Thesignal-to-noiseprobleminreal-timemarkets
Financial information has never been more abundant, or more fragmented. News, data releases, official statements, and policy signals arrive continuously, across sources that don't reference each other and rarely arrive labeled by importance. The bottleneck in modern markets isn't access to information — it's the cost of figuring out, in real time, which signal actually matters.
This is a harder problem than it looks. A statement from a central bank official can move markets significantly; a similar-sounding statement from a lower-level official often doesn't. A scheduled data release can be priced in already, or can surprise markets and trigger a sharp repricing, depending on how it compares to expectations that shift hour to hour. Distinguishing these cases requires more than keyword matching or sentiment scoring — it requires connecting an event to institutional context, historical precedent, and what the market was already expecting.
This is the layer we build for: not collecting more information, but reducing it — correctly, and fast enough for the reduction to still be useful by the time a person sees it.