RoboChem is controlled by custom Python code and uses open-source libraries (Figure ) with offthe-shelf instruments and devices. By coupling a liquid handler, syringe pumps, switching valves, a high power photoreactor as well as simple Internet of Things (IoT) devices such as phase sensors and ultrasonic detectors with an in-line 60 MHz NMR for data-rich optimization, we have come up with a workflow to easily and efficiently optimize and intensify photochemical processes (Figure ). Each generated 'reaction slug' (650 μL) represents a discrete set of reaction conditions, and the reactions are executed sequentially: sample preparation, followed by reaction under the specified conditions, and finally, automated analysis and processing. The use of NMR for data analysis allowed for the accurate gathering of yields without the need to first calibrate the analytics with a pure product, and allowed us to run most reactions with no internal standard. While a 60 MHz benchtop NMR was selected as the analytical technique, the platform is easily adaptable to accommodate other analytical techniques such as IR spectroscopy, HPLC-UV-Vis, HPLC-MS, or GC-MS. As the volume of the reaction slug is determined by the analytical method, it could be significantly reduced by an order of magnitude if an alternative technique like HPLC were utilized. The RoboChem platform operates in a closed-loop manner, driven by the Bayesian Optimization (BO) algorithm. This iterative process involves the BO algorithm proposing experiments, which are automatically executed and analyzed. The obtained results are then fed back into the BO algorithm, which generates a new set of conditions for further optimization. By harnessing the capabilities of a photo-microreactor equipped with high-intensity LEDs, known for scaling up photochemistry to productivities exceeding 2 kg/day, and its ability to make instantaneous computer-controlled power output adjustments, we can minimize reaction times and significantly enhance the throughput of the platform. This increased efficiency reduces the time required for a comprehensive optimization run.