Can citizens hold the technocratic state accountable when partisan cues offer little guidance? I study this question through antitrust enforcement, a domain governing trillions in economic activity yet rarely central to partisan debate. Using a survey experiment, I show citizens form systematic merger preferences from economically intuitive signals: deal size, industry overlap, and familiarity. Notably, revealing that a firm owns well-known brands increases support for intervention rather than generating leniency, an effect operating primarily through perceptions of acquiror corporate scale. A supplementary survey validates the inference: given nothing but the firms’ identi- ties, respondents use brand information to correctly classify whether the merging firms compete and to calibrate scrutiny to each deal’s economics, easing it for unrelated pairs while preserving it for true competitors. Decisions aligned with respondent preferences increase confidence in the antitrust agencies and presidential approval; enforcement failures reduce both. Complementing the experimental evidence, I construct an original dataset linking Hart-Scott-Rodino reviews from 2001 to 2020 to firm-level financial and brand data, showing that agency enforcement tracks these same dimensions, familiarity included. The alignment does not operate in the dark: nearly two-thirds of respondents name a real merger unprompted, and knowledge of enforcement concentrates where visible firms and agency action intersect. Together, the results show that citizens use familiarity as information rather than sentiment; democratic accountability can reach technical domains where partisan cues offer little guidance, to the extent that government’s choices are legible in the terms of everyday economic life.