1 00:00:02,616 --> 00:00:04,907 Moiz, thanks for joining me this afternoon. 2 00:00:05,996 --> 00:00:07,871 My pleasure, good to be on Ted. 3 00:00:08,376 --> 00:00:08,846 Awesome. 4 00:00:08,846 --> 00:00:12,178 Well, you and I got connected on LinkedIn. 5 00:00:12,178 --> 00:00:25,493 forget we were chiming in on a thread or something or another, but I saw your, what you're up to now, which I thought was super interesting with scorelytics, your founder and CEO. 6 00:00:25,493 --> 00:00:29,240 And you were with, Baker McKenzie. 7 00:00:29,240 --> 00:00:32,176 You got a background in the big four. 8 00:00:32,176 --> 00:00:36,034 Why don't you just us, just give us kind of a quick rundown of your 9 00:00:36,034 --> 00:00:39,649 your background and then what you're up to today with scorelytics. 10 00:00:40,556 --> 00:00:43,017 Yeah, yeah, thank you again, Ted, for having me. 11 00:00:43,017 --> 00:00:50,699 Yeah, so I've been, like I said, working with the big four, first part of my career for about nine years. 12 00:00:50,699 --> 00:01:00,132 And then I joined Baker McKenzie about, I guess, 15 years ago uh in the economic consulting group. 13 00:01:00,132 --> 00:01:06,434 So Baker McKenzie is one of the few law firms that has a large group of non-lawyers within it. 14 00:01:06,434 --> 00:01:08,384 So mostly economists. 15 00:01:08,514 --> 00:01:11,915 MBAs, PhDs, and so forth. 16 00:01:12,396 --> 00:01:20,750 So my experience is really, both on the consulting side, but also working uh at a big law firm, understanding how the law firm works. 17 00:01:20,750 --> 00:01:26,042 Obviously, I'm not a lawyer, but my role was to work with lawyers on a day-to-day basis. 18 00:01:26,042 --> 00:01:35,756 And as part of that effort, we started to look at what are ways that legal services can be provided to clients in a way that's 19 00:01:36,066 --> 00:01:41,769 considering non-legal activities like economics and data analytics and so forth. 20 00:01:41,769 --> 00:01:44,351 And that was helping to sort of lead that effort. 21 00:01:44,351 --> 00:01:50,314 And that's ultimately what led to scorelytics and the spinoff from Baker McKenzie. 22 00:01:50,770 --> 00:01:52,899 And tell us what scorelytics does. 23 00:01:53,600 --> 00:02:09,014 Yeah, so scorelytics is a platform that really tries to solve the problem that many lawyers, whether they're in-house at law firms as well, to really make legal intelligence 24 00:02:09,014 --> 00:02:09,694 actionable. 25 00:02:09,694 --> 00:02:16,035 So what I mean by that is there are situations where a business has a really big decision to make. 26 00:02:16,035 --> 00:02:19,230 It could be a merger and acquisition. 27 00:02:19,230 --> 00:02:23,467 It could be choosing to go into a new market, to exit a market. 28 00:02:23,763 --> 00:02:29,527 or just dealing with new regulations and the evolving landscape that's happening so quickly, right? 29 00:02:29,527 --> 00:02:31,138 And that's only accelerated. 30 00:02:31,478 --> 00:02:41,935 And one of the issues there has always been not only getting that information in a timely way, but also making that information so that it's actionable for the broader business, 31 00:02:41,935 --> 00:02:42,186 right? 32 00:02:42,186 --> 00:02:52,052 So legal teams want to engage with leaders in the company, whether it's the C-suite, the board of directors, their shareholders, et cetera, in a way that says, okay, this is an 33 00:02:52,052 --> 00:02:53,453 emerging issue. 34 00:02:53,749 --> 00:02:55,450 But why is it really important? 35 00:02:55,450 --> 00:03:06,132 And what we do is we say, we can not only track those developments in real time, but we can also translate those into what is the financial impact that particular development has 36 00:03:06,132 --> 00:03:06,753 on a company? 37 00:03:06,753 --> 00:03:08,273 Is it a $200 million issue? 38 00:03:08,273 --> 00:03:10,033 Is it billion dollar issue? 39 00:03:10,194 --> 00:03:20,456 So really looking at what the financial services industry has done for a long time when it comes to risk, putting risk in the context of quantitative uh data. 40 00:03:20,469 --> 00:03:23,693 And the score of it is what we say is that there's no reason why 41 00:03:23,829 --> 00:03:28,292 that same mentality or approach can be applied to legal risk. 42 00:03:28,292 --> 00:03:41,759 At end of the day, legal risks have a financial impact and legal teams are basically instructed to help the company save uh on revenue and potentially high litigation costs 43 00:03:41,759 --> 00:03:42,440 and so forth. 44 00:03:42,440 --> 00:03:45,451 So they are providing a financial benefit. 45 00:03:45,451 --> 00:03:52,235 So our RV was with scorelytics, why not take those legal risks and translate them into a quantitative measure that 46 00:03:52,363 --> 00:03:54,761 resonates with a broader organization. 47 00:03:55,088 --> 00:03:56,861 And who are your clients? 48 00:03:56,861 --> 00:03:59,635 it other law firms, inside counsel? 49 00:03:59,635 --> 00:04:03,500 Who buys your services or offerings? 50 00:04:03,731 --> 00:04:16,048 Yeah, so our clients are mostly large multinational companies because as you can imagine, for those companies, because they operate in so many different countries and are impacted 51 00:04:16,048 --> 00:04:24,632 by so many legal areas and developments that for them keeping track of all of that information is typically a challenge. 52 00:04:24,632 --> 00:04:30,406 Even though some of these companies may have really large in-house legal teams, they still struggle to keep up with the data. 53 00:04:30,406 --> 00:04:33,387 So our clients are large uh companies. 54 00:04:33,685 --> 00:04:36,886 mostly companies that operate uh globally. 55 00:04:36,886 --> 00:04:44,610 uh And then also law firms as well, because law firms are doing a lot of what we do typically using manual processes, right? 56 00:04:44,610 --> 00:04:53,073 They're looking at or trying to identify the latest trends in legal, but also trying to make that information actionable to their clients, right? 57 00:04:53,073 --> 00:04:58,135 And they're all trying to do that as efficiently as possible and add the most value to their clients. 58 00:04:58,135 --> 00:05:02,347 So law firms are also a big part of our client base. 59 00:05:02,638 --> 00:05:07,770 So are you a spin-off of, is Scorelytics a spin-off of Baker McKenzie? 60 00:05:07,770 --> 00:05:11,208 Is it a wholly owned subsidiary or are they sister companies? 61 00:05:11,208 --> 00:05:13,111 What is the structure? 62 00:05:13,119 --> 00:05:20,189 Yeah, so Baker McKenzie spun off Scoralytics in November of last year, so November 2024. 63 00:05:20,473 --> 00:05:23,385 Baker McKenzie continues to be a shareholder. 64 00:05:23,385 --> 00:05:26,617 own 30 % of the company. 65 00:05:26,977 --> 00:05:28,808 But they don't control the company, right? 66 00:05:28,808 --> 00:05:36,823 So we're independent as far as our ability to get outside financing from anybody else, to work with other law firms. 67 00:05:36,823 --> 00:05:40,429 So we have no limitations as far as working only with 68 00:05:40,429 --> 00:05:45,668 with Baker McKinsey, but yeah, the law firm owns 30 % of the company. 69 00:05:45,668 --> 00:05:46,768 Interesting. 70 00:05:46,888 --> 00:05:47,548 Yeah. 71 00:05:47,548 --> 00:05:59,128 and what was the motivation behind spinning off versus keeping you and your team and what you do in-house at the firm? 72 00:05:59,950 --> 00:06:07,270 Yeah, I think this is part of a broader question also about how law firms approach innovation. 73 00:06:07,670 --> 00:06:19,490 So I think the story within Baker McKinsey itself was that the firm obviously had some experience in terms of internal innovation and tools that were developed. 74 00:06:20,010 --> 00:06:28,350 Most of those tools, frankly, were for internal use as far as making processes more efficient around contracts and so forth. 75 00:06:28,365 --> 00:06:36,565 And with scorelytics, because it is sort of a different solution in terms of, you know, it's the risk, legal risk assessment solution. 76 00:06:36,925 --> 00:06:41,725 That was part of the conversation as well is that, you this solution is a tech solution. 77 00:06:41,905 --> 00:06:48,965 Perhaps it has a much larger potential than just within one firm. 78 00:06:49,685 --> 00:06:53,345 But also I think there was the question about technology and AI. 79 00:06:53,345 --> 00:06:56,811 So as you can imagine, you know, lot of law firms are 80 00:06:56,811 --> 00:07:03,585 looking at AI very, very closely and about the governance around the AI, how should it be deployed? 81 00:07:03,705 --> 00:07:07,498 What are the risks associated with AI and so forth? 82 00:07:07,498 --> 00:07:16,493 So that was part of the conversation as well, is to say, well, this is a technology company, but it is using AI and it will be using AI more and more. 83 00:07:16,694 --> 00:07:22,667 And is it better for the firm to try to address those questions and keep it in-house? 84 00:07:22,685 --> 00:07:25,858 or let it be what it should be, which is a tech company, right? 85 00:07:25,858 --> 00:07:30,672 There are tech companies that are operating in the space of AI and they're dealing with these questions. 86 00:07:30,672 --> 00:07:33,684 So that was part of the reasoning as well. 87 00:07:33,684 --> 00:07:45,664 But I think ultimately the decision was that for Baker McKinsey it's better to be an investor uh and to benefit from the growth of the company with help from other investors 88 00:07:45,664 --> 00:07:48,727 and other stakeholders out there who can help the company grow. 89 00:07:48,727 --> 00:07:51,969 Then you try to keep it in-house and only to serve 90 00:07:51,969 --> 00:07:53,355 the firm's clients. 91 00:07:53,560 --> 00:07:57,981 There have been a few of these I've seen in legal throughout the years. 92 00:07:57,981 --> 00:08:03,863 I'm not sure if you've heard of Cypharth Lean or Cleary X. 93 00:08:03,863 --> 00:08:09,334 um But, you know, it sounds like kind of a similar path. 94 00:08:09,334 --> 00:08:16,076 I remember now how you and I started um chatting. 95 00:08:16,076 --> 00:08:21,258 We were talking about the big four and the partnership model and some of the limitations around that. 96 00:08:21,258 --> 00:08:21,988 I'm... 97 00:08:23,170 --> 00:08:40,926 I'm a believer in that the partnership model creates a lot of friction towards scale and R and D and partnerships are, especially partnerships operating on a cash basis are 98 00:08:40,926 --> 00:08:44,859 optimized for profits, right? 