Deployed conformal predictors are long-lived decision infrastructure reused over finite operational windows. In practice, stakeholders care not only about marginal coverage, but also about operational quantities: how often the system commits versus defers, and what error exposure it induces when it acts. These deployment-facing quantities are not determined by coverage alone: identical calibrated thresholds can yield markedly different operational profiles depending on score geometry. We develop tools for operational certification and planning beyond coverage for split conformal prediction. First, Small-Sample Beta Correction (SSBC) inverts the exact finite-sample rank/Beta law to map a user request $(α^\star,δ)$ to a concrete calibration grid point with PAC-style semantics, yielding explicit finite-window coverage guarantees for a reused deployed rule. Second, because no distribution-free pivot exists beyond coverage, we propose Calibrate-and-Audit: an independent audit set supports certified finite-window predictive envelopes (Binomial/Beta-Binomial) for key operational quantities -- commitment frequency, deferral, and decisive error exposure -- and related metrics via linear projection, without committing to a scalar objective. Third, we give a geometric characterization of the feasibility constraints and regime boundaries induced by a fixed conformal partition, clarifying why operational quantities are coupled and how calibration navigation trades them off. The result is an operational menu rthat traces attainable operational profiles (Pareto trade-offs) and attach finite-window uncertainty envelopes to each regime. We illustrate the approach on benchmark molecular toxicity and aqueous solubility datasets.

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