Linc provides an AI‑driven financial reasoning platform that securely links users' bank, investment, and asset accounts via Plaid to create a single, read‑only model of their finances.
Funding
Funding not disclosed
Founders
Product
Problem
Consumers must aggregate data from multiple banks, investment platforms, and other assets manually, then interpret static dashboards or perform separate calculations to understand their financial situation and evaluate future scenarios. This fragmented approach makes it difficult to ask personalized, real‑time “what‑if” questions about retirement, debt payoff, or market changes.
Solution
Linc offers an AI‑driven financial reasoning platform that creates a single, read‑only model of a user’s finances by securely linking accounts through Plaid and ingesting live market and economic data. Users interact via natural‑language queries, receiving answers that incorporate cash, investments, debt, home value, goals, and current macro conditions. The system supports iterative follow‑up questions and scenario testing—such as early retirement, mortgage refinancing, or inflation spikes—without requiring manual data entry or separate tools. All sensitive data is anonymized before analysis, ensuring privacy while delivering market‑aware, actionable insights.
Target Audience
Primary users are households and individual investors who need personalized retirement planning, debt‑management, or portfolio analysis, as well as financially‑savvy consumers seeking AI‑driven answers to complex money questions.
Features
- Secure, read‑only account connections to over 12,000 financial institutions via Plaid, plus manual entry for non‑linked assets
- Unified financial model that combines cash, investments, debt, property values, and user goals into a single reasoning engine
- Real‑time market intelligence (interest rates, inflation, bond yields, stock data) sourced from FRED, Alpha Vantage, Polygon.io, and other providers
- Natural‑language query interface that supports follow‑ups and iterative “what‑if” scenario modeling
- Retirement and portfolio stress‑testing using historical market data and Monte Carlo simulations
- Transparent “show the math” feature exposing the calculation steps and data used for each answer
- Privacy‑first architecture with dual‑layer anonymization, AES‑256‑GCM encryption, and no ability to move money