99 00:08:44,859 --> 00:08:49,843 um Amortizing capital expenditures over 100 00:08:50,360 --> 00:08:55,463 periods of time is not something that you see in big law typically. 101 00:08:55,463 --> 00:09:12,592 I think that in consensus driven decision making, my listeners have heard me talk many times about um also just the governance mechanisms around partnerships versus more formal, 102 00:09:12,592 --> 00:09:19,236 um big companies or C corps and they have boards of directors and they have 103 00:09:19,296 --> 00:09:32,165 management that's installed with you know uh c-suite executives that aren't necessarily practitioners and they have internal audit and risk management functions and all of those 104 00:09:32,165 --> 00:09:45,885 governance mechanisms allow companies to scale and there isn't a law firm on planet earth that would qualify to be in the fortune 500 today uh k &e is really close um but they're a 105 00:09:45,885 --> 00:09:47,466 bit of an outlier they're 106 00:09:47,716 --> 00:09:50,056 I haven't got their 2025 numbers. 107 00:09:50,056 --> 00:09:54,756 Their 2024 revenue numbers were in the low seven billions. 108 00:09:54,756 --> 00:09:59,036 I think it's mid to high seven billions is the floor of the fortune 500. 109 00:09:59,956 --> 00:10:02,796 I'm a believer and I'd love to get your take on this. 110 00:10:02,796 --> 00:10:10,316 I'm a believer that scale is going to be important in this next iteration of law. 111 00:10:10,836 --> 00:10:14,416 And the reason I think it's going to be important is because there's going to be investment. 112 00:10:14,416 --> 00:10:17,056 Like firms are going to have to differentiate themselves somehow. 113 00:10:17,092 --> 00:10:21,343 um Historically, it's been with their people, right? 114 00:10:21,343 --> 00:10:32,056 Hey, we have the best &A practice or we have the best IP practice and you know, we've got a legal resume and that's going to continue to be part of the equation. 115 00:10:32,056 --> 00:10:44,139 But I think when we get into this tech enabled legal service delivery world, technology is also going to be an ingredient in that equation and that requires some R &D. 116 00:10:44,139 --> 00:10:46,592 So I don't know, I feel like 117 00:10:46,592 --> 00:11:00,364 It's going to be for firms to, it seems like the bigger firms that accumulate some mass and some scale will have more resources to invest and make their tech better than the 118 00:11:00,364 --> 00:11:04,777 firms down the street and can enable new capabilities. 119 00:11:04,777 --> 00:11:06,869 Kind of like what you guys do. 120 00:11:06,869 --> 00:11:07,309 I don't know. 121 00:11:07,309 --> 00:11:10,912 is your take on all that I just said there? 122 00:11:11,149 --> 00:11:13,549 Yeah, no, think you're exactly right. 123 00:11:13,549 --> 00:11:25,889 I think the governance structure in these firms, while I think it does help these firms with risk management and of course, there's bar rules and all those things, I do think 124 00:11:25,889 --> 00:11:33,869 that it sort of makes it difficult to really innovate in the same sense that a company is innovating in Silicon Valley or elsewhere. 125 00:11:33,869 --> 00:11:37,749 are purely tech companies that are taking risks, right? 126 00:11:37,749 --> 00:11:39,609 They're validating 127 00:11:40,093 --> 00:11:44,014 hypotheses, they're testing out some fail, some succeed, right? 128 00:11:44,014 --> 00:11:52,534 That all of that process that goes into innovation is not necessarily one that fits into a law firm model. 129 00:11:52,754 --> 00:11:57,974 But at the same time, you know, there are massive benefits to law firms, to innovation, right? 130 00:11:57,974 --> 00:12:05,814 So, for example, there's a lot of discussion and I've seen many articles about how do law firms get to the $10,000 billable hour, right? 131 00:12:05,814 --> 00:12:08,929 I think you may have seen some of these as well. 132 00:12:08,929 --> 00:12:22,513 where in my views, a lot of the discussion was about, if you automate basic tasks, mostly using AI, then all of a sudden you can charge clients 10,000 per hour. 133 00:12:22,653 --> 00:12:35,117 But my view is that technology, it's kind like what we're doing is if law firms are able to identify the problems that their clients are facing, even before the problem actually 134 00:12:35,117 --> 00:12:35,853 happens. 135 00:12:35,853 --> 00:12:36,153 right? 136 00:12:36,153 --> 00:12:42,513 So more of the predictive analytics to say, there's a trend that's happening in the regulation and litigation enforcement. 137 00:12:42,513 --> 00:12:46,653 And you can proactively go to a client and get ahead of those. 138 00:12:46,653 --> 00:12:54,393 All of a sudden you've gone from maybe two immediate issues that you're advising a client on to perhaps 10, right? 139 00:12:54,393 --> 00:12:58,673 And also those 10 are going to be the highest priority, the highest financial impact. 140 00:12:58,673 --> 00:13:05,269 And that's in my view, how technology can really be used to get these law firms to that. 141 00:13:05,269 --> 00:13:10,433 sort of 10,000 available hour uh example. 142 00:13:10,433 --> 00:13:11,533 That's how you get to it, right? 143 00:13:11,533 --> 00:13:14,365 Is to say, well, we're identifying these things much earlier. 144 00:13:14,365 --> 00:13:21,260 We're being proactive by going to our clients instead of being reactive to issues the client may already face, right? 145 00:13:21,260 --> 00:13:22,391 So that's how you grow. 146 00:13:22,391 --> 00:13:25,723 That's how you become sort of a much larger operation. 147 00:13:25,723 --> 00:13:29,025 But also I think these firms, as you know, care a lot about the profit per partner number, right? 148 00:13:29,025 --> 00:13:32,588 So that's how you increase that number as well. 149 00:13:32,588 --> 00:13:34,774 And that's sort of in our approach is that 150 00:13:34,774 --> 00:13:39,938 A solution like ours really helps law firms get to that model. 151 00:13:39,938 --> 00:13:49,244 But also, gets clients to pay for those fees as well, because the client now sees that what is exactly the value that's being added. 152 00:13:49,244 --> 00:14:00,852 So law firms can say, helped you save $500 million, or we helped you do an M &A transaction three months faster, because due diligence was accelerated because of data and 153 00:14:00,852 --> 00:14:02,813 analytics and so forth. 154 00:14:03,049 --> 00:14:14,238 That's sort of my view, but I think you're absolutely right about the governance structure within law firms uh that that makes it difficult to do a lot of this testing, so to speak. 155 00:14:14,238 --> 00:14:18,709 Everybody wants uh a tool that can be sold in the next month. 156 00:14:18,709 --> 00:14:20,894 And we went through this with our journey as well. 157 00:14:20,894 --> 00:14:25,648 was like, yeah, you have a great idea, but I want to start selling it in three months. 158 00:14:25,648 --> 00:14:31,677 And we were like, to actually develop all that data across 100 countries, across all these areas of law. 159 00:14:31,677 --> 00:14:40,031 It requires a lot of time and effort, but also then you have to deploy the actual platform, train the AI, and do all of that, right? 160 00:14:40,031 --> 00:14:43,953 So I think there is a bit of that lack of patience on that process. 161 00:14:43,953 --> 00:14:46,674 Everybody wants immediate results, as you alluded to. 162 00:14:46,674 --> 00:14:55,896 And we can do that as an independent company, but I'm pretty sure if you were within any law firm, having that type of runway and patience would be a challenge. 163 00:14:55,896 --> 00:15:10,614 Yeah, I try not to pitch what we do here at InfoDash on the podcast very much because that's not the goal, I'm going to have to talk about where we're putting our chips on the 164 00:15:10,614 --> 00:15:11,584 table. 165 00:15:11,625 --> 00:15:24,772 So we think that from a differentiation perspective, firms are not going to differentiate themselves with tools, Like off the shelf tools, Harvey, co-counsel, Legora, you name it. 166 00:15:24,772 --> 00:15:25,624 If your firm 167 00:15:25,624 --> 00:15:27,895 down the street can buy it just like you can. 168 00:15:27,895 --> 00:15:29,546 It's not a differentiator. 169 00:15:29,767 --> 00:15:38,091 But I think what is going to create real differentiation besides just maybe scale is data. 170 00:15:38,612 --> 00:15:43,855 And we uh have a labor and employment firm who we're partnering with. 171 00:15:43,855 --> 00:15:45,596 We just launched our Extranet product. 172 00:15:45,596 --> 00:15:49,148 So historically we've been um Intranet. 173 00:15:49,148 --> 00:15:52,960 We've been working on Extranet for about two and a half years and we just launched it. 174 00:15:53,132 --> 00:15:58,973 And what it does is it gives us the ability to develop client facing AI solutions. 175 00:15:59,234 --> 00:16:02,455 And um one, this use case is really neat. 176 00:16:02,455 --> 00:16:03,755 I'd love to get your take on it. 177 00:16:03,755 --> 00:16:13,918 So they compile all of this labor and employment data across all 50 states and their clients pay them a subscription to log in and peruse it and get updates and all that sort 178 00:16:13,918 --> 00:16:14,658 of stuff. 179 00:16:14,658 --> 00:16:21,494 And we pitched, Hey, what if instead of that, you take that data and you've got all of their 180 00:16:21,494 --> 00:16:30,246 employment documents, their employee handbook, their employment contracts, and you take Azure Open AI, because we deploy our solution in their tenant. 181 00:16:30,246 --> 00:16:35,268 So we have access to all their data and we also have access to Microsoft's capabilities. 182 00:16:35,268 --> 00:16:38,369 So we use Azure Open AI and we flag exceptions. 183 00:16:38,489 --> 00:16:45,651 And then we create work items in Power Automate and route them to the attorneys responsible for those relationships. 184 00:16:45,651 --> 00:16:50,692 And proactively, you could go, hey, know, non-competes just got banned in 185 00:16:50,830 --> 00:17:00,365 California and you've got 97 employment agreements with non-compete language, it becomes a revenue opportunity in addition to it. 186 00:17:00,365 --> 00:17:03,817 And it allows law firms to get like proactive, right? 187 00:17:03,817 --> 00:17:13,233 Instead of reactive, where the client has to go and find the exceptions themselves and then bring it to the law firm. 188 00:17:13,233 --> 00:17:15,334 It puts the law firm in the driver's seat. 189 00:17:15,334 --> 00:17:17,335 So I think we're aligned. 