Site characterization relies on both in-situ and laboratory testing. In the early stages of a project, in-situ tests are often performed before launching a full laboratory testing program. At this stage—when soil data is limited—in-situ tests can provide valuable insights for preliminary characterization. To enhance the interpretation of these tests, an automated parameter determination framework has been developed, employing a graph-based approach to derive soil and constitutive model parameters from in-situ measurements. Several studies have been conducted to validate the framework’s output in terms of both soil properties and model parameters. The framework is designed to be transparent and adaptable, allowing users to trace the computed values for different parameters and incorporate their experience, knowledge and expertise. In this study, the tool was applied to a well-documented test site in Australia. Additionally, the integration of machine learning models for predicting soil parameters is explored as part of ongoing efforts to incorporate data-driven techniques into the framework.