The core automated lab reactor (ALR) module utilized in this study was the EasyMax TM 102. This hardware platform incorporates iControl software control with an integrated data environment, enabling the monitoring, recording, and controlling of various process parameters. By aggregating realtime data from the ALR (e.g., reactor temperature, stir rate, dosed solvent volume, etc) and the HeinSight2.0 model (e.g., reactor volume, turbidity, etc), comprehensive data streams are simultaneously generated for immediate decision-making (Figure ). Furthermore, the ALR's viewing window allows for simultaneous image capture, providing qualitative measurements that cross-validate the quantifiable trends being monitored. Within HeinSight2.0, CNN is employed for object boundary determination to enable classification and yield parameters such as homogeneity, solid formation, and the presence of solid residue above the liquid level. Subsequently, image analysis techniques are utilized to derive additional parameters from the identified bounding boxes and segments, including volume, color, and turbidity (refer to the SI for further details). By combining iControl and computer vision analysis, our approach offers a macroscopic complementary analysis to microscopic in situ PAT tools and mathematical models, thereby increasing data richness from each experiment. This integration of visual data reporting within iControl also represents a crucial step towards standardized data collection, sharing, and repositories, as demonstrated by the facile technology transfer between the Hein Lab and Pfizer (Figure ). Throughout this paper, multiple visual parameters were analyzed over time during each of our case studies. We have selected a single parameter depending on each specific application (the "primary parameter") and corroborated its results with other collected parameters ("secondary parameters"). Liquid level monitoring was used to monitor an evaporative solvent exchange distillation and an antisolvent crystallization, employing automated feedback to maintain constant reactor volumes. The importance of monitoring multiple parameters was then demonstrated by monitoring an evaporative crystallization. Automated feedback was again demonstrated by performing a cooling crystallization while monitoring the solution homogeneity to inform heating/cooling changes. Turbidity was used to measure solid ̶ liquid settling kinetics, while the presence of a precipitate ("solid") was used to determine the agitation speed necessary for uniform suspension of solid. Lastly, multi-phase liquid detection was demonstrated by measuring the settling time in liquid ̶ liquid extraction.