Watt uses a reasoning graph — a derivative layer built on top of a base graph — that represents not what data points are, but how everything in the graph relates to everything else.

Watt pre-computes five relationship types across all signals daily, producing 16 trillion pre-computed relationship outcomes. This is what makes the data traversable by an AI agent in sub-second time, rather than requiring a human analyst working through pre-aggregated fields.