Retrieving information from point clouds for analysis and visualization has gained ever-increasing interest. A growing niche in this regard is ray queries, commonly used for image synthesis. Ray tracing is widely used in computer graphics, with a multitude of solutions based on bounding volume hierarchies. However, these solutions are rarely straightforward to integrate with raw point cloud data and geospatial analytical workflows. To overcome this, we present a novel approach to ray tracing in raw point clouds that builds upon and extends existing geospatial indices. The solution is exemplified by a fast octree implementation that supports versatile query semantics, such as neighborhood queries with constraints on k and radius for both points and rays, while offering configurable data organization schemes, including layered, fixed, and adaptive depth. The evaluation demonstrates satisfactory speed and capabilities for many scientific use cases, while simultaneously exhibiting low implementation costs, high flexibility, and simplicity in integrating ray tracing into analytical point cloud workflows.