We present PACO, a tool for strategy synthesis and explanation for stochastic processes based on the BPMN 2.0 standard, extended with probabilistic splits and positive impact vectors associated with tasks. The formal semantics is provided by Synchronous Probabilistic Impactful Networks (SPIN), an enriched Petri Net model. At its core, PACO implements an on-the-fly strategy synthesis algorithm for the input SPIN. To improve transparency, PACO includes an explainer module that decomposes synthesized strategies into minimal decision trees attached to individual process choices. PACO is provided as a web-based app that supports design, synthesis, explanation, and interactive simulation of stochastic processes. To further enhance usability at design time, PACO integrates an LLM-assisted design component that helps users create and interpret processes from natural-language descriptions. Finally, tested over a synthetic dataset of processes of increasing complexity, PACO demonstrates that explainable, automated strategy synthesis is practical for realistic BPMN process models despite the inherent computational complexity of the problem.