The Geek In Review

I sat down with Michael Anderson, Chief Product Officer at Filevine, at the LEX Summit in San Diego, where attendance topped 2,000 this year. I wanted to start with his "Back to Brains" article, which argues that lawyers spend too much time pointing, clicking, and typing to get software to do what they already know they want. Filevine announced the LOIS pen and notebook at the conference. It is a real pen with real ink on real paper, and a sensor sends the handwritten notes into the meeting transcript, so a litigator in a client meeting or a focus group can keep eye contact and still capture the thought. Michael calls the legal professional the hero of the story. That is also why LOIS has dictation and readout, since he says the best case-strategy ideas tend to show up when you're on the go.The law librarian in me had to ask about the citator. John Rizner demoed it for us earlier (Marlene and I assumed it would go over most people's heads, until two partners from my firm told me it was the greatest thing they had ever seen). Case results get a green check mark when the citation is verified, or a magnifying glass when something needs a second look. Claims about what an opinion says link to the passage that supports them. You can then highlight a specific holding and run what Michael called a semantic Shepardization of that text across the full corpus of U.S. case law. It uses machine learning to look for similar language, so it can surface cases that address the same point without citing the original, including sub silentio treatment. For people outside the profession, Michael uses Spotify to explain it. Most of us let it recommend songs based on what other listeners picked, and sometimes you want to ask for pop songs with a cello.We also talked about acquisitions. Filevine bought a deposition marketplace that schedules digital court reporters and paired it with its own live deposition feedback tool, which flags when a deponent says something inconsistent with earlier testimony or with a document in the case file. Pincites brings the work into Microsoft Word. Michael's reason litigators pick these tools up is that "intelligence is always competitive," and one fact your opposing counsel doesn't have gives you a chance to win. He also said something that matches what I see at my firm. Eight years ago he ran SaaS implementations where the main job was getting staff to use the system, and he has been surprised at how quickly attorneys have taken to AI by comparison. He credited Jack Newton with the line that LLMs are to attorneys what spreadsheets are to accountants.The headline announcement was voice agents that answer inbound calls to the firm. In the live demo on stage, the agent took a call, identified the caller, followed the firm's decision tree, and put the data into the intake system. Setup uses what most firms already have, such as the script you would hand a new front-desk hire or the configuration in a system like Lead Docket. When I asked about guardrails (I still think about the Air Canada chatbot), Michael said the agent works from the same process documents a new intake employee gets, and that employee doesn't have full case access either. He told a story about calling several law firms about a real estate matter and getting voicemail every time, with no callbacks. He also pointed to the California State Bar's Justice Gap Study, which shows how many Californians with legal problems never get the help they need, and he sees the after-hours, overflow, and voicemail calls as the place to start.Listen on mobile platforms:  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Apple Podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ |  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Spotify⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠Substack Email: [email protected]: Jerry David DeCicca 

Greg Lambert and Marlene Gebauer take The Geek in Review on the road to Atlanta for a conversation following the GDS AI Summit. Meeting with leaders from across industries offers a fresh perspective on familiar legal technology challenges: messy data, uncertain ownership, and the pressure to turn artificial intelligence experiments into measurable business improvements. Their shared takeaway offers some reassurance for legal innovation teams. Law firms face many of the same obstacles as their clients, and cross-industry conversations provide useful lessons for moving forward.Data quality anchors the discussion, with Greg offering a memorable description of law firm information: “Our data puddles are mud puddles.” Greg and Marlene examine structured data, unstructured data, and the less obvious problem of missing or unidentified information. Some organizational knowledge lives exclusively in the heads of experienced professionals, creating continuity risks when those people leave. The conversation also explores how priorities differ across industries, from protecting confidential information in legal services to safeguarding reputation when AI-generated content reaches an audience.Hidden workarounds emerge as another barrier to meaningful process improvement. Drawing on a conference discussion about nurses solving problems outside formal reporting channels, the hosts consider similar habits among legal assistants, paralegals, attorneys, and technology teams. When employees quietly resolve recurring problems, leadership receives an incomplete picture of daily operations. Over time, the workaround becomes the process. Introducing AI without understanding those informal practices risks embedding existing problems into a new system.Greg and Marlene also discuss the discipline required to run useful AI pilots. Their breakout group narrowed a broad data challenge into a more focused project around client data intake, ownership, and tracking. The exercise prompted questions about pilot duration, accountability, and measurement, including the risk of an extended experiment becoming a permanent process without a deliberate decision. Adoption numbers offer only part of the picture. Understanding whether people use a tool effectively requires clearer objectives, meaningful metrics, and responsibility for gathering evidence.The episode closes with a larger strategic question: Should AI adapt to existing business operations, or should organizations rethink their operations around AI’s capabilities? Greg questions the value of adding AI to established products while preserving the same underlying approach, while Marlene distinguishes enterprise-wide integration from solutions serving specific legal workflows. Alongside reflections on their own conference presentation and an evening at the Margaret Mitchell House, the hosts bring the conversation back to practical priorities for law firm leaders: define the problem, understand the process, and evaluate results before expanding an AI initiative. Listen on mobile platforms: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Apple Podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Spotify⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠SubstackEmail: [email protected]: Jerry David DeCicca

