Sol-gel processes have been applied for the preparation of various high-performance and biocompatible solids ranging from ultra-light aerogels to sustained drug-release materials, metal oxide semiconductors, and tough ceramics. Using the sol-gel method to establish versatile functional modifications on various surfaces is furthermore a straight-forward and cost-efficient approach. To allow for operational flexibility, control and reproducibility of the process, knowledge about the reaction progress is crucial. This is however a non-trivial problem due to the competing underlying mechanisms of hydrolysis and polycondensation and the consequently resulting orthogonality of the reaction parameters. In our talk we will focus on spin-on-glass as an example for sol-gel materials and will discuss the selection of key reaction parameters, their translation into arguments experimentally executable by a synthesis robot, the analysis of the reaction progress by GPC, and how the workflow was established by hardware/software components. We will continue presenting how the sparse dataset – covering only 0.38 % of the experimental space resulting from the number of (semi-)continuous key reaction parameters and variability thereof (92,610 combinations) – was used for the development of ensemble models based on polynomial regression. In validation experiments the ensemble regressors correctly predicted the ranges for dispersity and molecular weight in about 70 % of the cases.