190 00:17:17,335 --> 00:17:19,498 Like I really do think that data 191 00:17:19,498 --> 00:17:27,023 is going to be um a real important ingredient in how firms differentiate themselves and then how they use it. 192 00:17:27,023 --> 00:17:27,597 You agree? 193 00:17:27,597 --> 00:17:28,717 Yeah, totally. 194 00:17:28,717 --> 00:17:29,537 I totally agree. 195 00:17:29,537 --> 00:17:38,537 think that that's why you name it with its core litics, our focus for really the first eight months of our development was on data, right? 196 00:17:38,797 --> 00:17:44,737 We said, of course, all the discussions is about AI and how AI is going to change the world. 197 00:17:44,737 --> 00:17:51,817 And all of that is obviously true, but AI doesn't really do anything for you if your data is terrible, right? 198 00:17:51,817 --> 00:17:57,410 So of course, there's all this information about hallucinations, And there's lots of public. 199 00:17:57,410 --> 00:18:03,195 data about examples of cases that ended up in court filings that never really happened and so forth. 200 00:18:03,195 --> 00:18:15,245 So what we did is focused on the data to say, we're going to collect all of this data, but we're going to collect it from primary sources, so actual court documents, court filings, 201 00:18:15,645 --> 00:18:18,137 actually government sources, what we call primary sources. 202 00:18:18,137 --> 00:18:23,972 We're not going to rely on just what somebody else is saying about illegal development and have AI going to read that. 203 00:18:23,972 --> 00:18:24,959 So we spent. 204 00:18:24,959 --> 00:18:37,857 a lot of time on the data, but also we had lawyers work with us to actually review our AI's output and say, this the type of output that you would use if you were working on a 205 00:18:37,857 --> 00:18:39,508 case or you're rising a client? 206 00:18:39,508 --> 00:18:49,854 The same thing with in-house counsel to say, in terms of your workflows, have to present to your board, you have to present to your executives, you have to deal with other 207 00:18:49,854 --> 00:18:51,825 stakeholders within the business. 208 00:18:51,853 --> 00:18:57,413 how is our AI sort of able to produce the information that you need that fits into those workflows? 209 00:18:57,413 --> 00:19:05,333 And I think having spent those eight months validating all of those things makes everything else that we're doing much easier. 210 00:19:05,333 --> 00:19:11,173 But also I think in the legal space, as you probably know, trust is critical, right? 211 00:19:11,173 --> 00:19:20,941 Perhaps more so than even some other industries where it's like lawyers, the first reaction to AI is typically, is it trustworthy? 212 00:19:20,941 --> 00:19:23,701 Can I actually go in and validate the information? 213 00:19:23,701 --> 00:19:28,541 And what we also did is we actually got lawyers that, for example, litigated certain cases. 214 00:19:28,541 --> 00:19:41,521 And we said, can you review our AIs description of the case and timelines and all the other things that we're producing in our platform to confirm that everything is correct? 215 00:19:41,981 --> 00:19:48,181 And most of the time, the reaction we got was, your information is actually even more detailed than we would have thought you could have gotten. 216 00:19:48,181 --> 00:19:50,541 So we knew we had information. 217 00:19:50,770 --> 00:19:55,204 in our platform, they were like, I didn't know you could do that using AI, right? 218 00:19:55,204 --> 00:19:57,346 So it was very good feedback. 219 00:19:57,346 --> 00:20:00,518 But I think to your point, I totally agree with the approach that you described. 220 00:20:00,518 --> 00:20:05,552 em I more broadly, I think it's really about using AI to fit into workflows, right? 221 00:20:05,552 --> 00:20:16,141 To say, data is obviously the starting point, but how does that data actually, how is it presented to actually let somebody who's doing a particular task, not only do that task 222 00:20:16,141 --> 00:20:18,925 faster, but to make it more valuable. 223 00:20:18,925 --> 00:20:20,145 for the business, right? 224 00:20:20,145 --> 00:20:26,045 So your example was right on about the risk that you describe with respect to employment, right? 225 00:20:26,045 --> 00:20:31,165 Someone can say, well, this is something that's happened and here's how it actually impacts us. 226 00:20:31,165 --> 00:20:32,045 Why do I care? 227 00:20:32,045 --> 00:20:33,245 Why should I care? 228 00:20:33,245 --> 00:20:35,025 And why should others in the company care, right? 229 00:20:35,025 --> 00:20:39,245 So that's the critical part, I think, which I think we're both kind of tied into the same thing, right? 230 00:20:39,245 --> 00:20:42,965 So say, well, data has to be actionable and it has to be quantitative. 231 00:20:42,965 --> 00:20:46,605 So somebody knows how to prioritize this information, right? 232 00:20:46,605 --> 00:20:48,299 You can't just say the world's on fire. 233 00:20:48,299 --> 00:20:56,469 You have to say, are the top five things that matter to you and how do they rank right amongst those top five items. 234 00:20:56,622 --> 00:21:09,163 You know, I'm not sure if people, I know people in legal that have been around to multiple firms realize this, but I'm not sure people outside of legal or people who have only been 235 00:21:09,163 --> 00:21:16,109 at one or two firms realize how bad law firm data is in what shape it is. 236 00:21:16,109 --> 00:21:24,488 um And this goes across the spectrum from structured, you know, rows and columns, you know, 237 00:21:24,488 --> 00:21:27,039 very little like master data management. 238 00:21:27,039 --> 00:21:31,193 I was how this InfoDash and the predecessor company got started. 239 00:21:31,193 --> 00:21:31,954 I'm a data guy. 240 00:21:31,954 --> 00:21:36,617 I used to work at Microsoft on the SQL team and I have an undergraduate degree in math. 241 00:21:36,617 --> 00:21:40,340 like I can geek out with the data with you. 242 00:21:40,340 --> 00:21:41,541 I'm a little rusty. 243 00:21:41,541 --> 00:21:52,188 I'm sure I'd have to, I'd be a little slow, slower than you, but um you know, it is, I have seen, ah it is incredible how 244 00:21:52,880 --> 00:21:56,162 the lack of data hygiene in law firms. 245 00:21:56,162 --> 00:21:59,505 again, structured and unstructured even more so. 246 00:21:59,505 --> 00:22:13,054 So a law firm DMS is comprised of 87 drafts and red lines of the same document and email attachments and binary objects, know, videos. 247 00:22:13,234 --> 00:22:15,616 it's in such poor shape. 248 00:22:15,616 --> 00:22:16,877 And this is universal. 249 00:22:16,877 --> 00:22:21,860 Like I have, it's not one firm, it's most firms. 250 00:22:22,070 --> 00:22:27,764 And then you also have the issue of there's been a lot of M &A in big law. 251 00:22:27,764 --> 00:22:40,563 So you'll take one mess when a law firm merges and import that into another mess of a different nature, which makes things even more difficult to untangle because, maybe they 252 00:22:40,563 --> 00:22:49,900 weren't using matter centricity in the one DMS or, you maybe they have another problem over here and then you slam them together and you've, really got something to untangle. 253 00:22:49,900 --> 00:22:50,680 So 254 00:22:52,044 --> 00:22:55,386 I wonder sometimes, I don't know, how do you see this playing out? 255 00:22:55,386 --> 00:23:03,172 Like how do firms get their data in order to capitalize on whatever's coming next? 256 00:23:05,005 --> 00:23:06,965 Yeah, I mean, I think there's a couple of things there. 257 00:23:06,965 --> 00:23:15,945 So I think the first step is really to solve the problem of external data and client data. 258 00:23:16,225 --> 00:23:26,385 So a lot of the legal services that are provided typically look at what is happening in terms of all the things that I described, regulations, litigation, et cetera, which 259 00:23:26,385 --> 00:23:29,005 generally is external information. 260 00:23:29,105 --> 00:23:31,173 But then the second part of that is 261 00:23:31,350 --> 00:23:32,170 the client, right? 262 00:23:32,170 --> 00:23:40,524 So the client's own uh data and the client's own information that translates all of those things into what actually matters for that particular client, right? 263 00:23:40,524 --> 00:23:52,499 So a lot of law firms, they try to collect that client information and like as you described, DMS and all kinds of folders and information, all this is all over the place. 264 00:23:52,739 --> 00:23:58,914 So one way I think to really start with the solving the problem is to look at 265 00:23:58,914 --> 00:24:08,759 how the external data and the client data can be handled so that law firms are not ingesting that data and having to circulate that internally and so forth. 266 00:24:08,759 --> 00:24:18,814 So one way that, a technology perspective, that most companies deal with that is to say, the client has the data on the cloud and their own cloud, can we use technology like APIs, 267 00:24:18,814 --> 00:24:28,255 et cetera, to really feed into that information and get lawyers the information that they really need and not have all of these documents and information that's 268 00:24:28,255 --> 00:24:30,446 in all of these different places. 269 00:24:30,446 --> 00:24:38,548 um And then with respect to law firm uh data itself, uh I I agree that's a huge, huge problem. 270 00:24:38,548 --> 00:24:43,119 lot of law firms have tried different systems and procedures and so forth. 271 00:24:43,119 --> 00:24:50,431 And some of them are still reluctant to rely too much on the cloud and the cloud infrastructure to manage data. 272 00:24:50,471 --> 00:24:58,273 So part of that is solving those governance issues about using cloud and cloud infrastructure to house this. 273 00:24:58,601 --> 00:24:59,742 this data. 274 00:24:59,742 --> 00:25:02,145 But I think the other part is also platforms, right? 275 00:25:02,145 --> 00:25:11,313 So if a lawyer is using a platform like ours, they don't really need to have that data on their network or their folders and so forth, right? 276 00:25:11,313 --> 00:25:22,794 So especially if it's litigation cases and regular regulations and disclosure requirements and news and so forth happening around the world, we have that data stored in our cloud, 277 00:25:22,794 --> 00:25:23,174 right? 