Intelligent Virtual Assistant for Mobile Workers: Towards Hybrid, Frugal and Contextualized Solutions

Field workers require expeditious and pertinent access to information to execute their duties, frequently in arduous environments. Conventional document search interfaces are ill-suited to these contexts, while fully automated approaches often lack the capacity to adapt to the variability of situations. This article explores a hybrid approach based on the use of specialized small language models (SLMs), combining natural language interaction, context awareness (static and dynamic), and structured command generation. The objective of this study is to demonstrate the feasibility of providing contextualized assistance for mobile agents using an intelligent conversational agent, while ensuring that reasonable resource consumption is maintained. The present case study pertains to the supervision of illumination systems on a university campus by technical agents. The static and the dynamic contexts are integrated into the user command to generate a prompt that queries a previously fine-tuned SLM. The methodology employed, the construction of five datasets for the purposes of evaluation, and the refinement of selected SLMs are presented herein. The findings indicate that models of smaller scale demonstrate the capacity to comprehend natural language queries and generate responses that can be effectively utilized by a tangible system. This work opens prospects for intelligent, resource-efficient, and contextualized assistance in industrial or constrained environments.