The usage of large language models is restricted to people with quality education and high text literacy. In the absence of any intervention, the power of LLMs is likely to be exploited by empowered and elite groups in society. We aim to make access to LLMs more equitable by enabling low-text-literate users to interact with LLM-backed Conversational AI systems through iterative question answering over voice. We study this in the context of queries related to Social Welfare Schemes in India. We introduce actionable information retrieval (AIR), a system that improves accessibility for low-text literate users by guiding them through queries via an interactive flowchart of yes/no questions. This approach enhances user engagement, progressively leading users to precise answers without text-dense responses. We demonstrate these functionalities through Prabodhini, a light-weight, mobile-friendly application that uses Retrieval Augmented Generation (RAG) with chain-of-thought prompting over GPT-4o to retrieve personalized responses. Our pilot study results performed over the low-text literate users comprising the housekeeping, gardening, house-help, and security staff at BITS Pilani Hyderabad campus indicate a high level of user satisfaction, with positive feedback reported across all participants regarding the app’s usability, design, and functionality. Link to demonstration video can be found here , link to code, datasets, and evaluations are here .