Ray

What is Ray?

Ray is an open-source AI financial advisor that connects to your bank data to answer questions and help you reason about spending, savings goals, and financial health. The core purpose is to provide advice based on your actual accounts and transaction history, while keeping the assistant running locally on your machine.

Ray is designed to “learn your full situation” by maintaining a persistent profile of your family, income, goals, risk tolerance, and key decisions. As your situation changes, it updates that context so follow-up advice reflects what happened previously.

Key Features

• Local AI financial advisor: Run the assistant on your own machine (no sign-ups or app store installation mentioned), so you interact with advice from your local environment.
• Bank data connection via Plaid: Securely link accounts through Plaid and use your real balances and transactions as the basis for answers.
• Persistent user profile (“remembers everything”): Keep a running record of your goals, preferences, life events, and past decisions so advice evolves over time rather than restarting from generic assumptions.
• 30+ finance tools for real data queries and calculations: The assistant queries linked financial data, runs calculations, and uses those results to respond to questions (e.g., projections, net worth, anomaly checks).
• Spending and goal reasoning from actual transactions: Get responses about affordability and how choices may affect balances and contributions based on observed income/spending patterns.
• Behavior scoring and streaks: A daily 0–100 behavior score with streaks and unlockable achievements tied to spending/discipline patterns (e.g., no dining, zero-spend days).

How to Use Ray

1. Install Ray using an npm command: npm install -g ray-finance.
2. Connect your bank through Plaid using ray link to link accounts.
3. Ask questions in the Ray interface (launched with ray status/ray as shown) about your spending, savings goals, account totals, or affordability. Ray uses your linked data to answer.

Use Cases

• Affordability check before a purchase or trip: Ask whether you can afford a trip while considering your actual income/spending patterns and how it may affect account balances and contributions.
• Debt payoff planning based on real interest rates and cash flow: Use Ray to incorporate where your money is going now and suggest next steps for debt prioritization when you have constraints (e.g., upcoming family events).
• Net worth snapshot across linked accounts: Request your net worth; Ray pulls balances from accounts such as checking, savings, credit cards, and loans and provides a single updated number.
• Rent or housing “percentage of income” review: Ask if your rent is too high, using take-home income and your account numbers to compute housing share and assess changes when your living situation changes.
• Detect unusual or unexpected account activity: Request scans for anomalies in recent transactions to find items like unexpected charges or duplicate payments.

FAQ

• Does Ray require sign-ups? The installation flow described uses an npm command and states “No accounts, no sign-ups, no app store.”

• How does Ray get my financial data? Ray connects to your bank accounts through Plaid, described as using secure linking and encryption.

• Is Ray a dashboard or a chatbot? The page distinguishes Ray from dashboards and chatbots, presenting it as a “financial brain” that queries your real financial data and takes actions like running calculations.

• How does Ray keep advice consistent over time? Ray keeps a persistent profile of your goals, preferences, life events, and past decisions, so later conversations build on earlier ones.

• What kind of outputs can I ask for? Examples shown include projections toward savings goals, affordability for trips, daily behavior scores, net worth totals, and scans for unusual transactions.

Alternatives

• Personal finance budgeting apps with transaction categorization: Tools that summarize spending in dashboards can show what you spent, but they may not provide decision-focused advice or local, data-driven dialogue.
• Spreadsheet-based planning: Spreadsheets can model debt payoff timelines and projections, but users often maintain formulas and analysis themselves rather than asking an assistant to query live account data.
• Chat-based financial Q&A without direct bank integration: Generic assistants may answer questions but typically won’t incorporate your actual linked balances and transaction history in the way Ray does via Plaid and local tools.