  Identifying the flavour of neutral $B$ mesons production is one of the most
important components needed in the study of time-dependent $CP$ violation. The
harsh environment of the Large Hadron Collider makes it particularly hard to
succeed in this task. We present an inclusive flavour-tagging algorithm as an
upgrade of the algorithms currently used by the LHCb experiment. Specifically,
a probabilistic model which efficiently combines information from reconstructed
vertices and tracks using machine learning is proposed. The algorithm does not
use information about underlying physics process. It reduces the dependence on
the performance of lower level identification capacities and thus increases the
overall performance. The proposed inclusive flavour-tagging algorithm is
applicable to tag the flavour of $B$ mesons in any proton-proton experiment.
