Fireline was inspired by issues we discovered with existing retirement calculators, which often feel rigid, inaccurate, and disconnected from how people actually spend and manage money. Many existing tools use static assumptions that don't model real financial behavior, causing users to be uncertain about how realistic their projections truly are. We wanted to create a platform that makes retirement planning more dynamic, personalized, and grounded in real data rather than fixed formulas.
We use Monte Carlo simulation combined with user spending and financial inputs to estimate the probability of retiring at different ages. Instead of presenting a single deterministic outcome, the platform models thousands of possible financial futures, helping users understand risk and uncertainty in a more realistic way. By turning complex projections into clear insights, Fireline aims to give people a more intuitive understanding of their retirement readiness and how small changes can affect long-term outcomes.
Our web application is built using React and Next.js, with a Supabase database and authentication. A key part of the project involved developing and debugging custom simulation logic capable of running probabilistic financial models efficiently within a web application.
One of the biggest challenges we faced was managing scope while still building something meaningful within a limited timeframe. Designing the simulation model required careful iteration and bug fixing, especially when testing to see if our model outputs were realistic. We also spent significant time refining the product direction and user experience so that complex financial information could be communicated clearly without overwhelming the user.
In the future, we would improve Fireline by refining the in-app chatbot to provide more contextual guidance and integrating Plaid to allow users to securely connect their bank accounts and automatically import spending data. We also want to improve personalization within the simulation engine and enhance visualizations so users can better understand long-term financial risk. Our goal is to move retirement planning away from static guesswork and toward intelligent, data-driven forecasting that helps people make more confident decisions about their future.
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