Modern generative models often struggle to synthesize structured objects from detailed part specifications. They frequently produce anatomically implausible outputs or hallucinated components. We introduce PLATO, a novel two-stage framework that bridges this gap by enabling precise, part-controlled object generation. The first stage is PLayGen, our novel part layout generator which takes a list of parts and object category as input and synthesizes high-fidelity layouts of part bounding boxes. To enhance PLayGen's ability to learn inter-part relationships, we introduce novel structure-based loss functions. In the second stage, PLayGen's synthesized layout is used to condition a custom-tuned ControlNet-style adapter, enforcing spatial and connectivity constraints. This results in anatomically consistent, high-fidelity object generations containing precisely the user-specified parts. We further propose new part-level evaluation metrics to rigorously quantify adherence to part specifications. Extensive experiments show that PLATO significantly outperforms state-of-the-art generative models and produces structurally coherent objects in a controllable manner - marking a step forward in modular, part-driven asset generation.