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Greg Lambert

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Librarian-Lawyer-Knowledge Management-Competitive Analysis-Computer Programmer.... I've taken the Renaissance Man approach to working in the legal industry and have found it very rewarding. My Modus Operandi is to look at unrelated items and create a process that can tie those items together. The overall goal is to make the resulting information better than the individual parts that make it up.

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This week on The Geek in Review, we bring together a trio of Canadian legends from the legal web to celebrate the 20th anniversary of the Canadian Law Blog Awards, better known as the Clawbies. Steve Matthews of STEM Legal and Slaw.ca, Sarah Sutherland of Parallax Information Consulting and former president and CEO of CanLII, and legal market analyst and Substack author, Jordan Furlong join us to talk about how legal publishing has changed over two decades and where it heads next. Along the way, we share a little host pride, since 3 Geeks and a Law Blog picked up a Friend of the North Clawbies back in 2011. Canada remembers, even if the trophy cabinet looks a little full on our side of the border.

We start with Steve’s long-running mantra: do not build your professional home on rented land. For years he pushed lawyers toward blogs and owned domains, warning that social platforms could change rules overnight or simply fall apart. That warning came into sharp focus as Twitter morphed into X and law Twitter scattered toward BlueSky, Mastodon, Threads and other venues. Jordan talks about deleting years of tweets rather than leaving a personal archive tied to a platform he no longer trusts, then describes how his own publishing shifted from long-form blogging at Law21 to a Substack newsletter model that feels more like a curated living room of engaged readers than a noisy town square.

From there, Sarah introduces one of our favorite phrases in the episode, “law’s eternal September,” where a constant wave of new technology, including generative AI, keeps the justice system and the information world in permanent transition. We explore how legal publishers now balance automation and human judgment, with AI helping on classification, annotations, and summaries, while editors and authors still play a central role in verification and context. We share our own experience with AI-assisted prep for the show, and how a human guest had to correct outdated biographical details. That leads to a broader point about the need for trusted, non-AI sources that give researchers, lawyers, and readers a place to check facts and assumptions before sharing work with clients or the public.

Jordan, Steve, and Sarah then turn to the Clawbies themselves and the theme they have set for the upcoming awards year: “the year of the truth teller.” In an era of disinformation, sloppy AI content, and reputation-damaging LinkedIn posts, lawyers and legal professionals gain real value by standing out as accurate, consistent voices who care about community as much as client work. Steve explains how the Clawbies now cover blogs, newsletters, podcasts, Tik Toks, and other formats, while still focusing on authenticity and public legal education. We also learn about the “humble Canadian rule,” where nominators highlight one to three other voices, while the organizers quietly take a closer look at the nominator’s own work in the background. The mission stays the same: surface new voices, new formats, and generous contributors who strengthen public conversation.

We close with a look ahead. Steve predicts more structured, list-driven use of newer platforms like BlueSky for targeted conversations, while Sarah points to growing centralization as giants such as Thomson Reuters, LexisNexis, and Clio blend publishing and practice software. Jordan sees a fractured present, with silos and distrust, but also anticipates a future pull toward recombination, where readers gravitate to sources and bundles that feel trustworthy again. Through it all, the three guests encourage anyone interested in writing, podcasting, or other media to choose a format that fits personal strengths, commit to thoughtful output, and focus on truth-telling over pure marketing.

For listeners who want to follow along, Sarah is active on LinkedIn and BlueSky, Jordan anchors his work on Substack, and Steve runs both Slaw.ca and the Clawbies at clawbies.ca, where nominations open December 1 and winners appear on December 31.

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[Special Thanks to ⁠Legal Technology Hub⁠ for their sponsoring this episode.]

⁠⁠⁠⁠⁠Email: geekinreviewpodcast@gmail.com
Music: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jerry David DeCicca⁠⁠⁠⁠⁠⁠⁠⁠⁠

Transcript:

Continue Reading Furlong, Matthews, and Sutherland: Truth Tellers, Rented Land, and 20 Years of the Clawbies

This week we welcome Jiyun Hyo, co-founder and CEO of Givance, for a conversation about moving legal AI past shiny summaries toward verified work product. Jiyun’s path runs from Duke robotics, where layered agents watched other agents, to clinical mental health bots, where confident errors carry human cost. Those lessons shape his view of legal tools today: foundation models often answer like students guessing on a pop quiz, sounding sure while drifting from fact.

