Hosted Review & Analytics

Make connections faster.

relativity_logoWith massive scalability options and custom configurations for in-house teams or remote collaboration, Relativity Assisted Review & Text Analytics help case teams easily manage large-scale data collections with maximum complexity in record time. Add our industry-leading hosting packages to the mix, and you’ve got the perfect combination of high-level security, efficiency, ease-of-use and flexibility.

Relativity Analytics

Relativity Analytics improves review speed by quickly identifying key issues and organizing documents based on concepts. Users can find and group similar documents, train the system on key issues, automatically identify and assemble email conversations, and run advanced keyword analysis.

Best in Service Relativity hosting partnerOrganizing your collection by similarity increases your doc-to-doc review speed without changing your existing review workflow. Your review teams can continue to code documents the same way they always have, while increasing speed and improving accuracy. Clustering similar documents together requires no user input, as Relativity’s text analytics search engine does the work.

Relativity Review

Relativity Assisted Review is the flexible and transparent computer-assisted review workflow also known as predictive coding or technology-assisted review. By amplifying human expertise with powerful technology, Assisted Review serves as a better way to review large volumes of electronic information, reduces risk and cost, and is more accurate than traditional review methods.

Using Relativity Assisted Review, you can train Relativity on relevance and key issues by coding statistically sampled subsets of documents. Because every case is different, you can choose the workflow that fits your unique needs, such as using a control set to track your progress or starting a project with previously coded documents.

Relativity’s text analytics engine suggests coding decisions for the remaining documents in the case, based on the small subset of examples. You can then prioritize the relevant documents and repeat the process with a more focused sample set for increased accuracy. Results are validated by statistical analysis.

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