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.

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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 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.

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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 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:

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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 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.

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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 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.

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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 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 external content into a unified workflow. (Thomson Reuters) For legal innovation folks like me, this is interesting because it suggests a pivot from piecemeal tools toward platform thinking. Also, for more experienced legal innovation folks like me, this sounds a lot like what we used to get from Thomson Reuters with Westkm. But, with a lot more potential.

Here’s why the move matters in practical terms. Many law firms and corporate legal departments generate massive volumes of internal work-product like memos, closing binders, client-matter files that sit behind siloes. DeepJudge is built for just that scenario by its ability to index disparate internal sources (DMS, SharePoint, HighQ, email archives) and surface relevant content fast. (Artificial Lawyer) Meanwhile, TR has been the longtime provider of high-value external legal content (Westlaw, Practical Law, etc.). Bringing those two domains into one searchable, actionable ecosystem offers firms a “360-degree” view of firm knowledge and external insight. (Artificial Lawyer)

That said, I’m not buying into the idea that this solves everything overnight. Integrating internal sources across a global law firm is hard. Really hard!  Things like permissions, data governance, security protocols, taxonomy, indexing, change-management all still loom large in this integration. The announcement acknowledges this. (Thomson Reuters) For many peer firms I talk to, the biggest bottleneck remains adoption and workflow redesign rather than raw technology. Having it available is one thing but embedding it into how lawyers work is quite another.

From a business model and vendor-ecosystem perspective, this partnership is signal-rich. Rather than buying multiple point tools and handling multiple contracts, firms may now sign on with TR for its content, AI workflows, and DeepJudge’s internal-search engine under one procurement umbrella. According to the interview coverage, “in most cases, customers can subscribe to Thomson Reuters and DeepJudge solutions on one single contract … procurement and billing are streamlined.” (Artificial Lawyer) For legal ops and KM leaders, that simplifies vendor management—but it also raises questions: How will ROI be measured? What will change in the outside-counsel bidding process? If internal reuse of knowledge becomes a selling point, will fee structures change accordingly?

Strategically, this might shift how law firms approach their AI and knowledge agendas. Many firms are still running pilots, experimenting in one practice group or region. This partnership offers a more scalable “platform” option by indexing internal knowledge, connecting it to curated external content, and plugging in AI workflows. DeepJudge CEO, Paulina Grnarov, puts it like this: “Every firm working on their AI strategy is realising that fast, efficient access to the right information is the foundation … for making any AI workflows or agents truly effective.” (Artificial Lawyer) For innovation leaders inside firms, the message is clear: move from experimentation to enterprise-scale discipline.

What does this mean for corporate legal departments and legal operations teams? If your outside counsel or you are working with a firm using this combined TR/DeepJudge capability, you should begin asking:
“How are you leveraging internal precedent and firm knowledge in my matter?”
“Are you measuring reuse of knowledge as a value driver?”
“Are you expecting fewer hours or faster turnaround because of built-in indexing and AI?”

As clients increasingly insist on value-based service, this sort of capability may become a differentiator. The risk for firms is that those who don’t evolve may lose ground.

The TR–DeepJudge collaboration is a signal, not a destination. It suggests a next phase in legal-tech evolution through a combined unified internal and external knowledge, AI-augmented workflows, vendor consolidation. But success will depend on execution, governance, adoption, metrics, and change management inside firms. From where I sit, the question isn’t whether this partnership is interesting, because it is. The question is whether law firms will turn the promise into practice, and whether clients will ask hard enough questions to make it matter.

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 “no noticeable savings yet” from outside counsels’ use of generative AI. (Bloomberg Law News) Among those who did see some benefit, only 13% pointed to fewer billable hours and 20% to faster turn-around.  That suggests the headline of “AI slashes bills” is premature.

One major reason is that law firms remain where they always were: a patchwork of experiments instead of a unified transformation. The business model based on time-spent (“billable hours”) is deeply embedded. As a Harvard Law piece puts it, large law firms’ productivity gains from AI clash with the traditional billable hour model. (clp.law.harvard.edu) When a firm charges by the hour, there is a disincentive to reduce hours spent; improvements in efficiency don’t automatically translate to fewer billable hours. (2Civility) Until the billing model evolves, firms have less motivation to push AI’s full potential into cost-reducing workflows.

