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AI and machine learning in eDiscovery refer to the use of trained algorithms to automate document classification, relevance prediction, privilege detection, and pattern recognition across large volumes of electronically stored information. Portable AI models extend that capability by allowing organizations to train a model in one matter or environment and redeploy it across different platforms, cases, or data sets without rebuilding from scratch each time. For organizations that litigate frequently or face recurring regulatory investigations, that is a meaningful shift in how review economics work.  How Portable AI Models Work  For most of eDiscovery's history, AI models were platform-bound. You

AI-powered document review in eDiscovery uses machine learning and natural language processing to analyze large volumes of electronically stored information, classify documents by relevance, privilege, and responsiveness, and surface the most significant content for human review. It reduces the time and cost of first-pass review dramatically while improving consistency across document sets that no human team could process at the same speed or scale.  That is the case for AI in eDiscovery in one paragraph. The fuller picture is more nuanced.  Document review has historically been the most expensive phase of litigation and regulatory response. In large matters, review costs routinely account