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

Transcript

Marlene Gebauer (00:00)
Hi, I’m Marlene Gebauer from The Geek in Review. I have Sarah Glassmeyer here from Legaltech Hub. Sarah’s going to share with us how you can use the Legal Technology Directory to prep for ILTACON.

Sarah Glassmeyer (00:12)
Yeah, so this is probably one of the more exciting times of the year for legal tech kind of leading into ILTACON. Everyone’s excited to see all the new changes, see people we haven’t seen for a year. So Legaltech Hub can really help you pre-game and get ready to get the most out of it because while you’re there, it does seem like you live in Nashville and you’ve been there your entire life, but it goes really fast. You really only have like two and a half, three days to talk to vendors. So a couple of things I would suggest you do.

One, look at the list of who is exhibiting at ILTACON. So if you go to the ILTA website, they have a legal tech directory but guess what? That is us. We partner with ILTA. It is a mirror of our directory.

You can see the little badges, and that will tell you who’s exhibiting at ILTACON. Don’t just wander in there. Think ahead and think like, what am I looking for this year? Am I looking for a new kind of document automation tool? Am I looking for a new AI legal assistant?

So you do some filtering, make a list to see who’s going to be there, and start thinking ahead. And through the directory, you can see, for example, that some companies don’t integrate with X tool, so they’re off the list for us. So you can kind of do a little pre-filtering, pre-gaming, and figure out who you want to talk to, who’s going to make the most of your time. And then from there, if you’re back on our platform in the Legaltech Hub directory and you’re logged in, you can make notes. So as you’re wandering through the exhibit hall, visiting the people you want to visit, you can kind of make notes saying, “Here’s this person’s email address that I spoke to and I want to follow up with them,” or, “Maybe show Bob this when I get back to my office,” or, “These people are off the list.

Forget about them.” And so you just kind of keep track of who you’ve talked to, what you’re thinking about things live, and it’s all in one place on the Legaltech Hub directory, not just a swag bag full of business cards and flyers. So that’s how we can help you get the most out of what you’re doing. But also, I do suggest just wandering around the exhibit hall because everyone who’s there does have a listing on Legaltech Hub. So you can kind of double-check it. But I think it’s really good just to kind of wander, see who catches your eye, have a spontaneous conversation because you might find a vendor that you never considered or something you weren’t really thinking about ahead of time.

So it is kind of a fun time to get yourself absorbed in what’s happening in legal tech.

Marlene Gebauer (02:27)
Yeah, serendipity is definitely part of the ILTACON experience, but this is great advice in terms of using the Legaltech Hub directory because it can be overwhelming and this will allow you to focus on the vendors and people that you definitely need to talk to before you leave. Thank you.

Marlene Gebauer (02:54)
Welcome to The Geek in Review, the podcast focused on innovative and creative ideas in the legal industry. I’m Marlene Gebauer.

Greg Lambert (03:00)
And I’m Greg Lambert And today we are going to be looking at the next phase of legal AI work where vendors are moving beyond, or perhaps more accurately, adding to assistants and isolated pilots, toward more agentic work, more firmwide deployment and new pricing models.

Marlene Gebauer (03:20)
And our guest sits at the center of those commercial and operational questions. Patrick Forquer is Chief Revenue Officer at Legora, where he leads the company’s global go-to-market strategy and works with law firms and legal teams moving AI from experimentation into daily legal work. Patrick, welcome to The Geek in Review.

Patrick Forquer (03:40)
Hey, Marlene. Hey, Greg., thanks so much for having me.

Marlene Gebauer (03:43)
So one thing caught my eye recently. Legora is hiring a significant number of attorneys, not just software engineers. I heard Max on a podcast saying that you guys may have more lawyers than engineers, and that’s kind of unique in the industry. So you’re hiring legal engineers, legal associates, and lawyers with practice experience. What do those lawyers actually do day-to-day?

What skills make someone successful in those roles? And what does that tell you about where the profession’s headed?

Patrick Forquer (04:15)
Yeah, it’s a great question. I get this question all the time. And I think legal engineering is like one of the most exciting roles in technology now, just generally, because they’re on the front lines of AI and the AI revolution, doing the hard work of change management and adoption in firms. And so I think if you wind all the way back, I mean Legora was founded by engineers, right? One of the founding principles of the company is that we want to build with lawyers, not simply for lawyers.

That means taking their feedback and understanding the deep levels of requirements and subject matter expertise needed to build a successful legal AI platform. For us, that’s always been a founding principle, having lawyers in the business helping guide the direction of the product. More importantly these days, there’s this idea of transformation. We’re fortunate that Legora is popular right now. We get to have lots of conversations with many great firms and great companies out there in the market.