278 00:25:23,174 --> 00:25:24,385 So I think it's... 279 00:25:24,461 --> 00:25:32,664 know, adopting the process of sort of using a platform to feed into that data would be a good, a good step. 280 00:25:32,664 --> 00:25:39,347 I mean, in the financial industry, for example, people don't download and save all of the company financial documents, right? 281 00:25:39,347 --> 00:25:43,879 So analysts don't have like thousands of financial statements and reports, right? 282 00:25:43,879 --> 00:25:47,551 They use Bloomberg, they use capital IQ, they use a bunch of other sources. 283 00:25:47,551 --> 00:25:54,133 So I think looking at those types of models would be, would be helpful to say, you know, we don't need every single. 284 00:25:54,156 --> 00:25:56,961 document saved in our files, right? 285 00:25:56,961 --> 00:26:02,296 We can access uh that information using external platforms. 286 00:26:02,296 --> 00:26:17,062 Yeah, I spent 10 years at Bank of America and uh in primary, primarily risk management roles and uh internal audit, uh anti-money laundering, compliance, consumer risk. 287 00:26:17,062 --> 00:26:24,145 I bounced around all over the place and got a really, again, kind of a good cross-sectional view. 288 00:26:24,145 --> 00:26:31,728 even the banks have extremely mature risk management and governance processes. 289 00:26:32,104 --> 00:26:41,211 and even they have challenges, you know it's like law firms, law firms have, their data is a mess for a lot of different reasons. 290 00:26:41,211 --> 00:26:54,611 I talked about kind of the M &A scenario and they've under invested, um they have under invested in technology and they have taken a very, very much a wait and see attitude and 291 00:26:54,611 --> 00:26:59,124 some of this is not all their fault, know outside council guidelines have 292 00:27:00,096 --> 00:27:02,598 slowed down their migration to the cloud. 293 00:27:02,598 --> 00:27:16,188 um But also, again, back to the partnership model, getting lawyers to even profile their documents in the DMS, assign metadata, um pretty basic. 294 00:27:17,469 --> 00:27:25,325 Two attributes before you check in a document, um you wouldn't believe how many say other or are blank. 295 00:27:25,325 --> 00:27:27,396 And it's just really 296 00:27:28,046 --> 00:27:30,437 creates a lot of challenges around data hygiene. 297 00:27:30,437 --> 00:27:36,758 um And there hasn't been that enforcement mechanism because it hasn't really mattered. 298 00:27:36,758 --> 00:27:37,409 You know what I mean? 299 00:27:37,409 --> 00:27:40,980 Like, yeah, it creates a little bit of friction here and there. 300 00:27:40,980 --> 00:27:44,440 But now we're entering into a phase where it does matter. 301 00:27:44,441 --> 00:27:56,094 And you having 97 versions of the same document with all the red lines and you can't tell which one's the final um and all these irrelevant emails, everything that was just dumped 302 00:27:56,094 --> 00:27:56,984 in there. 303 00:27:57,066 --> 00:28:04,725 Now it does matter because we want to leverage AI and we got to make sense of it. 304 00:28:04,725 --> 00:28:07,708 So it's a real challenge. 305 00:28:07,863 --> 00:28:16,941 Yeah, I mean, think there's even larger business uh opportunities for law firms to try to fix this issue, right? 306 00:28:16,941 --> 00:28:25,578 So for example, having been at both the big forehand and big law, one question that typically comes up is something happens around the world. 307 00:28:25,578 --> 00:28:31,733 There is a sanction, there's a tariff or environmental issue and so forth. 308 00:28:31,733 --> 00:28:36,657 And there's typically a scramble to say, who's the right person to advise? 309 00:28:36,713 --> 00:28:38,354 on that issue, right? 310 00:28:38,354 --> 00:28:40,485 Basically trying to win that work for that firm. 311 00:28:40,485 --> 00:28:49,980 And I think a lot of law firms have started the process of collecting CRM data on experiences of their attorneys. 312 00:28:50,281 --> 00:29:01,137 But still, what I've witnessed and seen is that because the data is so spread out, it's very hard for a law firm to even meet that kind of requirement to say, hey, this is the 313 00:29:01,137 --> 00:29:05,029 group of partners that gives us the best chance to 314 00:29:05,165 --> 00:29:09,065 to not only win the engagement, but to get the best result for the client. 315 00:29:09,065 --> 00:29:16,685 And on the client side, they're looking for the same thing, to say which lawyers are going to be the most helpful, if it's litigation, who has the best track record, who's going to 316 00:29:16,685 --> 00:29:20,865 win the case earlier, who's going to get a better result for me based on their experience. 317 00:29:20,865 --> 00:29:28,085 So there are some business examples of why the data can hinder those types of business opportunities for law firms. 318 00:29:28,285 --> 00:29:30,933 I mean, that's one of things that we're working on is to say, know, 319 00:29:30,933 --> 00:29:34,566 Our platform provides a lot of that data on the development itself, right? 320 00:29:34,566 --> 00:29:35,246 What has happened? 321 00:29:35,246 --> 00:29:36,518 Where has it happened? 322 00:29:36,518 --> 00:29:38,959 Obviously, and lot of that information. 323 00:29:38,959 --> 00:29:50,879 But if law firms have their data kind of set up so that we can help link it with the CRM type of solutions, then essentially law firms can be in a better position to win uh legal 324 00:29:50,879 --> 00:29:51,569 work. 325 00:29:51,586 --> 00:29:53,477 Yeah, so yeah, that's interesting. 326 00:29:53,477 --> 00:29:55,818 I know a little bit about this because we integrate. 327 00:29:55,818 --> 00:30:05,243 we uh we provide a firm directory and we do what we call user profile enrichment and we tap into that experience data and that usually. 328 00:30:06,545 --> 00:30:10,587 Foundation is kind of the the big platform. 329 00:30:10,587 --> 00:30:15,790 Our friends at Lutera uh have that manages experience data. 330 00:30:15,790 --> 00:30:19,792 Do you compete with uh a foundation at all? 331 00:30:20,938 --> 00:30:22,259 No, directly. 332 00:30:22,259 --> 00:30:26,945 No, no, we really compete with those companies. 333 00:30:28,078 --> 00:30:31,749 Okay, so um I wanted to talk a little bit about agents. 334 00:30:31,749 --> 00:30:37,251 You and I had a good conversation about agents and I thought you had some interesting insights. 335 00:30:37,291 --> 00:30:49,214 We talked about the development of domain-specific AI agents in certain practice areas like employment law, trade law, ESG, kind of other practice areas. 336 00:30:49,214 --> 00:30:55,736 Are you guys playing around with any of that or do you have any vision for what that might look like? 337 00:30:56,625 --> 00:31:03,849 Yeah, so basically, our vision is really to build a vertical AI agent network. 338 00:31:03,849 --> 00:31:09,601 And the way we were doing that process is, as we discussed, starting with the data, right? 339 00:31:09,601 --> 00:31:15,254 So bottom up, getting the data correct, validating the data, and everything we've talked about so far. 340 00:31:15,354 --> 00:31:21,463 But then the next step is really to then make the AI and AI agents use that data. 341 00:31:21,463 --> 00:31:23,894 to work into the workflows that people are doing. 342 00:31:23,894 --> 00:31:39,643 So our vision is really to say, if you are a lawyer, let's say in ESG or trade or cyber, tax, et cetera, that our uh AI agents is essentially working with that particular lawyer 343 00:31:39,643 --> 00:31:43,685 to take the information that we have in our platform and to make it actionable. 344 00:31:43,685 --> 00:31:50,870 So if the lawyer wants to do legal research, that's probably one of the basic tasks that we do, or if they want to produce uh 345 00:31:50,870 --> 00:31:53,521 memo or client alert and so forth, right? 346 00:31:53,521 --> 00:32:00,744 Specific to their, not only their legal area, but also the information that they've been looking at in the past, right? 347 00:32:00,744 --> 00:32:05,206 So we track things like, you what is the user actually spending time on, right? 348 00:32:05,206 --> 00:32:13,179 So what we call behavior analytics and to customize the AI agent so that the user is getting that information. 349 00:32:13,179 --> 00:32:14,859 So that's, guess, know, step one, right? 350 00:32:14,859 --> 00:32:19,661 But then what we do is say, well, that person is in one legal area. 351 00:32:19,735 --> 00:32:29,210 But then when we roll up the different workflows for lawyers within a particular area, we want the AI agents essentially to talk to each other. 352 00:32:29,210 --> 00:32:45,319 So if somebody asks a business question, like there's a new tariff, what are my options as far as uh product flows and so forth that I can do to essentially eliminate or minimize my 353 00:32:45,319 --> 00:32:46,659 tariff impact? 354 00:32:46,900 --> 00:32:49,089 And what we know is from the legal space is that 355 00:32:49,089 --> 00:32:51,120 Tariffs is not simply a trade issue. 356 00:32:51,120 --> 00:32:59,495 Tariffs impact a whole bunch of other things, because what you can't do is say, I've already had a tariff, but now I've got a huge tax liability in other areas that all of 357 00:32:59,495 --> 00:33:01,086 sudden has come up. 358 00:33:01,086 --> 00:33:09,941 So uh what we have to do with these agents and what we want to do with scoreletics is say these agents should talk to each other and say, here's a potential solution to the tariff 359 00:33:09,941 --> 00:33:12,622 problem that solves the tariff problem. 360 00:33:12,622 --> 00:33:17,931 But the agent is looking at the tax rules and the employment rules and cyber, et cetera. 361 00:33:17,931 --> 00:33:22,006 are going to also chime in and to say, what is the impact on those areas, right? 362 00:33:22,006 --> 00:33:33,579 So at the end, our overall agent gives the company an actionable intelligence to say, here is a solution or potential solutions that could work that reflects the input from all of 363 00:33:33,579 --> 00:33:36,161 these different areas. 364 00:33:36,684 --> 00:33:38,625 Well, that is super interesting. 365 00:33:38,625 --> 00:33:41,787 know, a couple of things pop into my head when you talk about that. 366 00:33:42,087 --> 00:33:57,056 Are you borderline, are you walking the line on the practice of law there um with advising clients and interpreting regulations and creating guidance? 367 00:33:57,056 --> 00:33:58,006 Is that? 368 00:33:59,310 --> 00:34:00,710 Yeah, no, I think it's a good question. 369 00:34:00,710 --> 00:34:04,770 And that's the question that we deal with quite often. 