EconoGenie: Personal Finance BRO

An autonomous AI agent crafted to help users master their finances with minimal human intervention.

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Inspiration

Every year, Americans lose billions of dollars to credit card late fees and interest charges

  • $243 billion lost nationwide in 2024 due to financial ignorance, with the average American losing $1,015.
  • $14 billion in late fees charged by credit card issuers in 2022, over 10% of total interest and fees.

What it does

At its core, ECONOGENIE uses advanced AI technology, integrating machine learning algorithms with real-time data analysis to deliver personalized and accurate financial insights.

How we built it

Essentially a RAG app using Custom Knowledge base Tech Stack Cortex Search for retrieval Mistral LLM (mistral-large2) on Snowflake Cortex for generation Streamlit Community Cloud for front end

Challenges we ran into

  1. Efficiently chunk and index custom knowledge for cost and Time effective manner

  2. Streamlit is ideal for quick prototyping but lacks advanced features for end-user applications.

  3. Manage Snowflake database sessions effectively.

Accomplishments that we're proud of

An app designed to provide affordable, world-class financial advisory services to low- and middle-income families, with Snowflake playing a pivotal role in enabling this solution.

What we learned

Don’t underestimate the time needed to build a fully functional app in just 2 weeks. We began working on it as a weekend project starting January 7th.

What's next for EconoGenie: Personal Finance BRO

An Fully Autonomous AI agent empowers you to take control as the decision-maker, providing all the knowledge and insights needed to manage your finances effortlessly and automatically.

Built With

  • black
  • ci/cd
  • flake8
  • llm
  • make
  • mistral
  • mypy
  • pytest
  • python
  • snowflake
  • snowpark
  • standard-software
  • streamlit
  • test-coverage
  • test-driven-development
  • trulens
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