SIH26097: AI-Driven voice Assistant for livelihood Mapping and NSQF-Aligned Skilling Recom — SIH 2026 | Wynit

SIH26097: AI-Driven voice Assistant for livelihood Mapping and NSQF-Aligned Skilling Recom — SIH 2026 | Wynit

What they'll ask you

12

Generated from the brief's own wording and the competition's published rules, never from a guess about what this ministry prefers.

Which specific regional languages and dialects must be supported?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
What specific accuracy rate is expected for the AI recommendation matching?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Which IVR or telephony service provider APIs should be integrated?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.

9 morethe ones a panel gets to if there is time

Has any part of this been shown at a previous event, hackathon or college project?
The guidelines are explicit: your solution must not have appeared in any previous event or programme, of any sort. A recycled project is what a team under time pressure reaches for.
The brief asks for voice interaction interface. How would you build that?
Decoded from SIH26097 itself: Supports regional languages and local dialects for natural, empathetic voice conversations. The brief asks for it by name.
The brief asks for deployment channels. How would you build that?
Decoded from SIH26097 itself: Functions via IVR phone calls, WhatsApp voice notes, and lightweight mobile or kiosk interfaces. The brief asks for it by name.
The brief asks for beneficiary profiling engine. How would you build that?
Decoded from SIH26097 itself: Collects educational background, family occupations, skills, interests, and constraints using AI. The brief asks for it by name.
The brief asks for nsqf recommendation system. How would you build that?
Decoded from SIH26097 itself: Analyzes profiles using AI to recommend suitable NSQF-aligned training programs and employment opportunities. The brief asks for it by name.
Where does your data come from: a published source, one you collect, or one you generate?
No dataset is attached to this problem statement, so sourcing it is part of the work and nobody told you that.
Why not use what already exists? Name the closest thing to this that is already running.
A team that has not named the alternative themselves is answering this for the first time in the room.
Which single thing will you demonstrate end to end, start to finish, with nothing skipped?
Ours, not a rule: a narrow thing that fully works survives questioning better than a broad thing that half works. If nobody on the team can name it, that is the finding.
Show me this working: the jury will check if the voice assistant actually supports regional languages and dialects, handles low-connectivity environments through IVR or WhatsApp, and accurately matches user profiles to NSQF-aligned training programs.
This is the evaluator read for your problem statement, decoded from the brief's own wording.

The government's original description (437 words)

• 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 beneficiaryâ€™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.