FIRE AI: Accelerating Your Path to Retirement

Problem

The journey to Financial Independence, Retire Early (FIRE) is confusing, overwhelming, and often inaccessible. Most people confront an overload of information—from thousands of mutual funds and ETFs to constantly shifting markets and complex tax rules—making it difficult to know whether they’re making sound financial decisions. Many young professionals default to autopilot investing in their 401(k)s and IRAs, rarely rebalance, and often miss opportunities to optimize costs or adjust allocations. Traditional tools tend to rely on static calculators or generic advice that fails to account for personal circumstances, while human advisors are expensive and constrained in scope. FIRE AI addresses these challenges by delivering personalized, data-driven insights powered by Monte Carlo simulations, real fund comparisons, and transparent cost optimization, offering a smarter and more adaptive planning experience than existing financial tools.

Our Solution

FIRE AI provides an integrated personal finance intelligence platform that helps users understand, optimize, and project their financial futures. The system combines three core engines:

1. A portfolio insights system that compares 7,000+ mutual funds and ETFs covering 80% assets under management (AUM) to uncover lower-cost substitutes and surface strategy-aligned alternatives.
2. A Monte Carlo retirement planner that runs 10,000 simulations per combination of desired retirement age and desired annual spending in retirement to quantify risk, uncertainty, and long-term outcomes based on spending, savings rate, and retirement horizon.
3. A tax optimization module that incorporates IRS rules to maximize after-tax returns and recommend tax strategies to help individuals save even more.
   Together, these components give everyday investors a personalized financial blueprint that goes far beyond what static calculators or robo-advisors offer.

Dataset

FIRE AI integrates three major categories of data:

• Portfolio Insights Data: Over 7,000 U.S. mutual funds and ETFs enriched with returns, volatility, sector exposure, expense ratios, and risk correlations using sources like SEC EDGAR and Yahoo Finance.
• Retirement Planner Inputs: User-provided variables such as income, spending, and savings rate used to model retirement trajectories.
• Tax Strategy Data: 2025 IRS-based tax brackets, deductions, credits, and contribution limits to optimize financial decisions.
  All fund data is cached for 30 days and covers over 80% of total U.S. AUM, ensuring both breadth and freshness.

Data Pipeline

The data pipeline connects multiple services into a unified workflow. Raw data is first ingested and stored in Google BigQuery. Monte Carlo simulations run in batch to estimate future wealth trajectories under market uncertainty. Funds are embedded using Google Vertex AI’s gemini-embedding-001 model to enable fast cosine-distance similarity searches. Tax logic is layered on top to provide net-return-aware recommendations. All components feed into a front-end interface that delivers real-time insights to users.

Model & Results

The core model powering fund comparisons uses semantic embeddings. Using gemini-embedding-001, FIRE AI transforms portfolio holdings into high-dimensional vectors and evaluates similarity through cosine distance. Benchmarking against Vanguard’s own similarity tool demonstrates over 90% accuracy in surfacing comparable funds, with the added advantage of identifying lower-expense substitutes when available.

Evaluation results show:

• Embedding distances correlate strongly with return-based distances (Pearson r ≈ 0.64).
• Regression analysis confirms that embedding distance is a statistically significant predictor of fund performance similarity.

These findings validate that the model reliably captures real-world investment strategy similarities—often outperforming industry tools by offering both accuracy and cost optimization.

Tools

FIRE AI is built using a modern, production-ready technical stack including:

• Google Cloud (Vertex AI, BigQuery, Gemini API) for data storage, model hosting, and embeddings.
• Python libraries such as NumPy, Pandas, scikit-learn, matplotlib, and FAISS/Pinecone for data processing, vector search, and analysis.
• Financial and economic data sources including SEC EDGAR, IRS datasets, Yahoo Finance, Morningstar, Vanguard, Fidelity, and BLS.
• React, TailwindCSS for front-end components.

This combination provides scalable, high-fidelity infrastructure with reliable financial data coverage.

Acknowledgements

FIRE AI was shaped through the support of instructors, mentors, and collaborators. Special thanks go to Fred Nugen and Korin Reid for their ongoing guidance, as well as the project mentors and domain experts who ensured the modeling approach remained grounded and rigorous. Early testers also played a key role by providing actionable feedback that helped refine both the interface and core features. The FIRE AI community and open-source ecosystem contributed invaluable tools, insights, and inspiration throughout development.