Bridge

Inspiration

Goldman Sachs challenge to create an app for those who do not have easy access to traditional banking

What it does

Helps users budget and invest with a top stocks displayer, chatbot, budgeting, and tax estimate feature.

How we built it

Using React Native Expo and TailwindCSS for our front-end, as well as Flask for our backend server code while also integrating Cohere's Command R LLM for a financial advisor chatbot.

Challenges we ran into

One challenge we faced early on was deciding which LLM to power our financial advisor chatbot; we originally chose OpenAI's GPT models through their API, but decided to shift to Cohere's Command R which is purpose built for retrieval augmented generation (RAG) required for a use case chatbot. Another challenge we faced was the React Native front-end application's inability to communicate with our Flask server running on localhost. Our solution was to create a continuous deployment for our Flask server to onrender where the React Native application can properly make requests to our server API.

Accomplishments that we're proud of

An accomplishment we are proud of is implementin animations for our saving feature within the React Native front-end application. Another accomplishment we are proud of is allowing users to budget with fast and easy grouping.

What we learned

Over the hackathon, we learned to hone our front-end skills with reusable components and animation stylings. We also learned to create a chatbot using RAG with the Command R model from Cohere.

What's next for Bridge

Implementing a watchlist to help users see rising stocks

Built With

• cohere
• flask
• react-native
• tailwindcss