Cloud-based enterprise search services (e.g., AWS Kendra) have been\nentrancing big data owners by offering convenient and real-time search\nsolutions to them. However, the problem is that individuals and organizations\npossessing confidential big data are hesitant to embrace such services due to\nvalid data privacy concerns. In addition, to offer an intelligent search, these\nservices access the user search history that further jeopardizes his/her\nprivacy. To overcome the privacy problem, the main idea of this research is to\nseparate the intelligence aspect of the search from its pattern matching\naspect. According to this idea, the search intelligence is provided by an\non-premises edge tier and the shared cloud tier only serves as an exhaustive\npattern matching search utility. We propose Smartness At Edge (SAED mechanism\nthat offers intelligence in the form of semantic and personalized search at the\nedge tier while maintaining privacy of the search on the cloud tier. At the\nedge tier, SAED uses a knowledge-based lexical database to expand the query and\ncover its semantics. SAED personalizes the search via an RNN model that can\nlearn the user interest. A word embedding model is used to retrieve documents\nbased on their semantic relevance to the search query. SAED is generic and can\nbe plugged into existing enterprise search systems and enable them to offer\nintelligent and privacy-preserving search without enforcing any change on them.\nEvaluation results on two enterprise search systems under real settings and\nverified by human users demonstrate that SAED can improve the relevancy of the\nretrieved results by on average 24% for plain-text and 75% for encrypted\ngeneric datasets.\n