370 00:34:05,490 --> 00:34:08,750 No, we are not practicing law. 371 00:34:09,030 --> 00:34:22,870 If you try to that clear, really what we're trying to do is to help come up with information that lawyers can use to then either they have outside counsel review and time 372 00:34:22,870 --> 00:34:25,570 in and make that more actionable. 373 00:34:26,337 --> 00:34:31,970 But what we're trying to do is improve that process of the actual decision-making, right? 374 00:34:31,970 --> 00:34:36,843 To say, you know, here are the potential options that you may have. 375 00:34:36,843 --> 00:34:44,687 Of course, have to, like I said, you have to have lawyers and consultants and audit company firms and so forth to review. 376 00:34:44,687 --> 00:34:49,030 But we've actually saved you a whole bunch of time in that process. 377 00:34:49,030 --> 00:34:55,213 if that process of doing it manually would have been a month, maybe you've done it in three or four days. 378 00:34:55,327 --> 00:35:05,392 It still requires the same people to be involved in the process, but we're not looking to replace lawyers or to say that this is actually legal advice that the company can simply 379 00:35:05,392 --> 00:35:07,895 just take from our platform and run with it, right? 380 00:35:07,895 --> 00:35:12,391 It does require legal input and lawyer review and so forth. 381 00:35:12,600 --> 00:35:13,120 Interesting. 382 00:35:13,120 --> 00:35:17,851 Yeah, we just went through a process called QSBS, qualified small business stock. 383 00:35:17,851 --> 00:35:24,983 It's a federal government innovation program where if you're a, you got to check all these boxes, which we do. 384 00:35:24,983 --> 00:35:30,585 And if you're an LLC, you convert to a C corp and essentially you have a five year holding period. 385 00:35:30,585 --> 00:35:35,596 And if you maintain that period, that holding period, you get 10 times your basis. 386 00:35:35,596 --> 00:35:41,748 So you get evaluation when you convert to a C corp and then you get 10 times that essentially tax free. 387 00:35:42,102 --> 00:35:44,453 upon a liquidity event. 388 00:35:44,753 --> 00:36:00,918 Great, very, I mean, it's awesome for small software companies like us, but in the process of getting there, we had to pull in, know, tax, general counsel, um counsel that's 389 00:36:01,398 --> 00:36:04,819 specific to, has QSBS expertise. 390 00:36:04,819 --> 00:36:08,420 We even had a third party, um they're a fellow. 391 00:36:08,546 --> 00:36:18,083 TLTF portfolio company called QSBS expert, essentially a non lawyer, but consultant, former ENY guy who knew this stuff inside and out. 392 00:36:18,083 --> 00:36:20,985 So I had all these parties at the table. 393 00:36:20,985 --> 00:36:25,187 Like one phone call would cost like thousands of dollars. 394 00:36:26,429 --> 00:36:29,651 in addition to my team's time, that's just like external costs. 395 00:36:29,651 --> 00:36:37,436 If I could find a way to prime the pump a little bit and you know, have some uh 396 00:36:37,474 --> 00:36:42,049 have some things together that we could review rather than kind of starting from scratch. 397 00:36:42,049 --> 00:36:48,537 And it would eliminate a lot of the, it would make things much more efficient and streamlined. 398 00:36:48,537 --> 00:36:55,704 And it sounds like you're straddling the fence tax and law regulations. 399 00:36:56,109 --> 00:37:01,689 Yeah, I mean, we're looking to cover essentially every area of law. 400 00:37:01,929 --> 00:37:08,309 know, cyber I mentioned and IP law and employment law, tax, et cetera. 401 00:37:08,389 --> 00:37:15,569 It's going to be a process to actually get all of that data and integrate and get the AI agents all integrated as well. 402 00:37:16,529 --> 00:37:22,009 But yeah, I our view really is that the process as you described it is very manual. 403 00:37:22,009 --> 00:37:24,653 A lot of people are involved, but also 404 00:37:24,653 --> 00:37:30,213 we think it can be a lot more efficient if all of those people have the information that they need. 405 00:37:30,453 --> 00:37:36,093 So if, for example, the AI tells all the parties, are the rules. 406 00:37:36,093 --> 00:37:39,133 Here are the rules in all the countries. 407 00:37:39,633 --> 00:37:47,433 AI can make some recommendations, but then say, if you want to actually validate any of this, we can give you the underlying core documents, cases. 408 00:37:47,553 --> 00:37:48,933 We can give you the regulations. 409 00:37:48,933 --> 00:37:50,793 We can give you all the underlying data. 410 00:37:50,793 --> 00:37:52,110 So instead of having 411 00:37:52,110 --> 00:38:01,510 lawyers go out and find that information across all of these countries and areas of law, the AI is actually put you in a position where you're actually validating, right? 412 00:38:01,510 --> 00:38:07,310 More than going out and having associates and others, knowledge management lawyers, research, all of these things. 413 00:38:07,310 --> 00:38:18,050 Because in our view is that's one of the best benefits of AI is that, you you don't need to do that research and that review by hand, but manually every single time you have a 414 00:38:18,050 --> 00:38:21,865 transaction or an issue, there are a of efficiencies to having. 415 00:38:21,865 --> 00:38:23,325 AI do that for you. 416 00:38:23,325 --> 00:38:26,606 again, our view is we need to be transparent, right? 417 00:38:26,606 --> 00:38:31,748 All the sources, anybody in our platform can click on the source information. 418 00:38:31,748 --> 00:38:43,301 So like I said, documents, underlying news stories, the government sites, et cetera, and validate it down to the last sentence, so to speak, in the actual rules. 419 00:38:43,301 --> 00:38:48,892 So it has to be actionable, but also has to be auditable by anybody. 420 00:38:48,892 --> 00:38:51,553 And that's how we built our platform. 421 00:38:52,055 --> 00:38:58,549 So I, mean, M &A is actually a very good example of this as well. 422 00:38:58,549 --> 00:39:09,454 So as you can imagine in &A transaction, you have a whole bunch of business teams, legal teams that are around a table talking about how to do due diligence, where to focus on. 423 00:39:09,454 --> 00:39:20,621 And again, our view is if a platform like ours really tells you what are the key legal risks, for example, across all the countries where the target operates, also based on 424 00:39:20,621 --> 00:39:21,617 their, you 425 00:39:21,617 --> 00:39:27,120 how they operate, are they manufacturing, are they warehouse, are they service provider, do they work with third parties. 426 00:39:27,120 --> 00:39:36,067 That information should make that process a lot more efficient because people know here's where I need to focus my due diligence effort versus starting with blank slate and say, 427 00:39:36,067 --> 00:39:45,849 I'm just going to canvas the world and go country by country and really engage lawyers in all those countries to tell me what are the risks in that country. 428 00:39:45,985 --> 00:39:50,701 So the savings obviously both in cost but also timeliness is really important. 429 00:39:50,701 --> 00:39:54,255 That's what we feel AI can really add a lot of value. 430 00:39:54,902 --> 00:39:55,672 Interesting. 431 00:39:55,672 --> 00:40:09,288 Well, there's one topic I really wanted to talk about that you've got some perspective on given your time at KPMG and that is the big four entering the US legal market and the most 432 00:40:09,288 --> 00:40:23,862 recent iteration of that with KPMG law in Arizona who has been granted approval and you know, I'm I see a lot of I'll call it complacency 433 00:40:23,862 --> 00:40:32,826 in big law with respect, they're not, most people aren't that worried about um the big four entering in the legal market. 434 00:40:32,826 --> 00:40:35,097 And I feel like they should be a little bit. 435 00:40:35,097 --> 00:40:50,963 um And a lot of people point to other things like, you know, the big four have tried in the U S before, you know, the legal services act in the UK enabled non lawyer ownership of 436 00:40:50,963 --> 00:40:52,834 law firms and 437 00:40:52,834 --> 00:40:54,675 nothing really has changed there. 438 00:40:54,675 --> 00:40:59,006 In fact, the ones that have tried have haven't performed that great. 439 00:40:59,006 --> 00:41:09,669 um But I think what's different now is the means of legal production are changing and technology is now going to be a key driver. 440 00:41:09,669 --> 00:41:11,319 And that part isn't up for debate. 441 00:41:11,319 --> 00:41:20,644 If you don't think that's true, then I don't know what to say to you, but it's going to be a key component. 442 00:41:20,644 --> 00:41:23,324 of how legal services get delivered. 443 00:41:23,504 --> 00:41:35,464 And when I look at what the big four have with KPMG being the smallest at 40 billion, Deloitte being the biggest at 80 billion, you know, so six, five, six times the size of 444 00:41:35,464 --> 00:41:38,804 the biggest law firm in the world at K &E. 445 00:41:39,744 --> 00:41:44,804 DNA is in, I'm sorry, innovation is in their DNA. 446 00:41:45,084 --> 00:41:47,204 And it's not illegal. 447 00:41:47,204 --> 00:41:50,654 In fact, it's really innovation 448 00:41:50,654 --> 00:41:55,586 is a uh real struggle um in law firms. 449 00:41:55,586 --> 00:41:59,827 It's starting to get easier, but historically it's been a challenge. 450 00:42:00,068 --> 00:42:03,519 So I see some real advantages that the big four have. 451 00:42:03,519 --> 00:42:14,194 And I don't think the big four is going to come in and eat Big Law's lunch overnight, but I think incrementally they're going to start with the blocking and tackling, the ALSP type 452 00:42:14,194 --> 00:42:16,074 of services. 453 00:42:16,074 --> 00:42:20,676 then they're going to work their way up the complexity. 454 00:42:20,868 --> 00:42:26,788 food chain over time as they build credibility in the marketplace and demonstrate that they can deliver. 455 00:42:27,508 --> 00:42:42,468 you know, I had the chief legal officer at DHL on maybe two, three episodes ago, and I asked him, would you send work to the big four who set up a law firm? 456 00:42:42,468 --> 00:42:44,308 said, hell yeah, I would. 457 00:42:44,768 --> 00:42:50,788 You know, so, I mean, they have 97 % of the Fortune 500 on the 458 00:42:50,788 --> 00:42:53,608 audit side market share. 459 00:42:53,728 --> 00:42:55,828 So huge install base there. 