But at the end of the day, we have to help lawyers adapt AI into their ways of working, into their workflows. And candidly, you can’t just do that with standard consulting practices. You have to have deep levels of subject matter expertise. So, for example, people from an M&A background understand how an M&A deal gets run and the due diligence process. The same applies to litigation and the different aspects and practice areas of legal work.

And so our lawyers predominantly work in our legal engineering function. They embed with our sales and post-sales teams. First, they have deep levels of practice-area expertise. So they bring credibility to our clients: we understand your business, and we understand the types of deals, cases, and matters you’re working on. And then they have high levels of technology and AI proficiency, meaning they understand how to do things like prompt and context engineering.

They understand how agentic workflows work and how to build them. And they’re highly curious and adaptive in a client-facing environment where you can go in and work on a live deal or a live matter and help build sort of long-horizon workflows within Legora to help our clients deliver faster and better work. It’s really exciting, and I love spending time with our legal engineers. They’re some of the smartest, best people I’ve ever worked with.

Marlene Gebauer (06:36)
That’s a great endorsement.

Greg Lambert (06:36)
See Marlene, this is why we’re having a hard time finding these people. They’re taking them from us.

Marlene Gebauer (06:39)
That’s what I was going to say. I’m seeing discussions about what’s going to happen to our junior people, and those are all legitimate questions. I’m glad we’re having these discussions about other job opportunities that may be out there for people.

Patrick Forquer (06:58)
Yeah, and we definitely think that, Marlene. We think legal engineering is a new career path. It has always existed in different forms at different companies, and Greg and I were talking about that earlier. But the current form of building these bespoke workflows, skills, and capabilities within tools like Legora is not something that was done as commonly as it is now. And they’re making a meaningful impact on the client work that’s getting delivered.

And it’s showing up for our clients in their work with their clients. And I think that’s the most exciting part. I definitely think it’s a new career path that’s opening up a ton of opportunity for the folks that work for us. And we’re really excited about that.

Greg Lambert (07:45)
Just curious, Patrick, about the types of talent that you’re looking for. Is there a certain practice range? Are they, say, seventh-year or tenth-year lawyers? What’s the sweet spot you tend to look for in the talent?

Patrick Forquer (08:02)
We actually hire across the entire spectrum. We have everything from folks who’ve done a couple of years as an associate through partners who work with us and they tend to focus on obviously solving different problems. Especially within BigLaw, there’s so many different types of transactions and deals and matters and cases that are happening on a day-to-day basis and there are different needs, and how you train and enable a partner is very different from how you train and enable a first-year. And we just try to map practice area and sort of seniority to help solve the different problems we’re looking at so we can add the most value to the firms that we work with.

Greg Lambert (08:44)
You’re the revenue guy, so let’s talk a little money now.

Patrick Forquer (08:49)
Okay.

Greg Lambert (08:50)
I know that you guys have raised significant capital. I think I was looking at Series D so far. And you’ve gotten busy on the acquisition side including a recent acquisition of Wexler, which I think is like the fifth

Patrick Forquer (09:03)
Yeah.

Greg Lambert (09:04)
or sixth acquisition. So, instead of asking you specifically about the individual acquisitions, what’s kind of the strategy that you have when you’re looking at acquiring and building through the acquisition process?

Patrick Forquer (09:21)
Yeah, for sure. It’s a couple of different things. First and foremost, we’re hiring amazing talent. And so the folks that are coming into the business through this acquisition are high-slope, highly intelligent, hardworking folks that we want to work with. And they’ve built platforms that are solving big problems that are relevant to our customers and the firms we work with.

In this world, as you know, there’s so much going on in legal tech. There’s so much noise and so much chatter. A lot of times when you’re comparing sort of legal AI platform A to legal platform B, you’re looking at it in terms of like apples and apples, right? It’s like you’ve got drafting, we’ve got drafting, how are we doing it? How are they doing it?

And so on and so forth. So part of the strategy also is we want to be a wide application layer for the firms we work with and a platform that any practice area could work within, but we also want to be really deep. The deeper the capabilities we add, for example the litigation capabilities Wexler provides, the more we can be both wide and deep and that sort of keeps the comparison from an apples-to-apples comparison. So Max likes to make the comparison of apples to fruit salad. And so we want to add more of these bespoke, deep levels of capabilities.

