FlowFI

FlowFi is an AI-powered cashflow copilot that helps startups forecast money in/out, predict risks, and suggest actions using Google Gemini — turning financial chaos into clear, confident decisions.

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Inspiration

Most startups fail not because of a bad product — but because they run out of cash without realizing it. In fast-moving tech environments, invoices pile up, payments get delayed, and founders lose track of where their money stands. Traditional accounting tools are reactive, not predictive.

We built FlowFi to give startups financial foresight, not just hindsight — a tool that predicts, explains, and guides.

What it does

FlowFi is an AI-powered cashflow forecasting and decision-support platform for startups and small businesses. It -

  • Connects with QuickBooks and Plaid to gather real financial data.
  • Uses machine learning to forecast future cash inflows and outflows.
  • Flags risks like negative cash gaps and late payers.
  • Lets founders ask natural questions like: “What if revenue drops by 20% next month?”
  • Uses Google Gemini to generate easy-to-understand explanations and actionable recommendations.
  • FlowFi turns financial chaos into clarity — showing how long your runway lasts and what you can do to extend it.

How we built it

Backend: Built using FastAPI (Python) to handle data ingestion, forecasting, and Gemini integration.

Data Sources: Plaid API for bank transactions and account balances. QuickBooks API for invoices, bills, and customer payments.

Models: SARIMAX for time-series cashflow forecasting. Logistic regression to estimate late-payment probability.

\(Combined model: Expected Cash = Scheduled Cash × (1−𝑃(Late Payment))\) \(Expected Cash=Scheduled Cash×(1−P(Late Payment))\)

AI Layer: Gemini 2.5 Flash for text generation, scenario simulation, and JSON-based function calling.

Frontend: A React dashboard visualizing forecasts (P10, P50, P90) with an integrated Gemini-powered chat window.

Challenges we ran into

  1. Handling incomplete and noisy financial data from different APIs.
  2. Designing models that work even with limited historical data.
  3. Integrating multiple APIs (Plaid, QuickBooks, Gemini) securely and efficiently.
  4. Making forecasts interpretable, not just accurate — turning data science into real-world decisions.

Accomplishments that we're proud of

Built a fully functional prototype combining predictive modeling and conversational AI. Designed a system that understands finance and speaks human. Created an experience where a founder can ask questions instead of reading spreadsheets. Brought clarity to one of the hardest problems in startup life — cash visibility.

What we learned

  • How to merge machine learning with generative AI to make complex insights accessible.
  • The importance of explainability and trust in financial systems.
  • That startup financial data is messy — building resilient models means embracing imperfection.
  • How to build production-like systems quickly under hackathon constraints.

What's next for FlowFi

  • Integrate real-time alerts for cashflow risks via email and Slack.
  • Add AI-driven budgeting that automatically adjusts forecasts based on market signals.
  • Introduce a mobile app for on-the-go founders.
  • Expand to support multi-currency and cross-border accounts for global startups.
  • Ultimately, FlowFi’s goal is to become the financial brain for startups — a partner that predicts, guides, and helps them grow sustainably.

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