460 00:42:55,828 --> 00:42:56,088 I don't know. 461 00:42:56,088 --> 00:43:03,368 What is your take on how, what impact the big four entering the market in the U S. 462 00:43:03,528 --> 00:43:04,362 Um, how do you see that? 463 00:43:04,362 --> 00:43:11,188 I think this is a topic that think a lot of law firms obviously are talking about as well. 464 00:43:11,188 --> 00:43:24,520 Having spent time both at, as you noted, with the big four and big law, I think one of the challenges for accounting firms generally is, you're right, they're much larger, the 465 00:43:24,520 --> 00:43:30,685 resources they have, historically they've been a lot more innovative than obviously law firms have. 466 00:43:30,871 --> 00:43:33,902 But they also have sort of governance issues of their own. 467 00:43:33,902 --> 00:43:39,084 uh For example, they have so many different services that they offer, right? 468 00:43:39,084 --> 00:43:49,419 Often structured across practice, know, kind of broad areas, assurance and tax, and the ones that still have consulting, right, as well, that sometimes they're not obviously 469 00:43:49,419 --> 00:43:52,470 aligned when it comes to innovation strategy, right? 470 00:43:52,470 --> 00:44:00,558 So for example, compliance work, audit work, some of those types of things are high volume, high fees. 471 00:44:00,558 --> 00:44:08,578 type of work, whereas some of the things that we're talking about with respect to innovation really help to automate those tasks. 472 00:44:09,698 --> 00:44:13,838 as an economist, I can tell you what happens with those types of things, right? 473 00:44:13,838 --> 00:44:22,638 Is that once you do technology that is really improving productivity, ultimately that savings is passed along to your customer, right? 474 00:44:22,638 --> 00:44:25,798 So you're not going to charge them more, certainly not the same. 475 00:44:26,477 --> 00:44:34,277 if you implement efficiencies and how quickly you draft a contract or a compliance document and so forth. 476 00:44:34,277 --> 00:44:48,577 So I think the challenge for the accounting firms is really going to be, can they really get past that need or that kind of request internally to say the first thing they should 477 00:44:48,577 --> 00:44:54,757 be doing is diving headfirst into the areas of law that are the ones that 478 00:44:55,021 --> 00:45:04,761 AI is actually trying to automate the contracts, as I mentioned, drafting documents, maybe even some high level review of the documents and so forth, or whether they can actually 479 00:45:04,761 --> 00:45:08,541 look at innovation as a way to transform law. 480 00:45:08,641 --> 00:45:17,101 So going to the consulting side, they have lot of experience with all kinds of technology there, deploying it for clients as well. 481 00:45:17,101 --> 00:45:21,825 Can they really connect that part of the business to say, we can actually 482 00:45:21,825 --> 00:45:27,269 help to use technology to transform how legal services are provided, right? 483 00:45:27,269 --> 00:45:38,589 So more of a, we're providing a value to the client that is saving them a bunch of money or creating new revenue sources for that client versus, like I said, doing what they're 484 00:45:38,589 --> 00:45:41,411 already doing, but doing it faster and cheaper than big law. 485 00:45:41,411 --> 00:45:51,726 think that sort of that dynamic intention between the two types of opportunities may make it difficult for the big four to actually make 486 00:45:51,726 --> 00:45:55,786 headway in the legal space in the near future. 487 00:45:56,686 --> 00:45:58,786 But if they can somehow get past that, I agree. 488 00:45:58,786 --> 00:46:02,306 think the opportunity with them is obviously really, really large. 489 00:46:02,366 --> 00:46:07,606 I think the billable hour model is, again, law firms have it, but big four have it as well. 490 00:46:07,606 --> 00:46:18,605 So it's also a question of how do you actually charge a client for the services if the model is billable hour, then obviously the incentives, regardless of whether you're 491 00:46:18,605 --> 00:46:27,025 If you're a big four or big law is the same, you want to maximize the number of hours and billing rates that you're charging to clients and not really come up with a model. 492 00:46:27,025 --> 00:46:33,665 Like I said, you're adding value, maybe a charging based on the value that you're adding or value based pricing and so forth. 493 00:46:33,705 --> 00:46:34,865 So I think that's going to be the challenge. 494 00:46:34,865 --> 00:46:44,325 think in my personal view, I think the internal dynamics at big four will make this a problem and a challenge as you've noted in Europe, right? 495 00:46:44,325 --> 00:46:46,905 They've been in Europe for a while practicing law. 496 00:46:47,053 --> 00:46:54,895 and it hasn't really translated to them becoming huge players in the same sense as some of the big law firms are. 497 00:46:54,895 --> 00:47:04,078 But if they maybe think about acquisitions, right, that's typically a way for lot of these companies to say our internal culture governance makes some of these things a big 498 00:47:04,078 --> 00:47:04,578 challenge. 499 00:47:04,578 --> 00:47:13,440 But if you make an acquisition of companies that are already doing some of these things, that might be a better approach, I think, for certainly the big four, maybe others to get 500 00:47:13,440 --> 00:47:14,956 into the legal space. 501 00:47:14,956 --> 00:47:15,496 Yeah. 502 00:47:15,496 --> 00:47:18,457 Now the, the, the big four definitely have their challenges. 503 00:47:18,457 --> 00:47:32,151 mean, they're a partnership model as well, but they've managed to figure out how to scale and you know, lawyers, law firms have really been limited by, in my opinion, um, law firm 504 00:47:32,151 --> 00:47:45,104 leadership is all lawyers and you know, there's a certain lens, legal lens that has a certain shape and shapes the perspective, uh, in a way that 505 00:47:45,104 --> 00:47:51,747 is you need the more diversity you have in leadership and different angles on things, the better. 506 00:47:52,488 --> 00:48:01,453 The lawyer mindset is well documented as being risk averse and conservative and comfortable with the status quo and all the things that Dr. 507 00:48:01,453 --> 00:48:06,066 Larry Richard has documented studying lawyers for 35 years. 508 00:48:06,066 --> 00:48:14,300 that's not always the bit, those aren't always aligned to the best leadership um capabilities. 509 00:48:14,434 --> 00:48:17,810 you know, especially in the entrepreneurship world, like you gotta take risks. 510 00:48:17,810 --> 00:48:19,478 You gotta be uncomfortable. 511 00:48:19,478 --> 00:48:24,803 You gotta be comfortable with uncertainty and taking chances and so. 512 00:48:24,803 --> 00:48:25,737 um 513 00:48:25,737 --> 00:48:27,397 think I totally agree. 514 00:48:27,397 --> 00:48:32,057 And I think that's what my message to many law firms is, right? 515 00:48:32,057 --> 00:48:44,357 Is that actually to fight off competition and to really succeed, you have to be better at innovation and technology than the big four and others that are trying to get into the 516 00:48:44,357 --> 00:48:45,457 legal space. 517 00:48:45,777 --> 00:48:49,917 And oftentimes, we discussed at the beginning, it's not just developing it in-house, right? 518 00:48:49,917 --> 00:48:52,357 As we discussed, that has a lot of challenges. 519 00:48:52,437 --> 00:48:54,283 But a lot of law firms are looking at, you 520 00:48:54,283 --> 00:48:55,574 becoming investors, right? 521 00:48:55,574 --> 00:49:10,574 So investing in technology that they think could make them uh more productive, adding more value to clients and so forth without having to have that internally within their firm. 522 00:49:10,574 --> 00:49:20,171 So I think we will see an increase in law firms that are participating more and more in sort of innovation in that context in terms of becoming investors, but also hands-on 523 00:49:20,171 --> 00:49:20,851 investors, right? 524 00:49:20,851 --> 00:49:24,119 Giving these startups like us access to the lawyers to 525 00:49:24,119 --> 00:49:30,782 test out our products, get feedback and work with them on the workflows and the AI agents, everything which we talked about, right? 526 00:49:30,782 --> 00:49:34,074 To make it actionable for them and benefiting them. 527 00:49:34,074 --> 00:49:43,108 Specifically, I think that's one way I think a law firms will help or stay ahead of the competition is actually beat the competition at their own game by innovation. 528 00:49:43,108 --> 00:49:49,912 But like I said, I think it's, in my opinion, it's going to be through investment versus trying to do it themselves in house. 529 00:49:49,912 --> 00:50:00,120 Yeah, well, you know, the Legal Tech Fund, their first fund, I don't know if it was exclusively law firms as limited partners, but I know several big law firms were the LPs 530 00:50:00,120 --> 00:50:01,341 in that fund. 531 00:50:01,341 --> 00:50:07,745 So that's one proxy through which law firms have, you know, generated some exposure in the legal tech market. 532 00:50:07,745 --> 00:50:12,349 um And it's been great, you know, they're investors in us. 533 00:50:12,349 --> 00:50:13,570 We love working with them. 534 00:50:13,570 --> 00:50:15,231 It's been a good relationship. 535 00:50:15,231 --> 00:50:17,166 um 536 00:50:17,166 --> 00:50:18,786 Well, this has been a great conversation, Moyes. 537 00:50:18,786 --> 00:50:30,110 I appreciate you telling us about scorelytics and it's a fascinating, I think our listeners, you know, that work in cam and innovation functions at these big law firms and 538 00:50:30,110 --> 00:50:34,821 probably inspired some, some, some thought. 539 00:50:34,821 --> 00:50:45,024 And, um, I think we're going to see more of this model in legal where there's spinoffs and subsidiaries and sister companies. 540 00:50:45,442 --> 00:50:46,247 It just makes sense. 541 00:50:46,247 --> 00:50:49,655 So I really appreciate you uh sharing your thoughts with us. 542 00:50:49,729 --> 00:50:50,281 No, that's 10. 543 00:50:50,281 --> 00:50:52,003 Thank you so much for having me on. 544 00:50:52,003 --> 00:50:53,252 Yeah, this has been great. 545 00:50:53,252 --> 00:50:54,612 Alright, good stuff. 546 00:50:54,612 --> 00:50:56,572 Alright, enjoy the rest of your afternoon. 547 00:50:56,952 --> 00:50:57,992 Thanks, Mois. 00:00:04,907 Moiz, thanks for joining me this afternoon. 2 00:00:05,996 --> 00:00:07,871 My pleasure, good to be on Ted. 3 00:00:08,376 --> 00:00:08,846 Awesome. 4 00:00:08,846 --> 00:00:12,178 Well, you and I got connected on LinkedIn. 