AI-Driven voice Assistant for livelihood Mapping and NSQF-Aligned Skilling Recommendations for SC Communities under GIA component of PM-AJAY

• Background • The Pradhan Mantri Anusuchit Jaati Abhyuday Yojana (PM-AJAY) aims to reduce poverty among Scheduled Caste (SC) communities through livelihood promotion, skill development, and enterprise support under its Grant-in-Aid (GIA) component. A major challenge in implementation is the identification of appropriate skill training pathways that align with both the aspirations of beneficiaries and the actual livelihood opportunities available in their local regions. • Many target beneficiaries face barriers such as low digital literacy, limited awareness of modern trades, language constraints, and difficulty navigating text-heavy digital systems. As a result, there is often a mismatch between enrolled training programs and the beneficiary's interests, capabilities, or local market demand, leading to high dropout rates and poor post-training employment outcomes. • To improve inclusion and effectiveness, there is a need for an AI-enabled conversational system that can interact naturally in regional languages and dialects, understand beneficiary aspirations, assess skill gaps, and recommend suitable NSQF aligned livelihood opportunities in and around the beneficiary. • Basic Issues under GIA Component: • Lack of proper road map and Planning of the Perspective plans from execution to implementation • Identification of the participants Trained and skilled Financial consultants • Job placement issue after the skilling programme • Coordination Issues among the corporation, Ministry/Departments • Inadequate Technical and support team at ground level • Detailed Description The proposed solution should be an AI-driven, multilingual, voice-based virtual livelihood assistant capable of conducting conversational interviews with beneficiaries from aspirational SC communities. Instead of relying on traditional form-filling methods, the system should use voice interactions to collect information such as: • Educational background • Existing or traditional family occupations • Current livelihood activities • Skills and interests • Mobility and physical constraints • Preference for self-employment or wage employment • Local economic realities and opportunities The assistant should support regional languages and dialects to ensure accessibility for users with low literacy or limited digital exposure. The interaction should feel empathetic and conversational rather than administrative.The collected information should be analyzed using AI/MLbased profiling and recommendation mechanisms to identify: • Suitable NSQF-aligned training programs • Relevant trades and livelihood pathways • Skill gaps requiring intervention • Region-specific employment or enterprise opportunities The system should also function effectively in lowconnectivity and low-tech environments through deployment channels such as: • IVR-based phone calls for feature phone users • WhatsApp voice-note interfaces Lightweight mobile or kiosk-based solutions • Expected Solution: An AI-powered multilingual voice assistant application designed to help SC beneficiaries under PM-AJAY identify suitable skill training and livelihood opportunities.The app will support regional languages and local dialects, allowing users to interact through simple voice conversations instead of text-based forms.