Let $μ$ = ($μ$t)t$\in$R be any 1-parameter family of probability measures on R. Its quantile process (Gt)t$\in$R : ]0, 1[ $\rightarrow$ RR, given by Gt($α$) = inf{x $\in$ R : $μ$t(]--$\infty$, x]) > $α$}, is not Markov in general. We modify it to build the Markov process we call "Markov-quantile".We first describe the discrete analogue: if ($μ$n)n$\in$Z is a family of probability measures on R, a Markov process Y = (Yn)n$\in$Z such that Law(Yn) = $μ$n is given by the data of its couplings from n to n + 1, i.e. Law((Yn, Yn+1)), and the process Y is the inhomogeneous Markov chain having those couplings as transitions. Therefore, there is a canonical Markov process with marginals $μ$n and as similar as possible to the quantile process: the chain whose transitions are the quantile couplings. We show that an analogous process exists for a continuous parameter t: there is a unique Markov process X with the measures $μ$t as marginals, and being a limit for the finite dimensional topology of quantile processes where the past is made independent of the future at finitely many times (many non-Markovian limits exist in general). The striking fact is that the construction requires no regularity for the family $μ$. We rely on order arguments, which seems to be completely new for the purpose.We also prove new results the Markov-quantile process yields in two contemporary frameworks:-- In case $μ$ is increasing for the stochastic order, X has increasing trajectories. This is an analogue of a result of Kellerer dealing with the convex order, peacocks and martingales. Modifiying Kellerer's proof, we also prove simultaneously his result and ours in this case.-- If $μ$ is absolutely continuous in Wasserstein space P2(R) then X is solution of a Benamou--Brenier transport problem with marginals $μ$t. Itprovides a Markov probabilistic representation of the continuity equation, unique in a certain sense.