5 00:00:12,178 --> 00:00:25,493 forget we were chiming in on a thread or something or another, but I saw your, what you're up to now, which I thought was super interesting with scorelytics, your founder and CEO. 6 00:00:25,493 --> 00:00:29,240 And you were with, Baker McKenzie. 7 00:00:29,240 --> 00:00:32,176 You got a background in the big four. 8 00:00:32,176 --> 00:00:36,034 Why don't you just us, just give us kind of a quick rundown of your 9 00:00:36,034 --> 00:00:39,649 your background and then what you're up to today with scorelytics. 10 00:00:40,556 --> 00:00:43,017 Yeah, yeah, thank you again, Ted, for having me. 11 00:00:43,017 --> 00:00:50,699 Yeah, so I've been, like I said, working with the big four, first part of my career for about nine years. 12 00:00:50,699 --> 00:01:00,132 And then I joined Baker McKenzie about, I guess, 15 years ago uh in the economic consulting group. 13 00:01:00,132 --> 00:01:06,434 So Baker McKenzie is one of the few law firms that has a large group of non-lawyers within it. 14 00:01:06,434 --> 00:01:08,384 So mostly economists. 15 00:01:08,514 --> 00:01:11,915 MBAs, PhDs, and so forth. 16 00:01:12,396 --> 00:01:20,750 So my experience is really, both on the consulting side, but also working uh at a big law firm, understanding how the law firm works. 17 00:01:20,750 --> 00:01:26,042 Obviously, I'm not a lawyer, but my role was to work with lawyers on a day-to-day basis. 18 00:01:26,042 --> 00:01:35,756 And as part of that effort, we started to look at what are ways that legal services can be provided to clients in a way that's 19 00:01:36,066 --> 00:01:41,769 considering non-legal activities like economics and data analytics and so forth. 20 00:01:41,769 --> 00:01:44,351 And that was helping to sort of lead that effort. 21 00:01:44,351 --> 00:01:50,314 And that's ultimately what led to scorelytics and the spinoff from Baker McKenzie. 22 00:01:50,770 --> 00:01:52,899 And tell us what scorelytics does. 23 00:01:53,600 --> 00:02:09,014 Yeah, so scorelytics is a platform that really tries to solve the problem that many lawyers, whether they're in-house at law firms as well, to really make legal intelligence 24 00:02:09,014 --> 00:02:09,694 actionable. 25 00:02:09,694 --> 00:02:16,035 So what I mean by that is there are situations where a business has a really big decision to make. 26 00:02:16,035 --> 00:02:19,230 It could be a merger and acquisition. 27 00:02:19,230 --> 00:02:23,467 It could be choosing to go into a new market, to exit a market. 28 00:02:23,763 --> 00:02:29,527 or just dealing with new regulations and the evolving landscape that's happening so quickly, right? 29 00:02:29,527 --> 00:02:31,138 And that's only accelerated. 30 00:02:31,478 --> 00:02:41,935 And one of the issues there has always been not only getting that information in a timely way, but also making that information so that it's actionable for the broader business, 31 00:02:41,935 --> 00:02:42,186 right? 32 00:02:42,186 --> 00:02:52,052 So legal teams want to engage with leaders in the company, whether it's the C-suite, the board of directors, their shareholders, et cetera, in a way that says, okay, this is an 33 00:02:52,052 --> 00:02:53,453 emerging issue. 34 00:02:53,749 --> 00:02:55,450 But why is it really important? 35 00:02:55,450 --> 00:03:06,132 And what we do is we say, we can not only track those developments in real time, but we can also translate those into what is the financial impact that particular development has 36 00:03:06,132 --> 00:03:06,753 on a company? 37 00:03:06,753 --> 00:03:08,273 Is it a $200 million issue? 38 00:03:08,273 --> 00:03:10,033 Is it billion dollar issue? 39 00:03:10,194 --> 00:03:20,456 So really looking at what the financial services industry has done for a long time when it comes to risk, putting risk in the context of quantitative uh data. 40 00:03:20,469 --> 00:03:23,693 And the score of it is what we say is that there's no reason why 41 00:03:23,829 --> 00:03:28,292 that same mentality or approach can be applied to legal risk. 42 00:03:28,292 --> 00:03:41,759 At end of the day, legal risks have a financial impact and legal teams are basically instructed to help the company save uh on revenue and potentially high litigation costs 43 00:03:41,759 --> 00:03:42,440 and so forth. 44 00:03:42,440 --> 00:03:45,451 So they are providing a financial benefit. 45 00:03:45,451 --> 00:03:52,235 So our RV was with scorelytics, why not take those legal risks and translate them into a quantitative measure that 46 00:03:52,363 --> 00:03:54,761 resonates with a broader organization. 47 00:03:55,088 --> 00:03:56,861 And who are your clients? 48 00:03:56,861 --> 00:03:59,635 it other law firms, inside counsel? 49 00:03:59,635 --> 00:04:03,500 Who buys your services or offerings? 50 00:04:03,731 --> 00:04:16,048 Yeah, so our clients are mostly large multinational companies because as you can imagine, for those companies, because they operate in so many different countries and are impacted 51 00:04:16,048 --> 00:04:24,632 by so many legal areas and developments that for them keeping track of all of that information is typically a challenge. 52 00:04:24,632 --> 00:04:30,406 Even though some of these companies may have really large in-house legal teams, they still struggle to keep up with the data. 53 00:04:30,406 --> 00:04:33,387 So our clients are large uh companies. 54 00:04:33,685 --> 00:04:36,886 mostly companies that operate uh globally. 55 00:04:36,886 --> 00:04:44,610 uh And then also law firms as well, because law firms are doing a lot of what we do typically using manual processes, right? 56 00:04:44,610 --> 00:04:53,073 They're looking at or trying to identify the latest trends in legal, but also trying to make that information actionable to their clients, right? 57 00:04:53,073 --> 00:04:58,135 And they're all trying to do that as efficiently as possible and add the most value to their clients. 58 00:04:58,135 --> 00:05:02,347 So law firms are also a big part of our client base. 59 00:05:02,638 --> 00:05:07,770 So are you a spin-off of, is Scorelytics a spin-off of Baker McKenzie? 60 00:05:07,770 --> 00:05:11,208 Is it a wholly owned subsidiary or are they sister companies? 61 00:05:11,208 --> 00:05:13,111 What is the structure? 62 00:05:13,119 --> 00:05:20,189 Yeah, so Baker McKenzie spun off Scoralytics in November of last year, so November 2024. 63 00:05:20,473 --> 00:05:23,385 Baker McKenzie continues to be a shareholder. 64 00:05:23,385 --> 00:05:26,617 own 30 % of the company. 65 00:05:26,977 --> 00:05:28,808 But they don't control the company, right? 66 00:05:28,808 --> 00:05:36,823 So we're independent as far as our ability to get outside financing from anybody else, to work with other law firms. 67 00:05:36,823 --> 00:05:40,429 So we have no limitations as far as working only with 68 00:05:40,429 --> 00:05:45,668 with Baker McKinsey, but yeah, the law firm owns 30 % of the company. 69 00:05:45,668 --> 00:05:46,768 Interesting. 70 00:05:46,888 --> 00:05:47,548 Yeah. 71 00:05:47,548 --> 00:05:59,128 and what was the motivation behind spinning off versus keeping you and your team and what you do in-house at the firm? 72 00:05:59,950 --> 00:06:07,270 Yeah, I think this is part of a broader question also about how law firms approach innovation. 73 00:06:07,670 --> 00:06:19,490 So I think the story within Baker McKinsey itself was that the firm obviously had some experience in terms of internal innovation and tools that were developed. 74 00:06:20,010 --> 00:06:28,350 Most of those tools, frankly, were for internal use as far as making processes more efficient around contracts and so forth. 75 00:06:28,365 --> 00:06:36,565 And with scorelytics, because it is sort of a different solution in terms of, you know, it's the risk, legal risk assessment solution. 76 00:06:36,925 --> 00:06:41,725 That was part of the conversation as well is that, you this solution is a tech solution. 77 00:06:41,905 --> 00:06:48,965 Perhaps it has a much larger potential than just within one firm. 78 00:06:49,685 --> 00:06:53,345 But also I think there was the question about technology and AI. 79 00:06:53,345 --> 00:06:56,811 So as you can imagine, you know, lot of law firms are 80 00:06:56,811 --> 00:07:03,585 looking at AI very, very closely and about the governance around the AI, how should it be deployed? 81 00:07:03,705 --> 00:07:07,498 What are the risks associated with AI and so forth? 82 00:07:07,498 --> 00:07:16,493 So that was part of the conversation as well, is to say, well, this is a technology company, but it is using AI and it will be using AI more and more. 83 00:07:16,694 --> 00:07:22,667 And is it better for the firm to try to address those questions and keep it in-house? 84 00:07:22,685 --> 00:07:25,858 or let it be what it should be, which is a tech company, right? 85 00:07:25,858 --> 00:07:30,672 There are tech companies that are operating in the space of AI and they're dealing with these questions. 86 00:07:30,672 --> 00:07:33,684 So that was part of the reasoning as well. 87 00:07:33,684 --> 00:07:45,664 But I think ultimately the decision was that for Baker McKinsey it's better to be an investor uh and to benefit from the growth of the company with help from other investors 88 00:07:45,664 --> 00:07:48,727 and other stakeholders out there who can help the company grow. 89 00:07:48,727 --> 00:07:51,969 Then you try to keep it in-house and only to serve 90 00:07:51,969 --> 00:07:53,355 the firm's clients. 91 00:07:53,560 --> 00:07:57,981 There have been a few of these I've seen in legal throughout the years. 92 00:07:57,981 --> 00:08:03,863 I'm not sure if you've heard of Cypharth Lean or Cleary X. 93 00:08:03,863 --> 00:08:09,334 um But, you know, it sounds like kind of a similar path. 94 00:08:09,334 --> 00:08:16,076 I remember now how you and I started um chatting. 95 00:08:16,076 --> 00:08:21,258 We were talking about the big four and the partnership model and some of the limitations around that. 96 00:08:21,258 --> 00:08:21,988 I'm... 97 00:08:23,170 --> 00:08:40,926 I'm a believer in that the partnership model creates a lot of friction towards scale and R and D and partnerships are, especially partnerships operating on a cash basis are 98 00:08:40,926 --> 00:08:44,859 optimized for profits, right? 99 00:08:44,859 --> 00:08:49,843 um Amortizing capital expenditures over 100 00:08:50,360 --> 00:08:55,463 periods of time is not something that you see in big law typically. 