A multi-lingual digital platform or mobile application that matches marginalized entrepreneurs with concessional loan schemes, calculates EMIs, and locates the nearest authorized channel partner.
Ministry of Social Justice and Empowerment (MoSJE) · Department of Social Justice and Empowerment · Software
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Decomposed from what the description asks for. Nothing added.
Smart Scheme Recommender
An AI or rule-based engine that recommends suitable credit or educational loan schemes based on user inputs like project type, cost, and income.
Financial Calculator
A dynamic tool that calculates projected EMIs using scheme guidelines, interest rates, loan limits, and moratorium periods.
Geo-Spatial Partner Locator
A mapping service integration that identifies the nearest eligible channel partner based on user location and partner fund utilization eligibility.
Both columns are read off the brief's own wording. Nothing here is inferred from the ministry's name.
The jury will check whether the platform supports multiple languages, accurately recommends schemes based on user inputs, correctly calculates EMIs, and maps users to valid channel partners using location data.
A jury can still ask about these. Decide them deliberately rather than by accident.
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 must be supported?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
What APIs or databases should be used for partner data and NPA status?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
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 smart scheme recommender. How would you build that?
Decoded from SIH26092 itself — An AI or rule-based engine that recommends suitable credit or educational loan schemes based on user inputs like project type, cost, and income. The brief asks for it by name.
What the organisers attached, and what the brief assumes you can get.
Same organisation, same year. Reading two of theirs tells you more about what they care about than reading one.
Pick what you are about to do and copy the prompt. It carries the organisers' own wording, the constraints they never spell out, and an instruction not to invent requirements they never set.
Who has this problem, what already exists, and what you would have to find out.
The brief asks for financial calculator. How would you build that?
Decoded from SIH26092 itself — A dynamic tool that calculates projected EMIs using scheme guidelines, interest rates, loan limits, and moratorium periods. The brief asks for it by name.
The brief asks for geo-spatial partner locator. How would you build that?
Decoded from SIH26092 itself — A mapping service integration that identifies the nearest eligible channel partner based on user location and partner fund utilization eligibility. 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 whether the platform supports multiple languages, accurately recommends schemes based on user inputs, correctly calculates EMIs, and maps users to valid channel partners using location data.
This is the evaluator read for your problem statement, decoded from the brief's own wording.