We bought Cadastral for real estate work. We’ve got Wexler for litigation work. We brought a company called Graceview that we integrated into the platform in under 60 days for regulatory horizon scanning as an example. So not only do we want to be able to work with every type of lawyer, we don’t want to just do surface-level work. We want to go deep within each practice area and within each area.

So it’s really important to us that we provide the highest level of value possible. And we’re able to do that through our M&A strategy. And so we’re trying to provide more value and more bang for your buck when you work with Legora.

Greg Lambert (11:12)
Yeah. And when you’re doing these acquisitions, you talked about making it both wide and deep. Are you really looking at kind of becoming almost like a matter management platform where the attorneys are constantly working within the Legora platform over a wide range of activities? It’s not just legal research or document drafting. It may be directly accessing your document management system and bringing things in and working with them there or maybe even the accounting system.

How wide and deep are you going to go?

Patrick Forquer (11:49)
Yeah. With any type of work that you do, no one likes bouncing around to different systems to do different things. And insofar as we can add capabilities to our platform that make sense for the type of work that we’re doing, we’re going to look at it. And certainly for us, we want to look at M&A as an accelerant to adding value and keeping folks spending more time in the platform. So yeah, we’d love for you to be able to do an end-to-end M&A due diligence process, for example, in Legora.

Many firms are doing that today. And actually, that was our fifth acquisition of the year. And we’ve run all those acquisitions on Legora. And we did one of those acquisitions in 13 days, using our product to run the M&A due diligence. Obviously, we work with some amazing law firms on that as well.

So it’s been really fun to see it in action. The more capabilities that we have, the longer folks are going to spend in Legora, and that’s good for them and good for us.

Marlene Gebauer (12:46)
So let’s keep talking a bit about the workflows. We’ve certainly moved from asking, “Can AI draft or summarize?” And now we’re really talking more about these agents that can execute multi-step automated legal work. We hear a lot about it, and I’m sure that there are pockets where it’s done. Which legal workflows are genuinely ready for that sort of next step today, and where do you think it’s still a little bit of theater?

Patrick Forquer (13:22)
Yeah. Well, it’s a great call out, Marlene. From my perspective, you hear a lot about agents out in the market. Everyone’s got an agent doing X, Y, or Z. But if you really look under the hood, a common pitch is, “We have this number of agents.” Anyone who tells you they have like hundreds of agents, I think is perhaps misdirecting a bit.

So, is it a repackaged workflow? Workflows have existed for a long time. n8n technology has existed for a long time. And then there’s lots of different workflow tools that you can buy that have if-then logic. Are you buying something that’s a repackaged workflow or are you buying something that’s truly agentic? And for us, the way to think about it would be

Marlene Gebauer (14:04)
And maybe explain what’s truly agentic versus not.

Patrick Forquer (14:08)
Yeah, so the agent in Legora, like the Agent OS that we launched earlier this year, it can click every button you can click in the platform. So it’s not just a set of rules that it’s following, like “if this, then do this; if that, then do that,” which you have to then go in and like edit those rules or that logic within a particular pre-baked package. With the agent, the big difference is you’re sort of moving from prompt engineering to context engineering, right? So with agentic work, you want to give it a definable scope. You also want to have the data and the sources made available within the project in Legora.

So everything that you’re going to be doing is grounded in source and truth. And then you also want to have a human in the loop at each step to customize and make sure everything is working like you want it to. So if you give the agent a goal in our system, and then you give it some context of that goal around like what it is you’re trying to do, sort of guardrails and things to watch for, then our agent is going to come back and it’s going to ask you some questions, some clarifying questions around the scope, around the goal. It’s going to look through the documents and make sure it has all the context it needs to achieve the goal. Then it’s going to present you with a bespoke plan for that particular goal that you’ve outlined and you can layer in skills, tools, and resources within Legora to make it even more bespoke and more granular to improve the work product.

The plan that it’s showing you is then the prompt. So in the old school world, the “Improve Your Prompt” button was a big feature that we still have that people like, but with an agent, you don’t really need the “Improve Your Prompt” button as long as you give it enough context with a very well-defined goal and all the resources that it needs. It’ll create the plan. And the plan is in many cases a long-horizon task that can run for 20, 30, 60, 120 minutes in one go to solve these increasingly complex problems. As long as as it has access to all the legal skills that we provide it in the tool, all the context you give from the firm or from your client documents, the agent can then go run these long-horizon tasks and solve increasingly complex problems instead of saying, “Go run this four-step workflow” that was pre-baked when I logged in.

