Document review remains a leading eDiscovery challenge. Nextpoint shares six practical tips for managing volume, improving review consistency, reducing data, and building stronger case chronologies.
The argument for AI-powered document review is all but over argues John Tredennick. Large language models are smarter, faster, more consistent, and less expensive than human review teams. John made that case in an earlier article published by EDRM and JD Supra, Six Reasons Why Your Best Document Reviewer Isn't Human Anymore. Others across the industry have made these points as well.
But better results and real control over the review process don't come from using an AI model alone. They come from the methodology—how you build, test, and refine the review criteria the AI applies. Get the methodology wrong and you get fast, consistent, wrong answers. Get it right and one experienced review manager can run a review that used to require hundreds of contract attorneys, with better accuracy and complete oversight from start to finish.
In this article, John Tredennick describes Document-Driven Review, the methodology he and his team developed for ReviewPartner. He explains why most AI review platforms are still guessing, what Document-Driven Review does differently, and why it puts you back in control of the process.