Molecular modeling tools are routinely utilized in computational chemistry and computational biology projects. The ongoing advancements in hardware and software have made modeling diverse chemical systems more accurate and computationally affordable. However, with many software tools available to perform multiple relevant tasks, selecting the best workflow can become daunting in itself. In one of our recent works, we developed a workflow to assign chemical structures to experimental ion mobility mass spectrometry collisional cross-section (CCS) values. This requires multiple steps, including protonation state assignment, relevant conformational search, and conformation similarity filtering, to deliver a manageable workload for downstream quantum mechanical (QM) calculations. To simplify running our workflow, we have developed an open-source, user-friendly Python application called PEAS (precise ensemble autonomous sampling) that effectively streamlines the result chain through vertical modeling engine integration to limit user intervention. Since the crucial steps prior to quantum mechanical processing in modeling are charge state determination and relevant conformation sampling, we have therefore incorporated SEER (charge state predictor), Confab (conformation generator), and CCS Focusing (conformer filtering) into the development of PEAS. These engines have been separately validated and confirmed for efficiency and acceptable accuracy, and thus, we report that their unified performance also delivers similar outcomes. Documentation for PEAS and its Google Colab executable platform is available at https://github.com/mitkeng/peas.