A key idea is the “last ten percent gap.” Many systems reach outputs that look right on first pass yet slip on a few crucial details. In low-stakes tasks, small misses are a nuisance. In litigation, one missing email or one misplaced time stamp risks ruining trust and admissibility. Jiyun adds a second problem: when users ask for a tiny correction, models tend to rebuild the whole output, so precision edits become a loop of fixes and new breakage.

Givance aims at that gap through text-to-visual evidence work. The platform turns piles of documents into interactive charts with links back to source files. Examples include Gantt charts for personnel histories, Sankey diagrams for asset flows, overlap views for evidence exchanges, and timelines that surface contradictions across thousands of records. Jiyun shares early law-firm use: rapid fact digestion after a data dump, clearer client conversations around case theory, and courtroom visuals that help judges and juries follow a sequence without sketching their own shaky diagrams.

Safety, supervision, and security follow naturally. Drawing on robotics, Jiyun argues for a live supervisory layer during agentic workflows so alerts surface while negotiations or analyses unfold rather than days later. Too many alerts, though, create noise, so tuning confidence thresholds becomes part of product design. On security, Givance works in isolated environments, strips identifiers before model calls, and keeps architecture model-agnostic so newer systems slot in without reopening privacy debates.

The episode ends on market dynamics and the near future. Jiyun sees mega-funded text-first platforms as market openers, normalizing AI buying and leaving room for second-wave multimodal tools. Asked whether the search bar in document review fades away, he expects search to stick around for a long while because lawyers associate a search box with control, even if chat interfaces improve. The bigger shift, in his view, lies in outputs, more interactive visuals that help legal teams spot gaps, test case stories, and present evidence with clarity.

Listen on mobile platforms:  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Apple Podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ |  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Spotify⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

[Special Thanks to ⁠Legal Technology Hub⁠ for their sponsoring this episode.]

⁠⁠⁠⁠⁠Email: geekinreviewpodcast@gmail.com
Music: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jerry David DeCicca⁠⁠⁠⁠⁠⁠⁠⁠⁠

Transcript:

Continue Reading The Last Ten Percent, Visual Evidence, and Supervised Agents with Jiyun Hyo of Givance

We recorded this episode live at the TLTF Summit and the energy in the room made it feel like the perfect place for a conversation about growth, training, and the rapid climb of legal tech. We grabbed our gear, claimed a corner in the podcast room, and pulled in two guests with front row seats to the changes hitting the industry. Joining us were Kyle Poe from Legora and our friend and guest host, Zena Applebaum of Harbor. The Summit attracts a focused group of founders, investors, and leaders, and the four of us jumped straight into what this event represents and what attendees hope to get from it.

Kyle had been on the job for only two months, but Legora moves at a pace that feels closer to dog years. In that short time the team doubled, a new round of funding closed, and the company introduced a major product release. Kyle walked us through Legora’s new Portal experience, which brings clients inside the legal workflow in a controlled, collaborative environment. Instead of long email chains and static work product, the Portal supports shared editing, direct review of diligence work, and a more responsive model for client engagement. In an era when clients expect quick turnarounds, this shift sets up a new dynamic for firms.

Zena added helpful perspective from her prior trips to TLTF. She described the Summit as a place that rewards conversation, curiosity, and hallway exchanges. It is also a place to study the different stages of the legal tech journey, from early ideas on the startup stage to the seasoned players on the scale stage. She also brought timely news of Harbor’s acquisition of Encore Technologies, a move that strengthens Harbor’s ability to support training and adoption workflows across firms and corporate legal teams. Her focus on education paired well with Kyle’s insights on how Legora approaches enablement through its team of legal engineers.

Training became the heart of the conversation. We compared old habits with the expectations of a generation of associates who have been taught to avoid AI until they enter a firm. Kyle stressed the need to anchor attorney training in real use cases and to give them early wins so they build trust in the tools. He described the shift from task-based training to workflow-based thinking. Zena echoed this point and highlighted the growing trend of firms reserving time for associates to explore AI tools as part of their professional development rather than treating experimentation as a side project squeezed between billable work.

