When developers change source code, related artifacts like documentation and tests often become outdated, creating inconsistencies that are difficult to fix manually. We introduce ArtifactSync, a tool that leverages Large Language Models (LLMs) for automated cross-artifact consistency maintenance. LLMs are uniquely suited for this task because they can understand semantic relationships across diverse artifact types, such as code, documentation, and tests that traditional static analysis cannot capture. ArtifactSync performs change impact analysis using a hierarchical approach that starts by scanning file names and progressively “zooms in” on uncertain files, requesting more detailed context only when necessary. This targeted change impact analysis makes the evaluation of large repositories both feasible and cost-effective. ArtifactSync is implemented as both a command-line tool and an interactive VS Code extension. Within the editor, it presents each detected inconsistency with a clear explanation and an actionable fix that developers can apply directly. Our preliminary evaluation across two open-source repositories shows that ArtifactSync achieved up to 100% accuracy in both identifying impacted artifacts and generating corresponding fixes. A demonstration is available at https://youtu.be/AfLe_I_OnA0 and the tool at https://github.com/Alor-e/artifact-sync.