AN OFFLINE WEB PLATFORM INTEGRATING OLLAMA FOR SECURE INTERNAL TRAINING IN ORGANIZATIONS AND ENTERPRISES
Keywords
DOI:
https://doi.org/10.34238/tnu-jst.15182Abstract
This study proposes an offline web-based platform operating within an internal network to support teaching and internal training in organizations with high data security requirements. The system integrates locally deployed large language models (LLMs) via the Ollama platform, combined with an internal knowledge base to enhance output accuracy and contextual relevance. The platform provides three main functionalities: (i) automatic multiple-choice question generation from training documents, (ii) slide-based lecture creation, and (iii) a chatbot for knowledge retrieval. The research adopts a design and development approach combined with experimental evaluation and user surveys. Experiments were conducted on real internal training datasets, including legal documents and forensic materials. The results indicate that the system achieves an accuracy ranging from 82% to 92%, with acceptable response times in a local deployment environment. Additionally, the system helps reduce lecture preparation time by approximately 40–60%. The findings confirm the feasibility of deploying offline AI systems in high-security environments and demonstrate strong potential for practical applications in internal training within organizations and enterprises. The study also contributes to bridging the gap between AI capability and data security requirements in sensitive domains.Downloads
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