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Using Relativity Assisted Review, you can train Relativity on relevance and key issues by coding statistically sampled subsets of 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. Overall, review time and costs decrease rapidly when you use Assisted Review to amplify the efforts of your review team.
Through text analytics search technology, Relativity can automatically cluster similar documents together. Organizing 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.
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