We also talked about how AI is influencing both the pace and structure of client service. Kyle shared examples of how Legora uses prior work product to build integrated workflows, such as interrogatory response generators that pull from a full library of past responses. This not only speeds up production but also increases consistency and helps attorneys understand the reasoning behind revisions. Zena pushed the idea even further, noting that these systems give associates a chance to study the rationale behind changes in a way that human reviewers rarely have time to provide. This leads to better training and stronger validation of the final work product.

We closed with our crystal ball question. Kyle sees more adoption on the horizon but also anticipates uneven impacts across different practices as firms figure out how to adjust their business models. Zena pointed to the operational challenges ahead, especially the pressure to invest in data management and cloud infrastructure that supports true AI enablement. Her message was clear. If firms want the benefits later, they need to start organizing the foundations now. This episode blends optimism with realism, and it highlights the practical work ahead for firms, vendors, and everyone in between. Tune in for the full conversation and get ready for a lively discussion recorded right in the middle of the Summit buzz.

Listen on mobile platforms:  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Apple Podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ |  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Spotify⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

[Special Thanks to ⁠Legal Technology Hub⁠ for their sponsoring this episode.]

⁠⁠⁠⁠⁠Email: geekinreviewpodcast@gmail.com
Music: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jerry David DeCicca⁠⁠⁠⁠⁠⁠⁠⁠⁠

Transcript:

Continue Reading AI Dividends and Workflow Training: Live with Legora and Harbor at TLTF

In this episode of The Geek in Review, we welcome three powerhouse guests—Cas Laskowski, Taryn Marks, and Kristina (Kris) Niedringhaus—who are charting a bold course for Artificial Intelligence & the Future of Law Libraries. These three recently co-authored a major white paper, Artificial Intelligence and the Future of Law Libraries (pdf), which we see as less of a report and more of a call to arms. Together, we explore how law librarians can move from reactive observers of AI’s rise to proactive architects shaping its ethical and practical integration across the legal ecosystem.

Cas Laskowski, Head of Research Data and Instruction at the University of Arizona College of Law, shares how the release of ChatGPT in 2022 jolted the profession into action. Librarians everywhere were overwhelmed by the flood of information and hype surrounding AI tools. Cas’s response was to create a space for collective thinking and planning: the Future of Law Libraries initiative and a series of roundtables designed to bring professionals together for strategic collaboration. One of the paper’s most ambitious recommendations—a centralized AI organization for legal information professionals—aims to unify those efforts, coordinate training, and sustain a profession-wide vision. Cas compares the idea to data curation networks that transformed academic libraries by pooling expertise and reducing duplication of effort.

Kris Niedringhaus, Associate Dean and Director of the University of South Carolina School of Law Library, takes the conversation into education and training. She makes a compelling case that “AI-ready librarians,” much like “tech-ready lawyers,” need flexible skill-building models that recognize different levels of engagement and expertise. Drawing from the Delta Lawyer model, Kris calls for tiered AI training—ranging from foundational prompt literacy to higher-level data ethics and system design awareness. She also pushes back against the fear surrounding AI in academia, noting that students are often told not to use AI at all. We couldn’t agree more with her point that we’re doing students a disservice if we don’t teach them how to use these tools effectively and responsibly. Law firms now expect graduates to come in with applied AI fluency, and that expectation will only grow.

When we turned to Taryn Marks, Associate Director of Research and Instructional Services at Stanford Law School’s Robert Crown Law Library, the discussion moved to another key recommendation: building a centralized knowledge hub for AI-related best practices. Taryn describes how librarians are eager to share materials, lesson plans, and policy frameworks, but the current efforts are fragmented. A shared repository would “reduce duplication of effort” and allow ideas to evolve through open collaboration. It’s similar to how standardized models like SALI help the legal industry align without giving away anyone’s secret sauce. We loved this idea of a commons where librarians, educators, and technologists work together to lift the entire profession.

