Latent Lab reimagines how people interact with unstructured information by turning documents, images, and media into explorable semantic landscapes. Using multimodal AI embeddings and dimensionality-reduction techniques such as Parametric UMAP, the system maps conceptually related materials near one another so that patterns and relationships become visible rather than hidden in folders or search results. The interface supports zooming, filtering, and semantic querying across diverse collections—from personal archives to social-media histories—helping users see how ideas cluster, evolve, and connect. By emphasizing exploration over answer generation, Latent Lab restores human agency in an era of opaque algorithmic summaries. It serves as a foundation for applications, including Filescape (semantic file organization) and Algorithmic Mirror (reflective visualization of algorithmic profiling).