And so it’s quite different and it’s extremely flexible and bespoke to the particular problem that you’re working on. That education, though, and that sort of change have been a journey. These are new capabilities that only really emerged as the new models came out around Christmas and the beginning of this year. And so we’ve had to educate our teams on what they can do, what they can’t do. M&A due diligence, Marlene, to answer your question specifically, is an area where we’re seeing an increasing amount of that work being able to get done with the agentic work.

And we’re hoping there are increased capabilities with Wexler as we sort of bake their capabilities in the platform over time that you’ll be able to do that in the long run. But yeah, we’re still working on this together and figuring it out with our customers.

Marlene Gebauer (17:22)
Thanks for thinking of the litigators.

Patrick Forquer (17:24)
Yes, of course. Always thinking of the litigators.

Greg Lambert (17:28)
You were speaking about things coming out over the Christmas break. It was kind of funny because I was talking with one of our AI-forward partners that had developed a process earlier in the year, and he was sending an update and then one of the other partners was like, “Do you realize it’s only been six months since you created this?” And we were all

Patrick Forquer (17:49)
Yeah.

Greg Lambert (17:49)
like, “Holy smokes, is that all it’s been? It feels like forever ago.” I’m wondering, sticking with the agents, and especially with all the transformation we’re seeing, I have a two-sided question. What are you finding that law firms may be a little slow on when it comes to putting an agent to work? And then for those that may be overachievers, what are some things they think it can do that it’s not ready for yet?

Patrick Forquer (18:21)
Yeah, that’s a great question, Greg. I think one thing is to be transparent. The rate of change in AI capabilities, in some cases, outstrips what the organizations we work with are able to absorb. The change is happening so fast. We all have to be transparent and honest about the fact that these things are changing.

What you could do six months ago or the thing you designed six months ago may not work the same way. There are all kinds of organizational questions around that, like approvals to use different capabilities and all the risk and the training and the change management. So I always tell folks, if you’re using AI today only to summarize documents and draft emails, you’re probably not doing enough, right? But in

Greg Lambert (19:09)
Yeah, yeah.

Patrick Forquer (19:10)
many ways, the question is: Does the agent have access to all the context and data and resources that it needs to complete the task? We’re seeing an increase in requests for things like MCP builds. So we have a team of folks, for example, that can build secure MCP access to different systems that’ll enable multi-system execution across a number of different areas, but you have to have the right security and guardrails around data governance in place before you can do that. I think a lot of folks think, “I can eventually just tell the agent, go do this thing, and it’ll figure it out.” And the agents, in theory, have the capability to do that. But do you have that sort of matter-level isolation?

Do you have the document-level permissioning structure down? Have you given it enough context and enabled it with your firm’s embedded skills and context so it knows, “This is how we do this particular piece of legal research,” or, “This is how we do this particular type of drafting,” so it can produce content that’s in line with your expectations? And that’s a journey. Again, going back to our legal engineers, that’s why we have so many legal engineers, so that we can work with our clients in a more embedded way and help them develop those more complex use cases. Does it have access to all the skills and content that it needs to produce the output at the quality that you want?

Marlene Gebauer (20:34)
We’ve heard in the past that a lot of AI companies are saying lawyers are saving time using the tools. But we’re also starting to hear, because of that, how are we sort of looking at the economics of legal services? Danielle Benecke, on the podcast Max was on, was saying that they do this by outcome. There are a lot of different ways to do this. Her team works directly with clients.

So, when are we going to start talking about how to measure the changing economics of legal services and how are we going to price things when using AI? I think people want to talk about it, but I don’t know that people are ready to do anything about it. So I’m curious what your thoughts are on that.

Patrick Forquer (21:27)
Yeah, well, look, we could spend the whole podcast talking about this. We could probably spend all dinner

Marlene Gebauer (21:31)
True.

Patrick Forquer (21:31)
and all night talking about all that stuff too. And certainly I’m not here to tell lawyers how to price and package their work. But I have some thoughts on what you were going through. First, I think it’s a misnomer just to think about AI in terms of efficiency, right? If you’re using AI only to save time on things, I think that’s mostly the way the first wave of AI worked.

People are doing that sort of look back, right? I used to do this thing this way and it took me 10 hours, and now I do it with AI and it took me 30 minutes. That type of stuff has existed for a while. And I don’t think that’s particularly exciting to the market anymore. What we try to look at in terms of a look forward is, okay, if you’re commoditizing certain types of work product that used to take you 10 hours and now it takes you 30 minutes in terms of time, then what does that enable in terms of quality of output?

