Recommender systems wield a significant influence on society, particularly in regions like India where providing recommendations in Kannada, the local language, serves a wide user base. These systems play a crucial role in bridging the last mile connectivity gap. Typically, there are two primary approaches: content-based and collaborative filtering. Content-based methods leverage features like movie genres, while collaborative systems rely on user ratings. However, an innovative approach emerges with the utilization of Language Models (LMs). These models possess the unique ability to comprehend content in Kannada, enabling them to decipher movie intents and themes. This marks a departure from the traditional paradigms of recommendation systems. By employing Generative AI chatbots integrated with these LMs, a transformative solution comes to light. This AI-driven chatbot can seamlessly offer movie recommendations, eliminating the necessity for English proficiency or access to a personal computer. The significance of this advancement lies in extending recommendations to individuals who are not well-versed in English. This empowers a broader audience, enabling them to access personalized movie suggestions effortlessly. Consequently, the fusion of Language Models and recommender systems represents an ingenious stride towards inclusivity and accessibility. Through this fusion, barriers are dismantled, and the power of recommendations becomes democratized, fostering a more enriched entertainment experience for everyone.