-based generative AI layer using the Groq Llama-3 model for advanced reasoning and contextual conversation. The system also incorporates bilingual interaction capabilities, screen-aware computer vision, persistent conversational memory, and acoustic latency-masking techniques to improve user experience. Experimental observations demonstrate that the proposed hybrid architecture significantly reduces command response latency while maintaining high contextual intelligence and privacy. The results highlight the potential of hybrid AI architectures for next-generation desktop automation systems.},
        keywords = {AI Desktop Assistant, Bi-LSTM, Context-Awareness, Desktop Automation, Human-Computer Interaction, Hybrid AI Architecture, Natural Language Processing, Zero-Wait Control.},
        month = {March},
        }