What does that enable in terms of adding new features or new services that maybe you couldn’t have done previously? What can you package or repackage as part of new work product that wasn’t available? So there are all kinds of 50-state surveys and all these different examples of things that would have been too onerous in a world pre-AI that you can now deliver in a really effective way using platforms like Legora. And when we think about value, anytime you’re looking at an economic system more broadly, if certain pieces of the inputs into that system are getting commoditized in terms of time, then what you typically see in other industries is that the ecosystem and the economy itself gets bigger. So we think there’ll be more legal work, that there will be more legal services, that this is accretive to the overall market.

And it’s a really exciting time to be a lawyer and be working in legal services. And, as I’m sure you both know, like if you talk to any litigation partner, like they’re busier than ever, right? There are more cases coming in, there’s more work to do. People talk about time as it relates to you know the billable hour. But I was talking to a CLO of a big company a few weeks ago and he was mentioning, if we were working with an external counsel and they used to say, “Hey, if we can do this M&A deal in six months,” today they might say, “Maybe we can do it in three or four months.” Is it more or less valuable to do it in three months than six months?

Again, I’m not here to answer that question for folks or tell people how to price things, but I certainly think there’s an interesting conversation to be had around value. And if you’ve got an amazing attorney today who’s working just with Microsoft Word and Outlook, and you give them a platform like Legora, is that attorney more or less valuable to their clients? And what are the economics of that work? Certainly I have every confidence that legal research went from analog to online, data rooms went from banker boxes in a conference room to platforms like Intralinks and Datasite, and paper moved to email. There’s been all kinds of these examples over time and legal services has only grown and thrived.

We fully expect that to be the case with AI. How that looks and the shape of it between lawyers and their clients, things will change and evolve for sure, but we think it’ll be very positive and that there’ll be a lot of opportunity.

Greg Lambert (24:56)
Before I go further into the economics, there was something that you mentioned in your last answer where you talked about change management being one of the hardest parts of the deployment. I read something earlier where I think Crowell & Moring was talking about how they had a 91 percent attorney adoption rate. A year ago, we were fighting to tell people whether it was safe to use these tools or not, and now we’re

Patrick Forquer (25:23)
Yeah, yeah. Yeah.

Greg Lambert (25:25)
looking at 91 percent adoption. And I think they said they also had 70 percent-plus weekly usage within like six months of deployment. So I’m curious when you do these types of rollouts, what are the success metrics Legora has? I can look at metrics and I can see people have logged in or people have run prompts, but I don’t necessarily see

Marlene Gebauer (25:52)
Or the skills they’ve used. Yeah.

Greg Lambert (25:54)
Yeah, I don’t necessarily see some of the details. So what are you looking at on your side as far as success and how your users are using the product?

Patrick Forquer (26:05)
It’s a great question. And this is an area where we’ve evolved and grown so much over the past few years. And it’s a huge emphasis for me. It’s one of the top three things that I focus on every day when I go to work. How are we doing on the implementations that we have active?

And we actually have an entire team dedicated to this particular piece of our customer engagement. So this isn’t something that’s done by sales or our post-sales engagement team. We have a team that’s dedicated and focused on this. We’re continuing to evolve our strategies as we learn. We’ve done thousands of these now, so there are a couple of areas to think about.

For us, the speed and quality of implementation is the number one leading indicator of client satisfaction and overall renewal metrics. So we look at whether we got it done in the timeline we had agreed to with the client, right? So we look at speed. Did we get it done on time? And that time varies by client, by size, by the type of implementation.

We have different implementation options. We run through those and collaborate with our clients to create bespoke plans for all these big rollouts that we do. And then, of course, we look at a ton of data. And as you mentioned, Greg, for us, we don’t look much at activated licenses or number of logins. What we really look for is deeper than that.

Greg Lambert (27:32)
Those vanity metrics are really nice.

Marlene Gebauer (27:36)
Love it.

Patrick Forquer (27:36)
Of course, monthly active usage is nice. But we want daily active usage. And we don’t only want daily active users. Going back to my earlier sort of anecdote. If folks are coming in and only summarizing documents or saying, “Respond to this email for me,” that’s relatively shallow usage, and it’s easily commodified across other platforms.

We know that if folks are using more of our products and doing more complex tasks in Legora, and, as Marlene mentioned, are they using skills? Are they using playbooks? Are they using extraction templates? How many parts of the product are they using? What parts of the product are they using?

So we have a product called Tabular Review. Tabular Review gives us the ability to do bulk document review and extraction. When I first saw Tabular Review and met Max, I thought, “My gosh, this is amazing, and this is going to change the way work gets done. Period. The end.” If people use Tabular Review, they have their light bulb moment with AI.

