Converting segmentations made from volumetric image stacks (e.g., CT, MR) into high-quality, all-hexahedral finite element meshes remains a significant bottleneck in computational workflows. Existing tools are often closed-source, prohibitively slow, poorly documented, or lack the flexibility required for seamless integration into automated pipelines. To bridge this gap, we present automesh, an open-source mesh generation application developed in Rust. By leveraging the performance and safety of Rust, automesh provides a robust, scriptable command-line interface designed for end-to end automation. The software transitions from raw segmentation to smooth surface reconstruction (STL) using advanced Laplace, Taubin, and hierarchical smoothing algorithms. These surfaces guide the generation of adaptive, conformal, all-hexahedral meshes. Additional features include segmentation-based material removal and automated defeaturing. Ongoing evaluation demonstrates that automesh achieves significantly faster times-to-solution and greater robustness across diverse use cases compared to traditional methods. This presentation will detail the underlying algorithms, the software architecture, and practical demonstrations of the tool in action.