Editorial
Practitioner-level writing on Technology Assisted Review, privilege classification, FRCP Rule 26, and the operational realities of discovery. Written for e-discovery counsel, litigation support directors, and in-house legal teams — not for search engines.
The distinction between simple passive learning and continuous active learning in Technology Assisted Review is not academic — it determines how your review performs on complex multi-custodian matters.
Automated privilege detection tools are increasingly accepted in federal practice — but defensibility requires understanding what courts have actually scrutinized and what your protocol documentation needs to cover.
In-house legal teams managing government investigations or employment litigation face a calculation most BigLaw partners never see: how much of the outside-counsel review budget can be internalized with the right tools.
Federal Rule 26 imposes cooperation and disclosure obligations on discovery process decisions. What this means in practice for litigation teams using predictive coding.
The quality of your initial seed set — the documents attorney reviewers tag before the model begins active learning — is the most underappreciated variable in predictive coding performance.
SEC, DOJ, and FTC investigations arrive with tight response timelines and high privilege risk. In-house legal teams with the right review workflow can manage the initial privilege cut before outside counsel engagement.
A privilege log is not just a list. Courts scrutinize whether each assertion is supported by document-level facts. Here's what field-level documentation courts have found sufficient — and what they've rejected.
Chicago was where predictive coding was operationalized at scale — kCura built Relativity here not by accident. The city's proximity to federal courts, mid-market corporate legal departments, and litigation support vendors creates an innovation context that coastal observers underestimate.