It’s like, “My gosh, I can get all this data out of this big document set and then turn that into a RAG that I can draft, review, research, and so on, on top of with the agent.” And it unlocks an incredible amount of complexity in terms of the things that you can do. Those are the types of things we look at. Of course, we look at monthly and daily active usage, but we also look at quality of usage. So we have a number of different mechanisms and nomenclature we use internally. We might see that Marlene logged in a certain number of times this week and used a certain number of tools within the platform.

We can’t see the data or prompts, but we can see the tools she’s using. That’s the deeper level of analysis that we look at. And typically when we see certain feature usage, we know that we’re going to have a happy customer who’s solving big problems and getting great results from Legora.

Greg Lambert (29:31)
Now I’m going to hit you with the hard questions so…

Patrick Forquer (29:34)
Great.

Greg Lambert (29:35)
Yeah, butter you up.

Marlene Gebauer (29:35)
We save them for the end. We butter you up, and then we save them for the end.

Patrick Forquer (29:37)
[Laughs]

Greg Lambert (29:41)
and then hit you.

Marlene Gebauer (29:42)
Yeah.

Greg Lambert (29:43)
So a few weeks ago you announced that Agent Pro, which is one of the high-end tools in Legora, is moving to consumption-based pricing. The more tokens you use, the more it’s going to cost. And I think this was not a surprise to the industry that this was coming, but you were one of the first to move to this. So as a customer of Legora, what should people be thinking about as far as planning? It’s easy to plan per-seat pricing because you know what the cost is going to be.

When you start thinking about tokens, credits, or consumption-based pricing, how are you coaching your clients on how to approach that?

Patrick Forquer (30:27)
Yeah, it’s definitely a model from a technology provider that’s newer for legal tech. It’s used very heavily in other databases and technology systems, as you know. So for us, there are pros and cons to every pricing model. So on a per-seat model, the question we used to get was, “Okay, if you buy 1,000 seats from Legora, but what if these people don’t use it? What if Marlene goes on vacation and it’s sitting there?”

Greg Lambert (30:52)
Can I get a refund on that?

Patrick Forquer (30:54)
Yeah. “Do I get credit back if she doesn’t use it or something like that?” I can tell you that there’s no perfect pricing model. There are pros and cons to everything. The second thing is, look, this is new, this is a journey that we’re all on together. We try to lead with a bunch of empathy and understanding and try to be extremely collaborative and transparent with our clients as we work through this together.

I’ve worked at companies that sold consumption before. There are some key tenets I like to think about. First, we have to be able to predict consumption across various scenarios pretty well. The example I always use is: if we’re going to sell you 100 licenses, sorry, 100 credits of Legora. And to be clear, we’re not simply taking the tokens we use and just adding a margin on that and doing cost-plus pricing.

The credits are an amalgamation of different document API and token usage in the platform. So we don’t simply take the number of tokens that you used on a given day and add some money on top of that. But if I sell you 100 credits of Legora and then three months in you use 1,000 and I send you a surprise bill for 900 tokens, sorry, 900 credits, guess what? You’re going to get super pissed and you’ll probably churn, right? That’s an easy way to lose a customer.

That’s not a good scenario to be in. On the flip side of that, if I sell you 1,000 credits and you’re very happy, and then you use 100, and then you renew your contract at 100 credits. If we take a 900-credit churn, that’s bad for us. And then our investors are like, “What the heck? You just took a 90 percent churn on this very happy customer.

What’s going on?” So it aligns incentives in a really nice way. It aligns incentives around whether you’re using the product and whether we’re able to accurately predict that. Does the usage account for experimentation and different usage patterns? And do certain user types use the product differently than others? Are we able to report on that?

Are we providing visibility and governance? So, can we do user-level caps? In our case, you can put usage metrics against a particular project or matter in Legora. So you have governance and control you can put in place to make everyone comfortable and prevent certain situations from happening. But at the end of the day, it aligns incentives in a really nice way.

And if we’re able to predict the way we should, and communicate your consumption on a weekly, monthly, and real-time basis, so you know exactly how much you’re using and the patterns we’ve laid out hold, then you typically get to a good outcome. At the end of the day, I always ask our teams: for every dollar of Legora you buy, like what do our clients get? For every credit of Legora you use, what do you get in the outcome? Is it only efficiency, or do we deliver the other value-added benefits that we were talking about? And one of the more exciting things for the second half of this year is that we’ve hired a value engineering team who are working with our customers on what is the ROI of working with a platform like Legora.

