This talk will introduce the AIASSE software, a hybrid methodology that combines empirical potential-based Reverse Monte Carlo (RMC) refinement of diffraction data with first-principles Density Functional Theory (DFT)-based molecular dynamics simulations. This integrated approach offers a self-consistent description of disordered materials (fluids, amorphous solids) ranging from macroscopic bulk structures probed by diffraction experiments to the atomic and electronic scales. AIASSE can simultaneously refine bulk structural data and correct molecular-level parameters such as bond lengths, bond angles, and electrostatic charges, while providing accurate electronic property predictions. The approach will be demonstrated using a variety of systems, including dense fluid krypton, amorphous ices, SiO2 glass, and water-methanol mixtures. By addressing the limitations of traditional structure determination methods, AIASSE enables a more precise understanding of structure-property relationships, providing valuable insights into material behaviors under varying conditions like temperature, pressure and chemical composition. To aid in material characterisation, AIASSE includes built-in automated methods for determination of nearest neighbours, solid-like classification of local atomic environments, 2-body excess entropy, entropy of mixing (for multi-component systems) and aqueous H-bond network analysis.