Visually impaired people face significant challenges when attempting to interact with and understand complex environments, and traditional assistive technologies often struggle to quickly provide necessary contextual understanding and interactive intelligence. This thesis presents Audo-Sight, a state-of-the-art assistive system that seamlessly integrates Multimodal Large Language Models (MLLMs) to provide expedient, context-aware interactions for Blind and Visually Impaired (BVI) individuals. The system operates in two different modalities: personalized interaction through user identification and public access in common spaces like museums and shopping malls. In tailored environments, the system adjusts its output to conform to the preferences of individual users, thus enhancing accessibility through a user-aware form of interaction. In shared environments, Audo-Sight employs a shared architecture that adapts to its current user with no manual reconfiguration required. To facilitate appropriate interactions with the LLM, the public Audo-Sight solution includes an Age-Range Determiner and Safe Query Filter. Additionally, the system ensures that responses are respectful to BVI users through NeMo Guardrails. By utilizing multimodal reasoning, BVI-cognizant response editing, and safeguarding features, this work represents a major leap in AI-driven accessibility technology capable of increasing autonomy, safety, and interaction for people with visual impairments in social settings. Finally, we present the integration of Audo-Sight and SmartSight, which enables enhanced situational awareness for BVI individuals. This integration takes advantage of the real-time visual analysis of SmartSight, combined with the extensive reasoning and interactive capabilities of Audo-Sight, and goes beyond object identification to provide context-driven, voice-controlled assistance in dynamic environments.