Compounding the billing-model friction is uneven adoption of AI across practices and firms. Some firms or practice groups test tools for document review; others do contract-drafting automation; many lag behind entirely. Legal tech firms struggle to sell their AI products to large law firms because the billable hour model skews incentives. (Legal.io) Put differently: the technology is advancing but the institutional deployment lags. An Everlaw survey showed lawyers enabling generative AI report saving up to 260 hours annually, but such gains don’t necessarily map to billable-hour reductions if those hours are reallocated rather than eliminated. (everlaw.com)

From the client side the pressure is mounting. Many in-house legal departments expect outside counsel to adopt generative AI tools. According to a survey by LexisNexis, 67% of in-house counsel said they expect their law firms to use these tools. (legaldive.com) Meanwhile the ACC-Everlaw data show that 64% of respondents expect to bring more legal work in-house because of generative AI. (everlaw.com) In short, clients are signaling change and may drive pricing shifts, even though many firms aren’t ready.

One more factor is the measurement gap. Even when AI is deployed, law firms struggle to track and demonstrate savings to clients. AI might reduce time on a task but still require review, validation, or supplemental work by senior lawyers—so billable hours don’t fall as expected. The SSRN article “How the Billable Hour Can Survive Generative AI” argues that hours may drop but other factors (rate, staffing, utilization) change to offset that drop. (SSRN) Thus efficiency gains aren’t automatically visible or bill-reducing.

Looking ahead, AI’s role may push pricing models to evolve. Several thought-leaders suggest the billable hour’s grip is loosening. For instance a Thomson Reuters article on “Pricing AI-driven legal services: The billable hour is dead, long live…” observes that generative AI may accelerate shifts to flat fees or output-based billing. (Thomson Reuters) Similarly, research from Wolters Kluwer points to 67 % of corporate legal departments and 55% of law firms expecting AI-driven change to the billable hour model. (Wolters Kluwer) The inconsistency across firms means we are in transition rather than arrival.

In sum, AI is real, law firms are adopting tools, and some work is faster. But the core obstacle to billable-hour reduction is structural: the business model built on hours, inconsistent deployment of technology, lack of measurement/discounting mechanisms, and a client-driven push for change from outside. Until law firms coordinate practice-wide workflows, redesign billing, and reflect AI-driven efficiencies in their invoices, clients will continue to ask “where’s the savings?” and firms will nod and say “we’re working on it.”

Well… on the bright side, at least we didn’t say “It depends.”

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:

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

Jack Newton knows how to command a stage. ClioCon 2025 opened like a Vegas tech revival, complete with light shows, Clions (Clio employees) marching up to the stage, keynote hype, and a CEO convinced that lawyers are about to enter a “new era of intelligent legal work.” And for the first time, he wasn’t talking to solos and small firms. He was looking straight at Big Law.

After seventeen years of building a platform for the other 80% of the legal market, Clio is moving upmarket with a new product, a billion-dollar acquisition, and a not-so-subtle claim that the incumbents have gotten too slow, too siloed, and too expensive. The message was clear: the cloud kids are all grown up and ready to play with the enterprise crowd.


From Record to Action

Newton’s pitch was simple: “systems of record” are over. The next generation of legal tech will be “systems of action.” Instead of storing what lawyers have done, Clio wants to automate what happens next. Deadlines, drafts, client updates, billing, intake. All of these handled or at least initiated by an AI assistant that never takes a vacation or forgets a date.

It’s a clever reframing of what AI actually means in practice. Less magic, more workflow. The idea isn’t to replace lawyers but to replace all the boring, repetitive steps between thinking and billing. Whether that translates in firms that already juggle iManage, NetDocuments, Elite 3E, and a few thousand customized applications is the question.


Enter Clio Operate

The headline for big firms is the launch of Clio Operate, built on the bones of the acquired ShareDo platform. Newton called it “Clio Manage’s big brother,” designed for firms with 200 to 1,000+ lawyers. The promise is configurability, enterprise-grade permissions, and firm-wide governance that keeps multi-office operations aligned.

That’s all fine. But enterprise lawyers don’t buy promises; they buy controls. Does Operate honor existing ethical walls? Does it integrate cleanly with your DMS? Can it scale to thousands of concurrent users without melting down? Those questions didn’t make it into the keynote slides, but they’ll decide whether Clio Operate becomes a serious enterprise contender or just another mid-market flirtation.


