Ask Linc has always been market-aware. Through integrations with FRED, Alpha Vantage, and Polygon.io, Linc understands the broader environment your finances live in—current interest rates, inflation trends, Treasury yields, credit conditions, and real-time market context. That layer matters: good financial analysis shouldn't pretend markets don't exist.
But market data alone can only get you so far. It can tell you that yields are up. It can't tell you whether your portfolio is positioned to take advantage of that—or whether you're overexposed to rate-sensitive equities you don't realize you're holding.
That's the gap SnapTrade, Tiingo, and Financial Modeling Prep close.
SnapTrade connects Ask Linc directly to your brokerage accounts—Fidelity, Schwab, Robinhood, Vanguard, Interactive Brokers, and more—surfacing your actual holdings, position-level data, asset allocation, and two years of transaction history. Not an estimated account value. Not a balance. What you actually own.
Tiingo then provides the historical backbone: dividend-adjusted price data that powers retirement stress tests and simulations grounded in real long-horizon returns. Financial Modeling Prep fills in the fund-level detail—expense ratios, asset class, geographic focus—so when you hold an ETF, Linc understands what's inside it, not just what it's worth today.
Together, these data sources are what turn a list of holdings into a genuine analysis. Consider what a real Linc conversation looks like:
"Re-evaluate my entire portfolio. Stress test it and give me a probability assessment on how long I'll likely be able to sustain my revised monthly drawdown, given that I'm currently 77 years old."
Linc's response: a complete analysis—2.90% withdrawal rate, 8+ years of cash reserves, 100% survival probability across stress test scenarios, and specific action items around RMDs and inflation protection. That kind of answer requires knowing what someone actually holds and having the historical data to model what could happen to it.
That's the difference between broad market awareness and personal financial intelligence. Ask Linc now offers both.
Market context only matters when it changes a decision
A list of rates and headlines is not portfolio intelligence. The useful work begins when current conditions are connected to an actual holding, cash need, debt rate, or withdrawal plan.
Consider two households with the same 70% stock allocation. One is twenty years from retirement and adding money every month. The other plans to use part of the portfolio for a down payment next spring. A market decline affects both account balances, but only one may be forced to sell on a fixed date. The allocation is identical; the financial risk is not.
That is why portfolio analysis needs at least three layers:
- What you own: holdings, concentration, account type, cost basis, and available cash.
- What the market is doing: rates, inflation, valuations, and the range of outcomes visible in history.
- What the money is for: retirement income, a home purchase, a tax payment, or another goal with a real date.
Where AI helps—and where it should not improvise
AI is useful for understanding the question, finding the relevant context, comparing tradeoffs, and explaining the result. It should not invent a holding, guess at a balance, or perform important arithmetic differently each time.
The calculation layer should remain reproducible. If a withdrawal rate, tax estimate, or stress-test result matters to the recommendation, the user should be able to inspect the inputs and see how the number was produced. The conversational layer is valuable because it makes that analysis easier to ask for—not because conversation makes uncertain math certain.