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
For the better part of two years, law firms have been buying just about everything AI. Every vendor I talked to over that time expected renewals and expanded licenses at every firm. Every firm I spoke with said they were licensing widely and would cut back once they figured out what fit. Those two things
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 TGIR hosts, 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.
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Email: geekinreviewpodcast@gmail.com
Music: Jerry David DeCicca
What happened when I pointed Jev, SALI, and CLOC’s Core 12 at every post we’ve ever written
I read Nate Jones’s post on Jev, TypeSafe’s new “System One” model, and he described it as an LLM that can only talk in multiple choice. It reads what you give it, you hand it the possible
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.
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[Special Thanks to Legal Technology Hub for their sponsoring this episode.]
Email: geekinreviewpodcast@gmail.com
Music: Jerry David DeCicca
What happens when you separate elite legal talent from the traditional law firm structure? This week on The Geek in Review, we talk with Manuel Deó, co-founder and co-CEO of Ambar Partners, about a model designed around senior independent lawyers, flexible capacity, enterprise technology, and client choice. Deó explains why he and co-founder Rosa Espín did not set out to replace Big Law, but instead to address a gap between permanent in-house hiring and traditional outside counsel.
At the center of Ambar’s model is a simple idea: legal demand comes in different shapes, and the delivery model should match the problem. Deó describes how Ambar gives lawyers control over the clients, projects, fees, and schedules they take on, while giving clients greater visibility into cost and the individual lawyers doing the work. He also discusses Ambar’s recent Chambers recognition and argues that the term “alternative” is starting to lose some of its usefulness as clients grow more comfortable assembling legal services from a wider range of providers.
Deó walks through what Ambar calls its legal operating system, built around belonging, business, backbone, and badge. The model combines a professional community with business development, compliance, contracting, billing, technology, and institutional credibility for independent lawyers and specialist boutiques. Ambar’s public materials describe a shared technology environment that includes tools such as Harvey, Microsoft Copilot, Legora, and other legal technology products. The goal, according to Deó, is to give independent lawyers access to the infrastructure associated with a large firm without requiring them to give up professional independence.
The conversation also turns to AI, knowledge, and professional judgment. Deó argues that legal knowledge is becoming more abundant while judgment grows more valuable. He describes Ambar’s work on “expert twins,” where approved knowledge, prior work, playbooks, and experience associated with an individual lawyer form a trusted layer for AI-assisted work. That emphasis on institutional knowledge and permissions echoes a broader trend across legal AI, where vendors are increasingly focused on connecting AI systems to trusted internal work product and organizational context.
Finally, Deó offers a broader view of where legal delivery is heading. He sees legal departments assembling teams dynamically from in-house lawyers, traditional firms, independent specialists, boutiques, managed services, and AI agents based on the needs of a particular matter. Instead of asking which firm to hire, clients increasingly have reason to ask what combination of people, technology, expertise, and risk structure best fits the work. For Deó, the future belongs less to a single dominant delivery model and more to legal departments acting as orchestrators of capability.
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[Special Thanks to Legal Technology Hub for their sponsoring this episode.]
Email: geekinreviewpodcast@gmail.com
Music: Jerry David DeCicca
For millions of people, an everyday legal dispute never justifies the cost of a lawyer or a private mediator, no matter how much the outcome matters to them. Judge Victoria Wood saw that problem over and over during her years on the Napa County Superior Court bench. This week, Wood joins Judicaid Chief Strategy
What happens when a 40-year-old legal data company decides its employees should start building their own software? This week on we talk with Best Lawyers CEO Phillip Greer and Senior Vice President of Research and Product Strategy Elizabeth Petit about an internal AI transformation that reaches far beyond adding ChatGPT to the corporate toolkit. Best
In this episode of The Geek in Review, we talk with Patrick Forquer, Chief Revenue Officer at Legora, about legal AI’s move from experimentation into daily legal work. Forquer explains why Legora has invested heavily in legal engineers, lawyers with practice experience who work alongside clients on adoption, workflow design, prompt and context engineering, and change management. The conversation also explores an emerging career path for lawyers who pair substantive legal knowledge with AI fluency, especially as firms search for people able to translate practice needs into working systems.
