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Taking as an example the generation of <jats:italic>W<\/jats:italic> + jet events produced in <jats:inline-formula><jats:alternatives><jats:tex-math>$$\\sqrt{s}=$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mrow>\n                    <mml:msqrt>\n                      <mml:mi>s<\/mml:mi>\n                    <\/mml:msqrt>\n                    <mml:mo>=<\/mml:mo>\n                  <\/mml:mrow>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula>\u00a013\u00a0TeV proton\u2013proton collisions, we train a neural network to model detector resolution effects as a transfer function acting on an analysis-specific set of relevant features, computed at generation level, i.e., in absence of detector effects. Based on this model, we propose a novel fast-simulation workflow that starts from a large amount of generator-level events to deliver large analysis-specific samples. 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