Vendor-reported figures — source: www.relativity.com
The general counsel of a Fortune 500 industrial company needed to reduce routine, manual privilege review work so the legal team could focus on higher-judgment questions. Privilege review consumed the most time in their e-discovery projects and carried significant risk—missing even a single privileged document could prove critical to a matter’s outcome.
The GC ran a proof of concept of Text IQ for Privilege in parallel with traditional human privilege review. The AI received the same inputs as contract attorney reviewers: 225,000 documents, a list of 297 known attorneys, the Am Law 200, and legal terms. Using unsupervised machine learning and socio-linguistic hypergraph technology, Text IQ scored documents for privilege indicia and produced natural-language reason reports explaining why each document was potentially privileged.
Within two and a half weeks, Text IQ identified 80,000 potentially privileged documents and marked 145,000 as nonprivileged, eliminating them from further review. The AI also found 66 attorneys missing from the known attorney list (41 in-house and 25 outside counsel across seven firms), including matches from first names only, non-attorney references, and German-language communications. Privilege review finished faster than human review, with documented accuracy of 99.9 percent—and the GC estimated hundreds of privileged documents would otherwise have been produced.
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AI Roadmap — Associates spending the week on doc review. Contracts taking two weeks to turn. Both have AI fixes. We'll show you which, in what order. 72 hours.