The advent of high-throughput sequencing technologies and the availability of biological "big data" has accelerated the discovery of new protein sequences, making it challenging to keep pace with their functional annotation. To address this annotation challenge, techniques such as Sequence Similarity Networks (SSNs) have been employed to visually group proteins for faster identification. In this paper, we present an alternative visual analysis tool that uses Protein Language Model (PLM) embeddings. Our PLVis pipeline employs dimensionality reduction algorithms to cluster similar sequences, enabling rapid assessment of proteins based on their neighbors. Through analysis using average Jaccard distance and cosine similarity metrics, we found that well-separated clusters (those with silhouette scores above 0.95) captured high-dimensional information better than other regions of the projection. While proteins in poorly defined "fuzzy" regions showed similar embeddings to those in neighboring clusters, we note that distances in these projections should not be directly interpreted. To make this pipeline accessible to a wider research community, we have created a Google Colab Notebook for the comparison of protein datasets.