And it can’t simply be, “It saved Greg some time on something.” It’s got to be more robust. We’ve done some work with Ari Kaplan on this and published some reports, but this is really the first inning on this stuff. But if we can help our customers tell a narrative, tell a story with data showing that for every dollar of Legora we put in, this is what we get out, and we can do that in a way where we’re communicating and executing on the consumption pricing as I described, then I think it’ll be fine. But it’s new, and we’re working through it with our clients in a really collaborative way. I think we’ll be in a good place.

And like you say, this is the direction of travel for the industry more broadly and I think we’ll be in a good space.

Greg Lambert (34:57)
I think one of the questions I hear the most as we start looking at this, because it’s not just going to be Legora, there are going to be other

Patrick Forquer (35:06)
Yeah.

Greg Lambert (35:06)
AI companies that we work with that aren’t necessarily going to use credits and consumption, but instead use a pure model where if you use a million output tokens, you’re going to get charged a million output tokens. And if you let an inefficient process go awry, that’s on you because they’re not upcharging you, they’re charging for what you use. Are you guys thinking about safeguards so that, if somebody sets up an inefficient agent that runs into a loop and can’t get out of it, it breaks, or are there some kind of safeguards that you set up?

Patrick Forquer (35:45)
Yeah, a hundred percent. We already have a lot of these in place and we’re continuing to invest in this area around model selection, model routing, right? You don’t need to use whatever the latest model is for every single task in Legora, right? So model selection, model routing, guardrails against everything from prompt injection, matter-level isolation, all these different things. We we have to have a lot of guardrails in place in the product which we already have, and then we’re continuing to build what we need to make our clients feel more comfortable using Agent Pro in particular.

Marlene Gebauer (36:22)
So Patrick, we have a new question. We sort of look backward to the present and then for our crystal ball, we look for the future. So this is sort of looking backward and looking to the present. What’s one thing that strikes you that’s different today than last year in our industry?

Patrick Forquer (36:43)
I’m glad you asked because I was actually looking back on Max’s interview with you guys last year, and he made the prediction about these long-horizon agents. He said, “They’re coming. It’s going to come faster than you think.” Before we had the agent, we had the assistant. And the assistant could do a couple of steps of a workflow. I think our workflow builder capped out at about five steps because the prior models would run out of gas.

They get what they call context anxiety, right? And wrap something up before getting to the end. The ability for the agents to do increasingly complex long-horizon tasks is here. And it’s something that we were working toward last year, but the reality was we were still in workflow mode. Last summer we were in workflow mode, whereas this summer we’re in Agent OS, with all the things we were just talking about.

So that’s been really exciting to see that come to life. More importantly, we’re seeing our clients use it. And I hear from clients every day about something cool they did with Agent that they never would have been able to do. I was with a client yesterday down in Atlanta. He was talking to me about some exciting work he was doing for a business development use case.

It generated a deck he was excited to use in a pitch meeting. I think the agentic revolution, so to speak, is here and it’s all new and we’re figuring out new applications, new use cases for it every day, even those of us who work at Legora. I was in a demo the other day and one of our legal engineers asked the agent to not only redline a document, but create an HTML page of the issues list based on the redline. That way, the lawyer could have a visual representation they could share with their client based on the redline it had generated for the customer. I honestly didn’t know Legora could generate HTML like that.

I thought, “That’s so cool. I didn’t know

Marlene Gebauer (38:41)
Mm-hmm.

Patrick Forquer (38:41)
that.” And so there are all these things that we’re uncovering and, as you can tell, I get excited about it. Our team does too.

Greg Lambert (38:49)
Yeah, I heard someone say HTML is now the new markdown for agentic AI tools especially.

Patrick Forquer (38:58)
Yeah, for sure.

Greg Lambert (39:00)
So let’s jump to the crystal ball question and as we told you in prepping for the interview, you can answer this however you want, but I’m going to phrase it a little bit differently this time because I think this is an interesting and a little bit selfish of me to do this. Let’s say you and I swapped roles. You came in as a chief innovation officer in an AmLaw firm. What’s something that you think someone like me should prepare for over the next few months or a few years?

Patrick Forquer (39:36)
Well, I mean, I think your jobs will be increasingly essential to the success of the firms that you work with. And we think it’s really exciting. Yes, more money. More money. Well,

Greg Lambert (39:46)
So you’re saying we’ll make more money, right?

Marlene Gebauer (39:48)
[Laughs]

Patrick Forquer (39:50)
Hey, man, I read LinkedIn too. There’s a lot of stuff going on in knowledge management these days.

