The accurate digital documentation of Cultural Heritage (CH) assets demands workflows capable of integrating heterogeneous, multiscale datasets while preserving both geometric fidelity and radiometric completeness. This paper presents the first results of the AI-based processing pipeline developed within the HERITALISE project (Horizon Europe, 2025–2028), applied to three multiscale case studies at the <em>Reggia di Venaria Reale</em> (Turin, Italy): an outdoor-indoor UAV photogrammetric survey, a kinematic SLAM acquisition of a contemporary sculpture garden, and a close-range dataset of an 18th-century decorative artefact. 3D Gaussian Splatting (3DGS) is evaluated as a novel view synthesis method across all three scenarios, demonstrating strong photorealistic rendering capabilities, particularly for complex material properties and geometrically challenging interiors, whilst highlighting current limitations for metric surveying applications. A two-stage crack detection workflow, combining tile-based text-prompted segmentation with SAM3 and multiview ray-based reprojection onto the reconstructed mesh, is validated on UAV imagery, achieving an 84.9% ray–mesh intersection rate. Finally, a standardised evaluation framework is proposed, encompassing adaptive, scale-dependent geometric and radiometric metrics organised into reference-based and no-reference assessment scenarios, aggregated into a transparent synthetic quality score with three adaptive quality classes. The proposed methodology contributes toward a reproducible, sensor-agnostic standard for the assessment of AI-generated CH documentation products.