The $1B Bet on Data

Clio’s move from practice management to platform only makes sense if it controls the data that feeds the AI. That’s where the vLex/Fastcase acquisition comes in. It’s a billion-dollar swing that instantly gives Clio something close to global legal data parity with Lexis and Westlaw.

The new structure looks like this:

  • vLex and Fastcase content become Clio Library.
  • Docket Alarm becomes Clio Docket.
  • Vincent AI stays the brain behind the curtain.

Newton’s claim is that by grounding AI in real legal data, Clio can eliminate hallucinations and build something smarter than generic large language models. Maybe. But the bigger question for large firms is how Clio plans to keep that data segregated from confidential firm context. You don’t want your client memo accidentally “enriching” the Clio Library.


Clio Work and the AI Hub

Then came the showpiece: Clio Work, a new AI workspace priced at $199 per user per month. Newton positioned it as the bridge between the “business of law” and the “practice of law.” It pulls matter context from Manage or Operate, fuses it with Clio Library’s legal data, and lets Vincent draft, analyze, and reason alongside you.

It’s a fascinating idea, and at that price point, Clio is deliberately poking the research giants. They’re betting firms will trade legacy precision for workflow speed. Whether the AI can produce consistently reliable, citable output is still unproven. The demo audience saw smooth integration and accurate citations; what happens in a real case file under privilege pressure is another story.


AI Teammates Everywhere

Clio also re-skinned its products around the idea of “AI teammates.”

  • Manage AI: Extracts deadlines, drafts client updates, and even prepares bills.
  • Grow AI: Handles intake, conflict checks, and scheduling.
  • Draft AI: Turns old documents into templates with auto-built client questionnaires.

It’s the kind of automation you’d expect to see in an internal R&D lab at a large firm, except Clio is promising it out-of-the-box. The danger is obvious: an AI that acts faster than your review process. The benefit, if the governance is right, is hours shaved off every case. The line between those outcomes is thin and paved with risk management memos.


Money Machines: Clio Capital and Pay Later

Then came the financial add-ons: Clio Capital, which lets firms borrow through the platform, and Pay Later, which lets clients pay in installments while the firm gets paid upfront. Both ideas make sense for small practices chasing cash flow. For large firms, they look more like compliance puzzles waiting to happen.
Trust accounting, disclosure rules, and client consent all get messy fast when your practice management system starts behaving like a lender. It’s creative, but it’s also an ethics exam question in the making.


The Enterprise Reality Check

Newton’s presentation hit all the right notes—speed, integration, AI intelligence, and global reach. But large firms will judge on different metrics. Before anyone in Big Law gets seduced by the sizzle reel, Clio needs to show:

  • Single-tenant architecture and regional data residency.
  • Immutable AI logs and human-review checkpoints.
  • Deep, tested integrations with iManage, NetDocuments, Elite, and Intapp.
  • Compliance documentation that satisfies outside counsel guidelines.

That’s the difference between a good demo and an enterprise deployment.


Collapsing the Stack

What Clio is really chasing is control of the full workflow. The “intelligent legal work platform” is an attempt to merge research, drafting, intake, matter management, billing, and payments into one continuous experience. It’s the same logic that made Microsoft 365 unavoidable in the corporate world. Once everything lives in one suite, leaving becomes painful.

For Big Law innovation teams, that’s the strategic decision ahead: integrate with Clio’s ecosystem or build around it. Either way, the gravitational pull is growing.


Final Verdict

Jack Newton’s keynote was less a product launch than a statement of intent, along with a much-too-long-for-an-hour-keynote list of new resources and goals. Clio doesn’t want to be the friendly cloud alternative anymore. It wants to be the operating system for legal work, from solos to global firms.

There’s real substance in what they’ve built—the vLex acquisition alone gives them credibility they’ve never had before. But the enterprise market is unforgiving. It doesn’t reward charm; it rewards uptime, compliance, and control.

If Clio can deliver those without losing its speed and accessibility, this keynote might be remembered as the moment the cloud-first company that spent 17 years focused on small law finally cracked Big Law. If not, it will be another well-produced reminder that ambition and enterprise infrastructure rarely fit in the same demo.