Legora’s acquisition strategy provides another lens on the company’s ambitions. Forquer describes a strategy aimed at building breadth across legal work while adding depth in litigation, commercial real estate, regulatory monitoring, and legal research. Recent acquisitions such as Wexler, Cadastral, and Graceview bring specialized capabilities into a broader agentic platform. Legora’s own 13-day acquisition process also serves as an example of how M&A diligence, document review, drafting, and analysis are beginning to move through shared AI environments.
A major portion of the discussion focuses on the difference between traditional workflow automation and agentic AI for legal work. Forquer draws a line between prebuilt automation and agentic systems: workflows follow predetermined steps, while agents receive a goal, gather context, form a plan, call tools, and work across longer tasks with human review. Context engineering therefore becomes increasingly important. Matter data, firm knowledge, permissions, legal skills, and connections to systems through tools such as MCP all shape the quality of agentic work. M&A due diligence already represents one area where longer-horizon agentic processes are gaining traction. Legora describes the same architecture through its agentic operating system, or aOS.
The discussion then turns to economics, pricing, and proof of adoption. Greg points to Crowell & Moring’s reported 91 percent attorney adoption and nearly 70 percent weekly usage, while Forquer argues login counts and activated licenses tell only part of the story. Legora tracks daily activity and depth of feature use, including tools such as Tabular Review, skills, playbooks, and extraction templates. Agent Pro’s shift to consumption-based pricing introduces another measurement challenge, with credits tied to usage alongside dashboards, spending controls, and project-level attribution. For law firms, a broader question follows: when AI compresses hours while increasing speed, scope, and output quality, traditional measures of efficiency and value start pulling in different directions.
The episode closes with a look at what law firm innovation leaders should prepare for next. Forquer identifies the data layer as one of the central issues behind successful agentic AI. Secure access to documents, matter-level permissions, governance, firm knowledge, and well-structured context determines how far agents progress into complex legal work. Talent matters alongside infrastructure, which brings the conversation back to legal engineers and new hybrid roles spanning law, AI, knowledge management, and data governance. The episode leaves innovation and KM leaders with a practical agenda: improve data governance, build legal engineering skills, align stakeholders around risk and outcomes, and measure value through work product, adoption depth, and client impact.
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[Special Thanks to Legal Technology Hub for their sponsoring this episode.]
Email: geekinreviewpodcast@gmail.com
Music: Jerry David DeCicca
It turns out that tech companies don’t sit outside of government as just ordinary vendors. This week on The Geek in Review podcast, we welcome back Texas A&M University School of Law professor Hannah Bloch-Wehba to talk about accountability of Big Tech, AI regulations, government surveillance, and the intertwining of public authority and private tech infrastructure. Bloch-Wehba traced the dependency between the two powers all the way back to the 1930s in her article “How Tech Took Over,” in how the tech sector became a foundation for national security and economic growth.
Today’s hybrid form of governance, where Bloch-Wehba explains how a handful of private companies supply data and cloud systems, along with decision-making infrastructures across multiple governmental agencies. It is a struggle for traditional constitutional doctrines to adjust to the modern technology and the operations provided by contractors that are providing their core foundational operations.
The issues also enter into the criminal law enforcement areas and Bloch-Wehba’s “Rights, Knowledge, and Capture in the Datafied State,” discusses how trade-secret claims are throwing a barrier between proprietary data systems and criminal defendant’s ability to examine the systems that are being used to convict them in the courts. There is a strangeness in the judicial systems where corporate choices are shaping the legal process being followed, rather than corporate governance following established legal norms.
Bloch-Wehba’s “Information Law Pluralism” covers how privacy rules, audits, impact assessments, disclosure duties, researcher access, and independent review as parts of a broader system governing exactly how knowledge is shared, validated, and even produced. There seems to be no single device that transparently provides accountability. In addition, she lists how a political campaign program against states attempting to regulate AI companies and products is weakening state transparency even further.
Finally, we cover Bloch-Wehba’s “Rethinking Federal Support for Journalism” where she argues that platform payments give rise to the risk of replacing a governmental dependency gets switched for journalist and new organizations being financially tied to companies they must scrutinize. Ideas floated like an AI tax provide some alternative funding possibilities for supporting local and public-interest journalists.
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[Special Thanks to Legal Technology Hub for their sponsoring this episode.]
Email: geekinreviewpodcast@gmail.com
Music: Jerry David DeCicca