In many applications of (entropic) optimal transport, the transportation cost is unknown and cannot be specified a priori. In this talk, we present a strategy to address this challenge in a particular setting. We aim to transport a distribution (\mu) to (\nu), and we assume access to samples from a coupling between (\mu') and (\nu') that is optimal for the same unknown cost. Leveraging flow matching and structural properties of entropic optimal transport, we develop an algorithm that transfers this information to learn the corresponding Schrödinger bridge between (\mu) and (\nu).