Meet and Confer with Kelly Twigger

Kelly Twigger
  • 5.0 (8)
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Meet and Confer is the podcast for litigators, eDiscovery professionals, and anyone who knows that in a world of electronically stored information, discovery strategy isn’t optional—it’s essential. Hosted by attorney and discovery strategist Kelly Twigger, each episode offers clear, practical discussions on how to effectively leverage the power of ESI to craft successful discovery strategies for any type of litigation. Topics include, navigating evolving rules, understanding emerging case law, and making the strategic decisions that shape the outcome of a case. Whether you're a seasoned litigator, brand new associate, in-house counsel, or law student, Meet and Confer helps you think critically, stay prepared, and master your discovery strategy for modern litigation.

  1. Sep 23

    Why Signal Is the Most Dangerous Source of ESI You Need to Understand.

    Disappearing messages feel like a convenience until a court treats them as spoliation. We walk through the Delaware Court of Chancery’s May 27, 2026 ruling from the N-Ray World Wrestling Entertainment merger litigation, where key executives used Signal and repeatedly shortened auto-delete windows after receiving explicit litigation holds. The decision is a masterclass in how timing drives everything: when the duty to preserve arises, when a legal hold is issued, and how retention changes lined up with deal milestones in a way the court found impossible to ignore.  We unpack the Rule 37E framework step by step: duty to preserve, loss of ESI, reasonable steps, and prejudice. Then we get practical about the technology. Signal is not natively ephemeral, it becomes ephemeral only when users affirmatively turn on disappearing messages. That single detail changes the preservation story, the credibility analysis, and the risk profile for anyone advising clients who communicate on personal phones outside company IT control. We also explain why even excellent forensic work may prove when settings changed without being able to recover what the messages said once they are gone.  The biggest strategic insight is forum-specific. Delaware’s Court of Chancery Rule 37E expressly allows serious remedies on a showing of recklessness, while federal Rule 37(e)(2) typically requires intent to deprive. We compare how federal courts build intent through circumstantial patterns, drawing on Skanska, FTC v. Noland, and Hunter’s Capital, and we translate that into real guidance for your next preservation fight.  If you care about eDiscovery, legal holds, mobile device preservation, and spoliation sanctions, this is a must-listen. Subscribe, share this with a colleague who writes holds, and leave a review with the app or platform that gives you the most preservation anxiety. Thank you for tuning in to Meet and Confer with Kelly Twigger. If you found today’s discussion helpful, don’t forget to subscribe, rate, and leave a review wherever you get your podcasts. For more insights and resources on creating cost-effective discovery strategies leveraging ESI, visit Minerva26 and explore our practical tools, case law library, and on-demand education from the Academy.

  2. Sep 9

    When The AI Product Becomes The Evidence

    The evidence you need from an AI company might not be emails, chats, or a handful of prompt screenshots. It might be the machinery behind the product: the retrieval index, the operational database, and the logs that capture every interaction at scale. That shift changes what you ask for, how you scope it, and how you argue proportionality when the data volumes get huge.  We walk through Encyclopedia Britannica and Merriam-Webster v Perplexity AI, a Southern District of New York decision that reads like a plain English guide to retrieval augmented generation (RAG). We explain how an “answer machine” pulls content from a RAG database, hands it to a large language model, and generates outputs that can allegedly reproduce or paraphrase copyrighted works. We also break down Perplexity’s user activity log and why it can be the clearest record of what a user asked, what the system retrieved and ranked, what instructions went to the model, and what answer came back.  From there, we translate the holding into a practical discovery playbook: why copyright registration dates can define the right time window for technical discovery in a retrieval time case, how courts apply Rule 26 relevance and proportionality to limit massive productions, and how cost shifting can hinge on whether your cost numbers stay consistent and well supported. If you litigate, manage eDiscovery, or advise clients using AI tools, you’ll leave with sharper vocabulary and better requests. Subscribe, share the episode with a colleague, and leave a review if these case driven takeaways help you. Thank you for tuning in to Meet and Confer with Kelly Twigger. If you found today’s discussion helpful, don’t forget to subscribe, rate, and leave a review wherever you get your podcasts. For more insights and resources on creating cost-effective discovery strategies leveraging ESI, visit Minerva26 and explore our practical tools, case law library, and on-demand education from the Academy.

