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Zeta: Solving financial literacy through AI

5 min read Ā·

Recently participated in hackathon organized by IIT Ghandhinagar and IIEC(their startup incubator), they recived 200 individual appications, and from them they only selected 50, and the best part which i liked was team was formed by them, so there was little suprise element with new team, and all were random dudes, some guy was from pune, hydrabad, IIT bombay etcā€¦

letā€™s start from some stats to get how bad the situation is, and does it even matter to solve it:

Problem

But Why??

ā€œStill people fall into fraud and debt trapā€

Reasons

Solution:

So We decided to divide the problem according to the target audience, and we came up with 2 solutions:

Our target audience was:

Both of them have different problems, and we decided to solve them using different approaches.

Tech Savvy Audience

They already know how to use mobile to everything, but still they donā€™t care enough to learn about financial literacy, they care about this after getting scammed, but thatā€™s what we want to avoid, and still google seo and ads are fucked up in some sense, you mostly end up getting misinformation than getting some useful information.

So peer to peer community is best for this solution, where one can share their experiance, and connect with them, and cuz itā€™s community misinformation can be stopped through moderation and other people.

Normal Audience(mostly Rural):

They donā€™t know how to use any app or touch screen phone, and for them it is huge learning cuve!

so

Why not leverage what they already have & know how to use very well

they already know how to use their keypad phone, and know how to call!

Demo:

Still we know that this llms and ai agents based solution is not perfect, but itā€™s a start, and we can improve it over time, and we are open to feedbacks and suggestions.

For this we are working on feature to transfer call to actuall human during mid call using function calling.

Main benifit of this is they can get information in their own language, and they can ask any question they want, and they can get information in real time 24x7.

Technical Architecture:

Architecture

Because we wanted to get low latency as much as possible, running llama on local or on any gpu server will still be slow, so we used GROQ(blazing fast cuz of lpu)

I mostly worked on the backend part and on the LLM(with lokesh), i donā€™t like that much frontend, so other 3 guyes handled frontend, i created simple python script for call microservice, and used twilio for call handling.

Scalability of solution

Impact

and we won the hackathonšŸ„³ won

Hackathon Experience:

I guess thatā€™s it all!

IITGN

PS: look ma, i am in the news :) news