Marlene Gebauer (39:53)
Send that out to the…

Greg Lambert (39:57)
Yeah, we will neither confirm nor deny that we’ve passed that around to…

Patrick Forquer (40:02)
Yeah. It’s a talent war out there right now. But we might have to talk afterwards, Greg. I think the first thing is, and we’ve encountered this because we use Legora internally as well, and we use a number of AI tools. But the layer that gets often overlooked is the data layer.

And with all the talk about agents and all these different things, as we were discussing earlier in response to Marlene’s question, where does the data sit that you need to go execute these long-horizon tasks? And how do we teach systems thinking and design thinking to our internal teams? And maybe that’s the lawyers, or maybe that’s part of the KM function around how we think in terms of these step-by-step workflows that need access to specific pieces of context. The more context you give agents, the better they do, right? But accessing that context within the firm, for many reasons, is such a challenge.

We talk to firms all the time about this. It’s about getting the data in a way that’s secure within the right governance frameworks, but also understanding what your risk tolerance is for the different use cases that you want to do with different agents. That data layer is more important than ever. And that’s an area where our legal engineers are constantly collaborating and working with our customers to understand how it exists today and what we can change to make the agent more powerful. So the data layer is definitely an area that I would look at.

And I do think, going back to the legal engineering sort of type of conversation, that there are all kinds of new roles and new capabilities within those roles that are going to be available. So making sure you have the right team in place probably means I would ask for a bunch more headcount, Greg and Marlene, as I’m

Greg Lambert (41:54)
Mm.

Patrick Forquer (41:55)
sure you’re always doing yourselves and all the knowledge management leaders we work with, doing the same. There are new skills and new capabilities around AI specifically, data governance specifically, that I would want to hire for and make sure that we had that in-house so we could move faster on the AI adoption curve. Without this sort of data layer and without the talent in place, you’re going to be limited in the complexity of the tasks that you can do. And again, we’re all on this journey together as we sort of try to adapt these increasingly advanced capabilities and technologies into our platforms. And I think from our side, one thing that Legora wants to do a much better job of is to leverage the collective knowledge from our peers.

And that’s why I love this podcast and what you guys are doing because there’s so many smart people out there trying to solve these problems. But it can’t be KM or innovation alone. You need partners, customers, KM, innovation, and you need everyone on the same page. Can we align on what the goals and intentions are of the firm as it relates to AI? What are your expectations from us?

We need to make that alignment clear. For the people I talk to who do what you guys do, getting stakeholder alignment is difficult. Everyone has a perspective. People come from different places and may have higher or lower risk tolerance. But at the end of the day, you’re held to a standard, right?

You’re held to a level of expectation. And sometimes you have a great opportunity to go hit that expectation, and sometimes you’re limited because of the constraints put on you by the business. But I’m not sure everyone always appreciates that. From Legora’s side, our job is to try to bring those folks together so people have a deeper understanding of the challenges so that we can unlock the true potential here. Those are some things I would think about.

And you’re probably thinking, “I’ve already thought of all those things. This guy.”

Greg Lambert (43:50)
All right. Nope, I’m writing them all down. That’ll go in my monthly report now.

Marlene Gebauer (43:53)
We’re taking notes, man.

Patrick Forquer (43:59)
I know I’m not telling you anything you don’t know, but just to reinforce, as I’m sure you guys have talked about many times.

Greg Lambert (44:06)
Well, I appreciate that. And Patrick Forquer, CRO at Legora, thank you very much for taking the time to talk with us today. We’ve covered a lot of ground here and it’s been a lot of fun. Thanks.

Marlene Gebauer (44:19)
Thank you, Patrick.

Patrick Forquer (44:19)
Thanks, Marlene. Thanks, Greg.

Marlene Gebauer (44:21)
And thanks to all our listeners for listening to The Geek in Review. If you enjoyed the show, share it with a friend, share it with a colleague, and we’d love to hear from you on LinkedIn and Substack.

Greg Lambert (44:32)
And Patrick, where’s the best place for listeners to reach out and learn more about you and Legora?

Patrick Forquer (44:38)
So you can send me an email anytime, patrick@legora.com. We’ve got an amazing website, https://legora.com, and our LinkedIn page has all the latest announcements from the company. So give us a follow. Send me a LinkedIn connection or an email. We’d love to chat with your listeners, and I appreciate everyone listening today.

Marlene Gebauer (44:55)
And as always, the music you hear is from Jerry David DeCicca. Thank you so much, Jerry, and goodbye, everybody.

Patrick Forquer (45:02)
See you.