A question I keep getting: “How is EconLens different from just asking ChatGPT?”
Three things stand out.
First, the data is live. General models pull from training data that is months or years old. EconLens queries FRED, World Bank, Eurostat, OECD, and the ECB at the moment you ask, with every figure tagged with a date.
Second, it is smart about what it fetches. Ask about housing and it pulls mortgage rates, housing starts, and home prices. Ask about monetary policy and it pulls central bank rates, inflation, and yield curves. It reads your question and selects relevant indicators instead of giving you a generic essay.
Third, it compares to history automatically. Every response identifies the closest historical parallel in the last fifty years and tells you what happened next. A general model will only approximate this if you prompt it heavily and even then it is working from memory, not from live series.
Same question, fundamentally different engine. If you care about numbers being current, definitions being precise, and context being grounded in actual data, that difference matters.
econlens.app.