Previously, our group has focused on bringing together flexible hardware, data-rich monitoring techniques, and CV for liquid-level monitoring and control. The resulting system was aptly named HeinSight, reflecting its ability to provide valuable visual insights into the ongoing experiments. As part of our continuous pursuit of advancing automated process development, we now introduce HeinSight2.0 -a generalized vision system that combines image analysis and CNNs for realtime reaction monitoring and process control. Operating in tandem with a flexible hardware platform, our system can simultaneously monitor multiple essential physical parameters crucial to process development (Figure ), while also being capable of real-time responses, if required. These fundamental vision-based parameters hold universal relevance across various chemical experiments, making our system adaptable to a wide range of tasks. The strength of our model lies in its flexibility, achieved through the concurrent monitoring of all parameters, with a sub-selection being subjected to analysis based on the experimental requirements. This approach enables the utilization of a single model for diverse experiments, with the results further bolstered by crossvalidation using secondary data, thereby providing deeper experimental insights.