  3. Aug 26

    When Gen AI Outputs are the Evidence

    Your case doesn’t have “documents.” It has a database full of everything a system ever generated and no sane way to collect or review it all. That’s the discovery problem generative AI is creating, and it’s why the Disney v Midjourney orders are worth reading line by line if you draft ESI protocols, negotiate protective orders, or litigate proportionality fights around prompt logs and AI outputs. We break down a statistical sampling protocol built for prompts and outputs at massive scale, including why the parties land on 385 records per character bucket, what that margin of error really means, and where sampling can mislead you when the thing you’re measuring is rare or when damages depend on counts. We also dig into the practical drafting choices that make this workable: defining the population up front with character buckets, spelling out deduplication rules that preserve repeated prompts and multi-franchise overlap, and naming the exact metadata fields that tell the real story (from job IDs and parent job IDs to publication and moderation flags). The core takeaway is verifiability. Instead of asking the other side to “trust the sample,” the protocol uses a reproducible SHA-256 hashing method, requires a signed certification that the process was followed, and adds a verification list that discloses every eligible prompt ID and its hash so the other side can rerun the draw and confirm the denominator. We compare that approach to the OpenAI output log fights and explain why a smaller audited sample can be more valuable than a huge uncheckable production. Subscribe to Meet and Confer, share this with someone drafting a discovery protocol right now, and leave a review if you want more breakdowns of real court-approved language you can adapt. Thank you for tuning in to Meet and Confer with Kelly Twigger. If you found today’s discussion helpful, don’t forget to subscribe, rate, and leave a review wherever you get your podcasts. For more insights and resources on creating cost-effective discovery strategies leveraging ESI, visit Minerva26 and explore our practical tools, case law library, and on-demand education from the Academy.

  4. Aug 14

    Why Agreeing on Short Message Context Is Critical

    One disconnected Microsoft Teams message can look like evidence, but without the surrounding thread you can miss valuable context that can lead to additional evidence. We walk through Valcrum LLC v Dexter Axle Company LLC, a trademark and trade dress dispute that turns into a focused battle over Teams chat discovery, missing context, and what courts will actually order when keyword search fails on short form messaging. We trace the timeline from early requests through rolling productions where Teams content shows up as standalone messages, then a later production suddenly arrives with organized threads that reveal what everyone has been missing. From there, we unpack the procedural pressure points that can decide a motion before the judge ever reaches the merits: meet and confer certification requirements, building a real paper trail, and why a separate Rule 30(b)(6) motion gets denied for lack of good-faith conferral. On the substance, we dig into the court’s practical compromise. The judge refused to force a full reconstruction of all Teams chats into threads, leaning on proportionality and Sedona Principle 6, but still rejected the idea that keyword searches alone are “good enough” for Teams. The result is a context window of three days before and after disputed messages, plus an order requiring unredacted production of threads when objections are vague and unsupported. If you litigate ESI, negotiate an ESI protocol, or collect Teams and Slack data, you’ll leave with concrete language and strategy to use right away. Subscribe, share this with your litigation team, and leave a review with your biggest Teams discovery question. Thank you for tuning in to Meet and Confer with Kelly Twigger. If you found today’s discussion helpful, don’t forget to subscribe, rate, and leave a review wherever you get your podcasts. For more insights and resources on creating cost-effective discovery strategies leveraging ESI, visit Minerva26 and explore our practical tools, case law library, and on-demand education from the Academy.