This week we talk with Ross Guberman, founder and CEO of BriefCatch, and Heather Rodriguez, the company’s enterprise account executive. Ross built BriefCatch to put the lessons from his books and workshops in front of lawyers while they write. It started as a rules engine that flagged everything from typos to sentence flow. He admits he was never a coder, and says he would have looked for a technical co-founder if he had known the term existed. Today BriefCatch pairs that original engine with generative AI to review whole briefs, motions and judicial opinions for persuasiveness and consistency. The company has spent much of 2026 on checking how accurately briefs describe cases and statutes.That leads to RealityCheck, BriefCatch’s citation verification tool. Heather explains that it checks whether the cited opinion supports the proposition, pulls up the relevant language and flags potential problems. An experienced lawyer can disagree with its reading, and the lawyer still owns the argument. Ross adds that running RealityCheck on briefs from the 1990s through the 2010s turns up plenty of case law errors, so the problem predates generative AI. The talk then turns to how alike AI prose sounds, and Marlene warns she may lose her mind if she reads “it hits differently” one more time. Heather describes how BriefCatch offers choices the attorney can accept or reject. Ross gives the example of lawyers who use “indicate” for everything; the tool suggests more precise verbs such as specify, convey or reflect. On Anthropic’s plan to watermark text written by Claude, Ross praises the accuracy of the AI detector Pangram. He hopes the profession will start asking a better question about AI-assisted work: was it a good brief?Heather then describes where enterprise adoption stands. The Legal Technology Hub map has grown so crowded you need a magnifying glass to read it. Firms now ask whether a product is the best fit for a specific use case and whether it can replace tools they already own. Workflow fit, ROI and security reviews dominate the conversation, and she has seen firms that love a product spend months in security and procurement. Ross credits his reputation in legal writing with helping BriefCatch earn trust, though the company still answers the same questionnaires as every vendor. He walks through the WordRake acquisition, closed in January, which brought Gary Kinder’s conciseness and plain language technology and a new customer base to BriefCatch, and he hints that more acquisitions may follow. BriefCatch is also expanding past its Word plug-in into web apps, Outlook, an admin platform and PDF reports for courts, while Ross notes that most lawyers still do their real work in Word. When Greg asks about an MCP integration with Claude or OpenAI, Ross says one could arrive before the end of the year.Marlene asks what is true today that wasn’t a year ago. Heather says AI has become a normal part of legal work, and hands-on experience has taught lawyers what the tools do well and where they fall short. Ross calls the weekly stream of new possibilities energizing and overwhelming, for founders and customers alike. Looking ahead, Heather expects firms to build their own AI ecosystems and pick the right tool for each stage of the work. Ross predicts that courts, which are quietly experimenting with generative AI, will use it far more within a year. Once courts signal their approval, he expects lawyers to stop debating whether to use these tools and start choosing which ones. Marlene drops off near the end with technical issues, so Greg wraps up the conversation with both guests.Transcript