As we explored the broader implications, all three guests agreed that intentionality is key. Cas emphasizes that information architecture—the design of how knowledge is gathered, tagged, and retrieved—is central to AI’s success. Kris points to both the promise and peril of automated legal decision-making, warning that “done well, AI can expand access to justice; done poorly, it can amplify bias.” And Taryn envisions a future where legal information professionals are trusted collaborators across the entire lifecycle of data and decision-making.

We closed the conversation feeling both inspired and challenged. The message is clear: law librarians shouldn’t sit on the sidelines of AI. They are uniquely positioned to lead, to teach, and to ensure that the technologies shaping law remain grounded in ethics, accessibility, and the rule of law. For those who want to get involved, Cas directs listeners to the University of Arizona Law Library’s Future of Law Libraries Initiative page, which includes the white paper and volunteer opportunities. This episode reminded us that the future of AI in law won’t be defined by the tools themselves, but by the people—especially librarians—who decide how those tools are used.

Links:

Listen on mobile platforms:  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Apple Podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ |  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Spotify⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

[Special Thanks to ⁠Legal Technology Hub⁠ for their sponsoring this episode.]

⁠⁠⁠⁠⁠Email: geekinreviewpodcast@gmail.com
Music: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jerry David DeCicca⁠⁠⁠⁠⁠⁠⁠⁠⁠

Transcript:

Continue Reading Law Librarians Take the Lead: The Future of AI and Legal Information

I’ve been thinking about a story that I believe deserves more attention than it’s getting.

Robin AI, once positioned as a rising star in legal AI, has missed its funding round, cut a third of its staff, and landed on a distressed sale marketplace. The question isn’t whether this is unfortunate. It’s whether this is a harbinger. (Non-Billable)

Is Robin AI’s collapse a one-off execution failure, or the first visible crack in a legal tech AI bubble?

What happened at Robin AI

Robin AI launched in 2019 with a compelling premise: a “lawyer-in-the-loop” contract review system that combined large language models with proprietary contract data. The founding team brought credibility: lawyer Richard Robinson and machine-learning researcher James Clough building something at the intersection of both worlds. In early 2024, they raised $26 million in Series B funding.  The marketing was aggressive: major enterprise clients, ambitious platform expansion across drafting and negotiation, claims of transformative efficiency gains.

By late 2025, the picture had changed dramatically. Internal reports suggested the company failed to secure another major funding round (targeting roughly $50 million), laid off about a third of its workforce, and quietly listed itself for sale on a distressed marketplace.

That trajectory, from high-profile funding to forced sale in under two years, warrants closer examination.

The red flags were there

Robin AI never publicly disclosed its Series B valuation. In a market where lofty valuations typically accompany large deals, that absence now looks less like discretion and more like avoidance. Without a clear number, it’s impossible to assess whether investor expectations matched operational reality or whether growth projections were ever grounded in achievable metrics.

More telling were the employee accounts. Reviews on Glassdoor described a culture of overwork, inadequate support, and marketing claims that outpaced product capability. One reviewer noted the company positioned itself as AI-driven while “in practice most of the work is handled manually by staff.”   Another called it their “worst professional experience to date,” citing a “rule by fear” environment where junior team members shouldered contract reviews with minimal support.

These aren’t just grievances about workplace culture. They’re signals about the gap between what was being sold and what was being delivered.

What looks like a fluke
Continue Reading Is the Collapse of Robin.AI a One-Off or a Sign of a Legal Tech AI Bubble?

This week on The Geek in Review, Greg Lambert and Marlene Gebauer sit down to compare notes from a busy conference season. Marlene shares her experience at the American Legal Technology Awards where The Geek in Review was honored for excellence in journalism. She recounts the surreal joy of being recognized among friends and peers in legal tech, including fellow nominees like Steve Embry, and how a spontaneous speech turned out to be one of the night’s highlights. The duo reflects on how events like this underscore the sense of community that continues to define the innovation side of the legal industry.

