Semantic search represents a transformative advancement in search technology by focusing on understanding the intent behind user queries to retrieve the most relevant documents, rather than relying solely on keyword overlap. This technique utilizes vector spaces to map both documents and queries, enabling more accurate and useful search results, particularly in complex scenarios like legal document review. The introduction of the Transformer architecture in 2018 marked a significant improvement in search quality, allowing enterprises to achieve search capabilities comparable to large search engines like Google, even with minimal training data. This progress opens up substantial opportunities for industries such as finance and manufacturing that handle extensive internal documents, despite challenges in processing longer, multimodal, and semi-structured documents. Semantic search is anticipated to revolutionize information retrieval and enterprise search applications, enhancing efficiency and user experience by bridging the gap between user intent and data access without extensive training data or investment.