Law firms and legal departments face pressure to adopt AI, yet buying another tool does not answer the hardest question: Which problem needs solving? Dr. Maryam Salehijam, founder and CEO of Andarzi, joins Greg Lambert and Marlene Gebauer to discuss how legal teams choose between existing systems, new vendors, and tools built for their own workflows. Her starting point is to observe the work, identify a useful outcome, and then decide what technology belongs in the process.Salehijam describes legal teams paying for AI platforms while struggling to get consistent use from them. She urges organizations to examine tools they already own and ask vendors for more training before adding another subscription. A legal department’s use of ServiceNow for intake provides one example. The conversation also tackles shifting model capabilities, consumption pricing, and the cost of assigning an expensive model to a routine task.What counts as a return on legal AI investment? Time and money matter, but Salehijam argues for measuring employee satisfaction as well. Repetitive contract review and document work consume attention lawyers would prefer to spend on judgment, business problems, and client relationships. She describes using anonymous feedback before and after a workflow change to learn whether people’s work has improved, while Greg raises the tension between hours saved and the billable hour.The discussion turns to local AI models, data security, and the growing interest in AI-native law firms. Salehijam argues for decisions based on the task and the client’s requirements, including a direct conversation with clients about how their information should be handled. Greg presses her on whether smaller firms and large firms face different constraints. Marlene asks whether the emerging “legal engineer” role will endure, prompting a pointed exchange about technical skill, credentials, and what lawyers need to learn.For teams putting AI into practice, Salehijam favors hands-on learning with peers, repeated use, and work product over a quick certificate. She also warns leaders to account for the time and effort a new workflow demands before promising immediate gains. In the closing questions, she reflects on a legal AI market with fewer new entrants than she expected and predicts more law firms and legal departments will ask whether owning a workflow serves them better than renewing another set of software seats.LINKSAndarziDr. Maryam Salehijam on LinkedInAI Fluency for Legal Professionals on MavenHarveyLegoraServiceNow Legal Service DeliveryNotionAstra for LawListen on mobile platforms:  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Apple Podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ |  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Spotify⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠SubstackEmail: [email protected]: Jerry David DeCicca

Paul Lee, co-founder and CEO of Patlytics, joins Greg Lambert to explain how an AI platform built specifically for intellectual property work is changing the patent lifecycle. Patlytics supports workflows spanning patent drafting, prior art analysis, office action responses, portfolio management, litigation readiness, and claim-chart preparation. Lee reports that the company now works with roughly 55 percent of the Am Law 100 and hundreds of corporations across technology, biotechnology, pharmaceuticals, and other patent-intensive industries.Lee traces Patlytics’ origins to his experience as a venture capitalist and more than 100 conversations with patent attorneys. Those interviews exposed a practice filled with expensive, labor-intensive processes, from drafting detailed patent specifications to constructing claim charts for litigation. His interest also grew from the Apple and Samsung patent battles, the IP expenses faced by venture-backed companies, and conversations with Patlytics co-founder Arthur Jen and former Latham & Watkins patent litigator Bob Steinberg.The conversation turns to Patlytics’ work involving USPTO patent examiners and the broader effect of placing AI on both sides of the examination process. While confidentiality limits the details Lee discusses, he identifies quality and the examination backlog as two areas where specialized technology offers meaningful assistance. He also contrasts Patlytics with broad legal AI platforms such as Harvey and Legora, arguing that patent professionals need tools designed for the precision, technical detail, and specialized workflows of IP practice.Human judgment stays central to Lee’s vision. Patent attorneys still own the work product, approve key decisions, and remain responsible when an AI-generated analysis falls short. At the same time, client expectations continue to rise. Clients want faster work, higher quality, and lower costs, while law firms need sustainable margins. Lee sees flat-fee arrangements and more predictable workflows as one route toward sharing the “AI dividend” between clients and their outside counsel. In-house teams also gain more capacity for infringement analysis, patent-portfolio reviews during M&A, cross-licensing strategy, and litigation preparation.Looking ahead, Lee describes a striking change in attitude among patent professionals, from widespread skepticism a year ago to broad optimism today. His crystal-ball concern is less about whether lawyers will adopt AI and more about whether its economics will hold together. As free experimentation gives way to consumption-based pricing, firms will need to measure the value of each workflow and avoid spending $50,000 in AI costs on a $5,000 matter. Token maxing had its moment. ROI gets the next meeting invitation.Listen on mobile platforms:  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Apple Podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ |  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Spotify⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠Substack⁠[Special Thanks to ⁠⁠Legal Technology Hub⁠⁠ for their sponsoring this episode.]Email: [email protected]: Jerry David DeCicca Transcript