Equity valuation is often implemented as a deterministic pipeline: select point inputs, compute a discounted cash flow (DCF) number, and compare it to market price. This design hides uncertainty, makes model risk difficult to quantify, and encourages overconfident decisions under regime shifts. The point estimate is the problem; the distribution is the deliverable. This working paper specifies an alternative from first principles: a valuation pipeline that produces \emph{probability distributions} over enterprise value and connects those distributions to \emph{explicit decision policies}. Cash flows are modeled as strictly positive stochastic processes on their natural support, the perpetuity constraint $r>g_{\mathrm{term}}$ is enforced without distorting the dependence structure, growth and discount-rate components are coupled through a heavy-tailed copula, and a two-level Monte Carlo design separates path-level variability from parameter (model) risk. The evaluation target is neither a single accuracy number nor the unobservable intrinsic value itself: a \emph{convergence model} maps the valuation gap to realized excess returns, and the framework is falsified if the estimated convergence speed is indistinguishable from zero.