Greg takes listeners behind the scenes at ClioCon, describing it as one of the most energetic user conferences around. He dives into his conversation with Clio CEO Jack Newton and how the company’s recent vLex acquisition signals a bold expansion into the Big Law space. With $900 million in funding, Clio appears ready to bridge the divide between small-firm technology and enterprise-level workflows. Greg also teases an illuminating hallway chat with Ed Walters, now at Clio Library (formerly vLex/Fastcase), about the major leap forward in legal research accuracy driven by improvements in RAG (retrieval-augmented generation) and vector database indexing.

Marlene offers her own takeaways from the Association of Corporate Counsel (ACC) Annual Meeting, where AI and governance dominated the agenda. She describes a landscape where in-house lawyers are wrestling with both the promise and peril of generative AI, from shadow AI concerns to data hygiene challenges. Her biggest surprise was seeing law firms themselves exhibiting at the ACC conference, signaling a shift toward direct engagement between firms and their corporate clients in shared learning spaces.

Together, Greg and Marlene unpack the emerging themes of human-centered governance, the evolving role of AI in matter management, and the race among vendors to automate core workflows without losing the human touch. From Clio’s plans to build AI-driven workflow mapping that could auto-draft documents, to Marlene’s caution about how bespoke law firm processes might resist one-size-fits-all automation, their discussion paints a picture of a profession both accelerating and self-checking at once.

The episode winds down with lighter reflections on travel mishaps, conference after-parties, and the long arc of Richard Susskind’s The End of Lawyers? conversation—still ongoing, now infused with cautious optimism about AI’s role in expanding access to justice. As always, they end where The Geek in Review thrives: at the intersection of humor, humility, and the hopeful chaos of legal innovation.

Listen on mobile platforms:  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Apple Podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ |  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Spotify⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

[Special Thanks to ⁠Legal Technology Hub⁠ for their sponsoring this episode.]

⁠⁠⁠⁠⁠Email: geekinreviewpodcast@gmail.com
Music: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jerry David DeCicca⁠⁠⁠⁠⁠⁠⁠⁠⁠

Transcript:

Continue Reading Conferences, Catch-ups, and Clio’s Big Swing at Big Law

Artificial intelligence has moved fast, but trust has not kept pace. In this episode, Nam Nguyen, co-founder and COO of TruthSystems.ai, joins Greg Lambert and Marlene Gebauer to unpack what it means to build “trust infrastructure” for AI in law. Nguyen’s background is unusually cross-wired—linguistics, computer science, and applied AI research at Stanford Law—giving him a clear view of both the language and logic behind responsible machine reasoning. From his early work in Vietnam to collaborations at Stanford with Dr. Megan Ma, Nguyen has focused on a central question: who ensures that the systems shaping legal work remain safe, compliant, and accountable?

Nguyen explains that TruthSystems emerged from this question as a company focused on operationalizing trust, not theorizing about it. Rather than publishing white papers on AI ethics, his team builds the guardrails law firms need now. Their platform, Charter, acts as a governance layer that can monitor, restrict, and guide AI use across firm environments in real time. Whether a lawyer is drafting in ChatGPT, experimenting with CoCounsel, or testing Copilot, Charter helps firms enforce both client restrictions and internal policies before a breach or misstep occurs. It’s an attempt to turn trust from a static policy on a SharePoint site into a living, automated practice.

A core principle of Nguyen’s work is that AI should be both the subject and the infrastructure of governance. In other words, AI deserves oversight but is also uniquely suited to implement it. Because large language models excel at interpreting text and managing unstructured data, they can help detect compliance or ethical risks as they happen. TruthSystems’ vision is to make governance continuous and adaptive, embedding it directly into lawyers’ daily workflows. The aim is not to slow innovation, but to make it sustainable and auditable.

The conversation also tackles the myth of “hallucination-free” systems. Nguyen is candid about the limitations of retrieval-augmented generation, noting that both retrieval and generation introduce their own failure modes. He argues that most models have been trained to sound confident rather than be accurate, penalizing expressions of uncertainty. TruthSystems takes the opposite approach, favoring smaller, predictable models that reward contradiction-spotting and verification. His critique offers a reminder that speed and safety in AI rarely coexist by accident—they must be engineered together.