  5. Aug 7

    You Can't Compel What You Didn't Negotiate: The Importance of AI Language in an ESI Protocol

    A party can run generative AI to decide what gets produced, and you still might not be able to “see inside” the process after the fact. That’s the gut punch and the practical lesson from Schulte v LinkedIn Corp. (N.D. Cal.), where plaintiffs tried to compel detailed transparency about LinkedIn’s Relativity AIR document review, including metrics like illusion estimates, document error rate, and how many humans validated the AI’s calls. We unpack why the court says no and how the interim ESI order does most of the work. The order was negotiated in January 2023, before generative AI review became a standard litigation flashpoint, and it only requires disclosure of technology assisted review. The court folds generative AI review into that TAR disclosure clause, treats the requested metrics as discovery on discovery, and applies a familiar rule: you do not get discovery about the other side’s search and review methods without a specific, non-speculative showing that the production itself is deficient. From there, we walk through the rest of the rulings that make this order so usable in day-to-day e-discovery strategy: keyword search term pre-culling before AI review, the proportionality math when you’re staring at multiple terabytes across custodians, the high bar for adding an in-house lawyer as a new custodian, and why text messages stay off the table when your ESI protocol carves them out absent good cause. The throughline is simple and sharp: what you can negotiate into an ESI protocol is far broader than what a judge will order on a motion to compel months later. If you’re drafting or revisiting an ESI protocol, this is your roadmap for generative AI, TAR, search terms, and messaging data. Subscribe to the Meet and Confer podcast, share the episode with a colleague negotiating a protocol, and leave a review with the one AI disclosure clause you would fight hardest for. Thank you for tuning in to Meet and Confer with Kelly Twigger. If you found today’s discussion helpful, don’t forget to subscribe, rate, and leave a review wherever you get your podcasts. For more insights and resources on creating cost-effective discovery strategies leveraging ESI, visit Minerva26 and explore our practical tools, case law library, and on-demand education from the Academy.

  6. Jul 15

    The First Stipulated ESI Protocol For Generative AI Review

    A new line just got crossed in e-discovery: a federal stipulated ESI protocol treats generative AI document review as its own governed process, and it requires disclosures many lawyers have spent years trying to protect. We walk through James v Cerebras Systems, where the parties agree to an AI-specific review appendix that demands documented workflows, human-confirmed privilege calls, audit trails, and prompt disclosure with rapid redline updates. We unpack how the protocol is built and why that structure matters: search terms, TAR, and “AI responsiveness review” each get their own rulebook, plus explicit controls for layering methods that can silently compound missed responsive documents. Then we dig into the details that will change meet and confer conversations, including 95% confidence level sampling with a plus or minus 2% margin of error, reporting of recall and precision, an “illusion” rate trigger, and required quality checks for hallucination, oversummarization, and misclassification. The bottom line is clear: AI doesn’t reduce counsel’s Rule 26G duty, it raises the bar for defensibility. We also connect the ESI protocol to the companion protective order, where AI use on protected material turns on contract terms in a data processing agreement, not hype about a brand name. We explain what it really means when an order references enterprise ChatGPT or Harvey, why consumer accounts typically fail the “no training” requirement, and how these rules can create new incentives to over-designate confidentiality. Subscribe to Meet and Confer for more case-driven discovery strategy, and if you found this useful, share the episode with a colleague and leave a review with your biggest takeaway. Thank you for tuning in to Meet and Confer with Kelly Twigger. If you found today’s discussion helpful, don’t forget to subscribe, rate, and leave a review wherever you get your podcasts. For more insights and resources on creating cost-effective discovery strategies leveraging ESI, visit Minerva26 and explore our practical tools, case law library, and on-demand education from the Academy.

