Today's eDiscovery AI Brief
Eleven sources scanned every morning. Scored out of 100: Recency 50, Impact 15, Source 10, and Topic fit 25, with noise penalties for vendor or event-heavy items.
Discernis Discovery publishes performance three different ways and throughput in three separate statements, and the figures leading the marketing are the ones that tell a buyer least. The court order the company markets around asks for something its public pages do not show: the contract.
This week’s kitchen sink for August 28, 2026 (with meme from Gates Dogfish) discusses AI as evidence, draconian rules for California lawyers & more!
A federal court addressed a proposed “three-strata validation” process for ESI discovery in In re Class Action Settlement Administration Litigation. The parties agreed to statistically valid sampling of documents coded nonresponsive, while the court rejected required re-review of responsive documents and post-review.
Working with clients to collect their own data ranks as the third most common firm-level challenge in Nextpoint's 2026 eDiscovery Landscape Survey, cited by 31% of respondents. Unlike document review or data mapping, ediscovery client collection isn't primarily a technical problem. It's a relationship one, shaped by how well a.
Three August rulings involved alleged judicial AI use and defective citations, and neither appellate opinion said AI caused the errors. California's SB 574, awaiting an Assembly floor vote by Aug. 31, would impose express duties on lawyers who use generative AI and require personal verification of citations in papers covered.
ILTACON 2026 concludes today! One more day of sessions & the Closing Reception. Here are Thursday happenings at ILTACON 2026!
Since U.S. employee exemptions under the California Privacy Rights Act (CPRA) expired—joining the long-standing enforcement of the EU/UK GDPR—enterprises have faced a surging operational challenge: the employee Data Subject Access Request (DSAR).... By: Exterro, Inc.
Matter planning
A focused planning board for scope, sampling, model controls, validation, and the people who need to explain the workflow later.
Jump into the same curated resource categories available in the full library: guides, case law, vendor documentation, research, and practitioner references.
A high-level primer for understanding what Technology-Assisted Review is, how the workflow moves from goals to validation, and how prediction scores and metrics should be interpreted before relying on TAR in a real matter.
6 resources→Use these after the primer when you want the primary overview materials behind the TAR workflow, including EDRM, FJC, and Grossman/Cormack resources.
4 resources→Neutral or semi-neutral references users can rely on when learning TAR or building defensible workflows.
4 resources→Workflow-focused resources for connecting AI assistants to eDiscovery context, MCP servers, and legal plugin patterns while keeping human review, read-only defaults, and non-legal-advice boundaries visible.
3 resources→Resources for teams using generative AI, analytics, classifiers, or AI-assisted review features in litigation workflows.
4 resources→Key decisions and legal themes users should understand before proposing or challenging TAR.
2 resources→Practical TAR explainers, workflow notes, and analysis that add context after the news brief and resource library.
Model access alone is not enough. MCP and legal plugins connect an AI assistant to the matter context, permissions, and workflows your team already relies on.
Read MoreMachine learning has quietly rewritten the rules of document review. Here's what every legal professional needs to know before their next large-scale matter.
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