Cleary Gottlieb has been advancing the development of in-house e-discovery capabilities in the United States and Europe for more than 15 years.
We use a high-tech approach to discovery, working with and developing artificial intelligence (AI) tools and processes for discovery matters. Our capabilities on this score go beyond simply deploying off-the-shelf technology. We work closely with top technology vendors to adapt their products and processes to meet our clients’ needs—whether those are quickly finding key evidence in a critical internal investigation, gaining litigation advantage over an adversary, designing a defensible investigation plan for regulators worldwide or reviewing contracts in an M&A matter.
We are also continuing to innovate with AI, including by adapting predictive processes, concept clustering, sentiment analysis and other advanced technologies to provide our clients with even higher-quality, more efficient service. Our team adds value through the full discovery process, from implementing defensible preservation and collection processes, to designing and negotiating e-discovery protocols, to litigating related disputes.
Our integrated global team includes more than 80 full-time discovery professionals, three review centers and data centers in the United States and Europe. We choose our lawyers and technologists carefully and have rigorous training processes. This allows us to staff all of our matters with top-notch, multilingual attorneys. Our team is also well-versed in the law of discovery, including cross-border issues and data privacy.
Successfully obtained broad court endorsement for the use of a predictive review process in Rio Tinto v. Vale, setting the standard for modern use of technology-assisted review in litigation.
Routinely use technology-assisted review to comply with in-depth merger investigations by the U.S. Department of Justice and the Federal Trade Commission.
Saved a client millions of dollars by working with a vendor to adapt a predictive process to identify privileged documents within a large dataset of responsive documents.
Worked with the Department of Justice to negotiate a novel process under which, after initial training of a predictive model was complete, we used human review to further refine production sets, thereby increasing the accuracy of the model and reducing the production of nonresponsive materials—at a much lower cost to our client than a fully human review.
Advised the internal counsel and compliance function for a large investment bank on development of in-house predictive analytics to identify potential future compliance issues.
Worked with a vendor to adjust its standard training model to reduce the size of training sets of documents, which helped the model reach stability faster and at lower expense to our clients.
Implemented continuous active learning workflows on existing keyword search review populations to decrease client costs and increase efficiency of review.
Created custom index of parsed client data to more effectively perform sentiment analysis on reactions to potential anticompetitive conduct by industry competitor.
Developed custom de-duplication solution and used advanced analytics to minimize EU-based data in scope for a U.S. merger investigation.
Analyzed several million documents to identify and develop processes to remedy data collection gaps resulting from corrupted date fields for an investment bank client and worked with client to develop and implement improved collection procedures.
Advise on data protection-compliant production strategies in cross-border cases with EU-based data.