  7. Jun 24

    Your Protective Order Wasn't Written for ChatGPT

    A Texas judge just gave litigators something they’ve been asking for and something they’ve been dreading: a clear ruling that litigation-related ChatGPT conversations can qualify as work product, paired with a warning flare about protective orders that never anticipated generative AI. We walk through Tate Group Automotive v Legacy Automotive Capital from the Business Court of Texas and explain why the analysis isn’t really about what the AI “did,” but who used the tool, in what role, and for what litigation purpose. We connect Tate to the growing body of AI discovery case law, including the federal decisions that treat AI as a tool rather than an automatic waiver event under Federal Rule 26(b)(3), and the outlier authority that opponents cite when they want to argue waiver. Then we show what changes when you’re in Texas, where Rule 192.5 protects work product prepared “by or for a party.” That single word expands protection and helps explain why Tate comes out the way it does, even though the user is a represented party acting on their own. The most practical part of the ruling isn’t the privilege win. It’s the court-ordered disclosure of what went into ChatGPT: an inventory of discovery materials shared with the tool, identified by Bates number where possible, including documents produced under a protective order. That tees up the uncomfortable question every litigator should raise at the next meet and confer: if you paste confidential discovery into a generative AI platform, did you “disclose” it to someone outside the permitted recipients list, or did you simply use software? We end with concrete next steps for e-discovery teams and trial counsel: audit your active protective orders, negotiate AI-specific language before the fight starts, document tool usage and data sources, and make sure you can account for inputs if a motion to compel lands. If you find this helpful, subscribe, share with a colleague, and leave a review so more lawyers can keep up with how fast AI discovery law is moving. Thank you for tuning in to Meet and Confer with Kelly Twigger. If you found today’s discussion helpful, don’t forget to subscribe, rate, and leave a review wherever you get your podcasts. For more insights and resources on creating cost-effective discovery strategies leveraging ESI, visit Minerva26 and explore our practical tools, case law library, and on-demand education from the Academy.

  8. Jun 18

    When Expert AI Prompts Become Evidence

    Your expert uses generative AI to cut millions of documents down to something a human can actually read. Then the other side asks the question everyone has been dancing around: are the AI prompts and outputs discoverable? I walk through Conservation Law Foundation v Shell Oil Company (D. Conn.), where a magistrate judge answers “yes” and frames prompts as expert methodology under Rule 26, not some brand-new category of evidence. The twist: the ruling comes as a text-only minute order on the docket, and it’s currently stayed while the district judge reviews a Rule 72 objection, so the doctrine is moving in real time. We get specific about what was used and why that matters. The expert team worked with GPT-4 models through Microsoft Azure OpenAI Service via the secure Azure API, an enterprise deployment with a very different risk profile than the consumer ChatGPT product. That technical choice intersects with discovery obligations, vendor retention questions, and preservation planning. If you are relying on “we don’t have it” defenses, we also talk about the court’s trust-but-verify approach and how sworn Rule 33 and Rule 34 responses can create Rule 37 exposure if anything later turns up in logs, metadata, or cloud systems. From there, I pull out the practical drafting and strategy lessons: why Rule 29 stipulations must name AI prompts, AI queries, and AI outputs explicitly; why relabeling prompts as “search terms” won’t hold when the technology is generative; and how a prompt produced cold can be a gift to cross-examination unless the expert report explains the full AI methodology. We also connect the through line to Florida’s amended Rule 2.515, which puts citation accuracy and verification squarely on the signer, with sanctions on the table. If you work with testifying experts, eDiscovery, or AI-assisted document review, this is the roadmap you want before the next motion to compel lands. Subscribe, share the episode with your team, and leave a review so more litigators can keep up with how AI discovery doctrine is being built. Thank you for tuning in to Meet and Confer with Kelly Twigger. If you found today’s discussion helpful, don’t forget to subscribe, rate, and leave a review wherever you get your podcasts. For more insights and resources on creating cost-effective discovery strategies leveraging ESI, visit Minerva26 and explore our practical tools, case law library, and on-demand education from the Academy.

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Meet and Confer is the podcast for litigators, eDiscovery professionals, and anyone who knows that in a world of electronically stored information, discovery strategy isn’t optional—it’s essential. Hosted by attorney and discovery strategist Kelly Twigger, each episode offers clear, practical discussions on how to effectively leverage the power of ESI to craft successful discovery strategies for any type of litigation. Topics include, navigating evolving rules, understanding emerging case law, and making the strategic decisions that shape the outcome of a case. Whether you're a seasoned litigator, brand new associate, in-house counsel, or law student, Meet and Confer helps you think critically, stay prepared, and master your discovery strategy for modern litigation.

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