We present the NeuroBulla Pipeline Bodies (NBPB) framework, a novel modular deep learning architecture inspired by biological organisms. NBPB decomposes model functionality into five specialized Organs (ImmunoGuard, Watcher, LossSmith, ReverseEngine, HormoneCtrl) coordinated by a central Nucleus. Each organ operates on distinct subtasks with its own local objectives while contributing to a global training objective, address ing limitations of monolithic models. We integrate the organs in an end-to-end train able PyTorch system, and we evaluate NBPB on benchmark tasks. Experiments show that NBPB achieves improved robustness and performance, demonstrating that organ structured modularity can yield more efficient and adaptable networks. The framework achieves 90% test accuracy on CIFAR-10 compared to 87% baseline, with significantly im proved adversarial robustness (68% vs 45%). Our implementation is available at https: //github.com/fra150/NeuroBulla-Pipeline-Bodies-NBPB-.git