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"Generative AI for Document Review: A Case Study on the Future of eDiscovery"

Document review remains the most expensive phase of eDiscovery. As data volumes surge, legal teams face mounting pressure to review faster, reduce spend and maintain defensible accuracy. This case study explores how generative AI can help legal professionals handle large document sets with improved speed, consistency, and measurable precision.

When it comes to eDiscovery, document review accounts for roughly 73% of every dollar spent. In today’s big data era, the volume and diversity of information continues to grow, driving up costs and increasing the risk of human error caused by fatigue and inconsistent interpretations. Legal professionals are under pressure to review faster, control spend, and maintain defensible accuracy without sacrificing quality. 

Generative AI offers solutions enabling teams to analyze massive datasets faster, more accurately, and at a fraction of the historical cost. Unlike earlier tools that rely heavily on pattern recognition, large language models can understand context, interpret nuanced language, and generate detailed classification results. At the same time, responsible implementation remains essential, with careful attention to security, confidentiality, and defensible validation. 

Download this case study to learn: 

• How generative AI can classify large volumes of documents with high recall and precision 
• What a real-world responsiveness review comparison revealed about speed and accuracy 
• How prompt refinement and human validation improve defensibility 
• Practical ways to manage costs while leveraging advanced review technology 
• Key considerations around security, confidentiality and verification workflows 

Download the guide to explore how generative AI can support faster, more defensible document review.

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