We present an ultrafast neural network (NN) turbulent tokamak transport model, QLKNN, for heat and particle ﬂuxes. QLKNN is a surrogate model based on a database of 3 · 108 ﬂux calculations of the quasilinear gyrokinetic trans- port model QuaLiKiz. To ensure accurate reproduction of the underlying model, we include known features of the physical system by choosing speciﬁc training targets and using a customized cost function in our training pipeline. We have coupled QLKNN to the tokamak modelling framework JINTRAC and rapid control-oriented tokamak trans- port solver RAPTOR. We demonstrate and validate the coupled frameworks through application to three JET shots covering a representative spread of H-mode operating space, predicting turbulent transport in the plasma core region (0.2 < ρN,tor < 0.85). QLKNN is able to accurately reproduce QuaLiKiz-predicted kinetic proﬁles (Ti,e and ne ) but or- ders of magnitude faster, from 7 days on 16 cores (JINTRAC-QuaLiKiz) to 20 minutes on 2 cores (JINTRAC-QLKNN) and 90 seconds on 1 core (RAPTOR-QLKNN). The discrepancy between QLKNN and QuaLiKiz is only on the or- der 1%-10% in rotationless cases. The impact of rotation on turbulent ﬂuxes is included in QLKNN through a new ﬂux scaling rule in postprocessing, based on a set of linear gyrokinetic simulations. This difference from the native QuaLiKiz rotation rule results in slightly larger (3%-15%) differences in the ﬁnal kinetic proﬁles for cases including rotation. Dynamic behaviour is also well captured by QLKNN, with differences of only 4%-10% compared to full Qua- LiKiz observed at mid-radius, for a study of density buildup following the LH transition. Deployment of neural network surrogate models in multi-physics integrated tokamak modelling is a promising route towards enabling accurate and fast tokamak scenario optimization, Uncertainty Quantiﬁcation, and control-oriented applications.