Finally, Nguyen discusses TruthSystems’ recent $4 million seed round, led by Gradient Ventures and Lightspeed, which will fund the expansion of their real-time visibility tools and firm partnerships. He envisions a future where firms treat governance not as red tape but as a differentiator, using data on AI use to assure clients and regulators alike. As he puts it, compliance will no longer be the blocker to innovation—it will be the proof of trust at scale.

Listen on mobile platforms:  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Apple Podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ |  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Spotify⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

[Special Thanks to ⁠Legal Technology Hub⁠ for their sponsoring this episode.]

⁠⁠⁠⁠⁠Email: geekinreviewpodcast@gmail.com
Music: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jerry David DeCicca⁠⁠⁠⁠⁠⁠⁠⁠⁠

Transcript:

Continue Reading Trust at Scale: Nam Nguyen on How TruthSystems is Building the Framework for Safe AI in Law

I’ve been watching the legal-tech landscape for a long time, and this morning’s announcement from Thomson Reuters’ partnership with DeepJudge marks a moment worth pausing over. (DeepJudge) On October 22, 2025, TR disclosed that DeepJudge’s enterprise-search and AI-knowledge-platform capabilities will be integrated into TR’s CoCounsel Legal offering to bring internal-firm knowledge and…

The promise of generative artificial intelligence (AI) in legal practice is seductive: speed up document review, contract drafting, legal research, and thereby shave down hours billed. Yet the reality for many law firms is different. A recent survey by the Association of Corporate Counsel (ACC) and Everlaw found that nearly 60% of in-house counsel reported…

Few people understand the intersection of legal practice, data analytics, and diversity like Catherine Krow, Managing Director of Diversity and Impact Analytics at BigHand. In this episode of The Geek in Review, hosts Greg Lambert and Marlene Gebauer sit down with Krow to trace her journey from a high-powered trial lawyer to an influential legal tech leader. After seventeen years at firms like Orrick and Simpson Thacher, Krow’s turning point came when a client challenged her team’s billing after a major courtroom victory—a moment that sparked her mission to fix what she calls the “business of law.”

That single moment led to the creation of Digitory Legal, a company designed to give law firms the data and transparency they desperately needed but didn’t yet value. Krow describes how her framework—plan, measure, refine—became the basis for improving cost predictability and strengthening client trust. When BigHand acquired Digitory Legal in 2022, Krow’s vision found a larger stage. Now, her “data refinery” powers better pricing, resource allocation, and even equity within firms. As she explains, clean data doesn’t only improve profitability, it reveals hidden inequities in work allocation and helps firms retain their most promising talent.

Krow also digs into one of her favorite topics: “data debt.” Law firms are drowning in data but starved for information. She explains how poor data hygiene—like inconsistent time codes and messy narratives—has left firms unable to use their most valuable resource. BigHand’s impact analytics tools attack this problem head-on, transforming raw billing data into usable intelligence that drives decision-making across finance, staffing, and diversity efforts. And while the technology is powerful, Krow is clear that solving data debt is as much a cultural challenge as it is a technical one.

Another major theme is the evolving role of business professionals within law firms. Krow argues that lawyers’ traditional discomfort with financial forecasting and project management is holding firms back. Her solution? Combine legal expertise with the commercial acumen of allied professionals. Together, they can meet client demands for budgets, accountability, and measurable value—especially as AI begins to reshape how legal services are delivered and priced.

The episode closes with Krow’s broader reflection on the next decade of legal innovation. She warns that the biggest shift ahead isn’t about AI or analytics—it’s about mindset. Firms that embrace data-driven decision-making now will define the future of law; those that don’t will be left behind. Through her work at BigHand, Krow is helping to ensure that future is both more efficient and more equitable.

Links:

Listen on mobile platforms:  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Apple Podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ |  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Spotify⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

[Special Thanks to ⁠Legal Technology Hub⁠ for their sponsoring this episode.]

⁠⁠⁠⁠⁠Email: geekinreviewpodcast@gmail.com
Music: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jerry David DeCicca⁠⁠⁠⁠⁠⁠⁠⁠⁠

Transcript:

Continue Reading Data Debt, Diversity, and the Business of Law: A Conversation with BigHand’s Catherine Krow