In this work we developed a hybrid "physics-informed" ML strategy to successfully adjust the pH of liquid formulations with citric acid/sodium hydroxide by incorporating the knowledge from the physical chemistry of the titrants. For example, we calculate and use the speciation state for the weak, polyprotic titrant to better predict how much acid/base to dose and additionally, the capacity of these titrants to generate a buffer system informed our development of a multi-stage titration algorithm. This algorithm generalises to other strongstrong or weak-strong titrant pairs. Secondly, we address the engineering challenge of high-throughput pH adjustment of potentially viscous formulations. In the spirit of the "Fourth Industrial Revolution" and digitalisation of formulations research and development (R&D), the pH adjustment would ideally be fully automated . A simple automated pH adjustment system was shown in , however, it cannot cope with viscous samples. Without sufficient mixing after addition of acid or base, pH will not reach its true equilibrium value and experimental measurements may be erroneous if recorded too soon. In addition, without the ability to clean a pH probe from viscous residues of the sample materials it would be impossible to automate a batch of titrations as manual cleaning would be required in between samples. To the best of our knowledge, there is no suitable automated titration system on the market and thus, we present and open-source such an autonomous platform suitable for viscous formulations.