To address the challenges of multi-device testing on a single sample, the development of a robust data management is essential . This system must enable seamless integration and communication across various instruments and bridge physical and temporal gaps in experimental stages that often exist between disparate experimental phases . Such advancements are crucial in SDLs, where progress has been hindered by a lack of a standardized and user-friendly software with intuitive operational guidelines . Efficient data management and experimental planning enables rapid processing and interpretation of high dimensional data, which unravel physio-chemical relations that surpass the analytical capacity of conventional approaches . It also facilitates the incorporation of AI agents and Deep Learning (DL) tools, which can further accelerate exploration in chemical spaces and reduce recourse allocation . This approach is in line with significant scientific efforts, as demonstrated by Pascazio et al. in chemistry and in other domain , which emphasize the importance of consistent data quality protocols and data acquisition methods, in compliance with FAIR data principles .