Gubbi Pay

What inspired us: The inspiration behind Gubbi came from the financial exclusion suffered by millions of people in rural and indigenous communities in Mexico. Many of these people lack access to basic financial services due to geographic isolation, cultural barriers, and limited technological infrastructure. We saw an opportunity to bridge this gap using blockchain and voice recognition. The idea was to create a tool that not only provides financial access, but also respects and integrates the cultural and linguistic diversity of these communities.

What we learned: Throughout the project, we learned that creating inclusive or niche apps requires a very detailed understanding of the cultural and social context of users. Designing a user-friendly interface for unbanked people in indigenous regions involves not only technical development but also cultural sensitivity. We explored the power of tokenization (Real World Assets - RWA) and how assets such as land, livestock, or crops can be transformed into financial opportunities for rural users. We also learned the importance of a clear regulatory understanding, as we navigated compliance with Mexican financial laws and international standards.

How we built it: Gubbi was built using Core DAO and Avalanche blockchain technology. We integrated voice recognition functionality in indigenous dialects to make navigation as seamless and accessible as possible. The app is designed to work both online and offline, ensuring usability in areas with limited internet connectivity. For financial services, we incorporated tokenization of real-world assets, allowing users to tokenize their land or livestock and use these assets as collateral for loans or other services. Smart contracts and staking mechanisms were developed to manage these tokenized assets and reward users with $GUBBI tokens, fostering economic growth within the app’s ecosystem.

Challenges we faced: One of the biggest challenges was ensuring accessibility through voice navigation in indigenous dialects, as linguistic complexity makes it difficult to find a suitable translation. Developing natural language processing (NLP) models for underrepresented languages ​​required significant collaboration with language experts. Additionally, achieving a user-friendly design for populations with low literacy and limited exposure to technology required iterative testing and ongoing user feedback.

Gubbi not only provides financial access, but also empowers communities to use their assets in ways that were previously out of reach.

Built With

• amazon-web-services
• avalanche
• core
• javascript
• mongodb
• native
• nlpsolidity
• node.js
• react
• solidity
• web3.js