101 00:08:55,463 --> 00:09:12,592 I think that in consensus driven decision making, my listeners have heard me talk many times about um also just the governance mechanisms around partnerships versus more formal, 102 00:09:12,592 --> 00:09:19,236 um big companies or C corps and they have boards of directors and they have 103 00:09:19,296 --> 00:09:32,165 management that's installed with you know uh c-suite executives that aren't necessarily practitioners and they have internal audit and risk management functions and all of those 104 00:09:32,165 --> 00:09:45,885 governance mechanisms allow companies to scale and there isn't a law firm on planet earth that would qualify to be in the fortune 500 today uh k &e is really close um but they're a 105 00:09:45,885 --> 00:09:47,466 bit of an outlier they're 106 00:09:47,716 --> 00:09:50,056 I haven't got their 2025 numbers. 107 00:09:50,056 --> 00:09:54,756 Their 2024 revenue numbers were in the low seven billions. 108 00:09:54,756 --> 00:09:59,036 I think it's mid to high seven billions is the floor of the fortune 500. 109 00:09:59,956 --> 00:10:02,796 I'm a believer and I'd love to get your take on this. 110 00:10:02,796 --> 00:10:10,316 I'm a believer that scale is going to be important in this next iteration of law. 111 00:10:10,836 --> 00:10:14,416 And the reason I think it's going to be important is because there's going to be investment. 112 00:10:14,416 --> 00:10:17,056 Like firms are going to have to differentiate themselves somehow. 113 00:10:17,092 --> 00:10:21,343 um Historically, it's been with their people, right? 114 00:10:21,343 --> 00:10:32,056 Hey, we have the best &A practice or we have the best IP practice and you know, we've got a legal resume and that's going to continue to be part of the equation. 115 00:10:32,056 --> 00:10:44,139 But I think when we get into this tech enabled legal service delivery world, technology is also going to be an ingredient in that equation and that requires some R &D. 116 00:10:44,139 --> 00:10:46,592 So I don't know, I feel like 117 00:10:46,592 --> 00:11:00,364 It's going to be for firms to, it seems like the bigger firms that accumulate some mass and some scale will have more resources to invest and make their tech better than the 118 00:11:00,364 --> 00:11:04,777 firms down the street and can enable new capabilities. 119 00:11:04,777 --> 00:11:06,869 Kind of like what you guys do. 120 00:11:06,869 --> 00:11:07,309 I don't know. 121 00:11:07,309 --> 00:11:10,912 is your take on all that I just said there? 122 00:11:11,149 --> 00:11:13,549 Yeah, no, think you're exactly right. 123 00:11:13,549 --> 00:11:25,889 I think the governance structure in these firms, while I think it does help these firms with risk management and of course, there's bar rules and all those things, I do think 124 00:11:25,889 --> 00:11:33,869 that it sort of makes it difficult to really innovate in the same sense that a company is innovating in Silicon Valley or elsewhere. 125 00:11:33,869 --> 00:11:37,749 are purely tech companies that are taking risks, right? 126 00:11:37,749 --> 00:11:39,609 They're validating 127 00:11:40,093 --> 00:11:44,014 hypotheses, they're testing out some fail, some succeed, right? 128 00:11:44,014 --> 00:11:52,534 That all of that process that goes into innovation is not necessarily one that fits into a law firm model. 129 00:11:52,754 --> 00:11:57,974 But at the same time, you know, there are massive benefits to law firms, to innovation, right? 130 00:11:57,974 --> 00:12:05,814 So, for example, there's a lot of discussion and I've seen many articles about how do law firms get to the $10,000 billable hour, right? 131 00:12:05,814 --> 00:12:08,929 I think you may have seen some of these as well. 132 00:12:08,929 --> 00:12:22,513 where in my views, a lot of the discussion was about, if you automate basic tasks, mostly using AI, then all of a sudden you can charge clients 10,000 per hour. 133 00:12:22,653 --> 00:12:35,117 But my view is that technology, it's kind like what we're doing is if law firms are able to identify the problems that their clients are facing, even before the problem actually 134 00:12:35,117 --> 00:12:35,853 happens. 135 00:12:35,853 --> 00:12:36,153 right? 136 00:12:36,153 --> 00:12:42,513 So more of the predictive analytics to say, there's a trend that's happening in the regulation and litigation enforcement. 137 00:12:42,513 --> 00:12:46,653 And you can proactively go to a client and get ahead of those. 138 00:12:46,653 --> 00:12:54,393 All of a sudden you've gone from maybe two immediate issues that you're advising a client on to perhaps 10, right? 139 00:12:54,393 --> 00:12:58,673 And also those 10 are going to be the highest priority, the highest financial impact. 140 00:12:58,673 --> 00:13:05,269 And that's in my view, how technology can really be used to get these law firms to that. 141 00:13:05,269 --> 00:13:10,433 sort of 10,000 available hour uh example. 142 00:13:10,433 --> 00:13:11,533 That's how you get to it, right? 143 00:13:11,533 --> 00:13:14,365 Is to say, well, we're identifying these things much earlier. 144 00:13:14,365 --> 00:13:21,260 We're being proactive by going to our clients instead of being reactive to issues the client may already face, right? 145 00:13:21,260 --> 00:13:22,391 So that's how you grow. 146 00:13:22,391 --> 00:13:25,723 That's how you become sort of a much larger operation. 147 00:13:25,723 --> 00:13:29,025 But also I think these firms, as you know, care a lot about the profit per partner number, right? 148 00:13:29,025 --> 00:13:32,588 So that's how you increase that number as well. 149 00:13:32,588 --> 00:13:34,774 And that's sort of in our approach is that 150 00:13:34,774 --> 00:13:39,938 A solution like ours really helps law firms get to that model. 151 00:13:39,938 --> 00:13:49,244 But also, gets clients to pay for those fees as well, because the client now sees that what is exactly the value that's being added. 152 00:13:49,244 --> 00:14:00,852 So law firms can say, helped you save $500 million, or we helped you do an M &A transaction three months faster, because due diligence was accelerated because of data and 153 00:14:00,852 --> 00:14:02,813 analytics and so forth. 154 00:14:03,049 --> 00:14:14,238 That's sort of my view, but I think you're absolutely right about the governance structure within law firms uh that that makes it difficult to do a lot of this testing, so to speak. 155 00:14:14,238 --> 00:14:18,709 Everybody wants uh a tool that can be sold in the next month. 156 00:14:18,709 --> 00:14:20,894 And we went through this with our journey as well. 157 00:14:20,894 --> 00:14:25,648 was like, yeah, you have a great idea, but I want to start selling it in three months. 158 00:14:25,648 --> 00:14:31,677 And we were like, to actually develop all that data across 100 countries, across all these areas of law. 159 00:14:31,677 --> 00:14:40,031 It requires a lot of time and effort, but also then you have to deploy the actual platform, train the AI, and do all of that, right? 160 00:14:40,031 --> 00:14:43,953 So I think there is a bit of that lack of patience on that process. 161 00:14:43,953 --> 00:14:46,674 Everybody wants immediate results, as you alluded to. 162 00:14:46,674 --> 00:14:55,896 And we can do that as an independent company, but I'm pretty sure if you were within any law firm, having that type of runway and patience would be a challenge. 163 00:14:55,896 --> 00:15:10,614 Yeah, I try not to pitch what we do here at InfoDash on the podcast very much because that's not the goal, I'm going to have to talk about where we're putting our chips on the 164 00:15:10,614 --> 00:15:11,584 table. 165 00:15:11,625 --> 00:15:24,772 So we think that from a differentiation perspective, firms are not going to differentiate themselves with tools, Like off the shelf tools, Harvey, co-counsel, Legora, you name it. 166 00:15:24,772 --> 00:15:25,624 If your firm 167 00:15:25,624 --> 00:15:27,895 down the street can buy it just like you can. 168 00:15:27,895 --> 00:15:29,546 It's not a differentiator. 169 00:15:29,767 --> 00:15:38,091 But I think what is going to create real differentiation besides just maybe scale is data. 170 00:15:38,612 --> 00:15:43,855 And we uh have a labor and employment firm who we're partnering with. 171 00:15:43,855 --> 00:15:45,596 We just launched our Extranet product. 172 00:15:45,596 --> 00:15:49,148 So historically we've been um Intranet. 173 00:15:49,148 --> 00:15:52,960 We've been working on Extranet for about two and a half years and we just launched it. 174 00:15:53,132 --> 00:15:58,973 And what it does is it gives us the ability to develop client facing AI solutions. 175 00:15:59,234 --> 00:16:02,455 And um one, this use case is really neat. 176 00:16:02,455 --> 00:16:03,755 I'd love to get your take on it. 177 00:16:03,755 --> 00:16:13,918 So they compile all of this labor and employment data across all 50 states and their clients pay them a subscription to log in and peruse it and get updates and all that sort 178 00:16:13,918 --> 00:16:14,658 of stuff. 179 00:16:14,658 --> 00:16:21,494 And we pitched, Hey, what if instead of that, you take that data and you've got all of their 180 00:16:21,494 --> 00:16:30,246 employment documents, their employee handbook, their employment contracts, and you take Azure Open AI, because we deploy our solution in their tenant. 181 00:16:30,246 --> 00:16:35,268 So we have access to all their data and we also have access to Microsoft's capabilities. 182 00:16:35,268 --> 00:16:38,369 So we use Azure Open AI and we flag exceptions. 183 00:16:38,489 --> 00:16:45,651 And then we create work items in Power Automate and route them to the attorneys responsible for those relationships. 184 00:16:45,651 --> 00:16:50,692 And proactively, you could go, hey, know, non-competes just got banned in 185 00:16:50,830 --> 00:17:00,365 California and you've got 97 employment agreements with non-compete language, it becomes a revenue opportunity in addition to it. 186 00:17:00,365 --> 00:17:03,817 And it allows law firms to get like proactive, right? 187 00:17:03,817 --> 00:17:13,233 Instead of reactive, where the client has to go and find the exceptions themselves and then bring it to the law firm. 188 00:17:13,233 --> 00:17:15,334 It puts the law firm in the driver's seat. 189 00:17:15,334 --> 00:17:17,335 So I think we're aligned. 190 00:17:17,335 --> 00:17:19,498 Like I really do think that data 191 00:17:19,498 --> 00:17:27,023 is going to be um a real important ingredient in how firms differentiate themselves and then how they use it. 192 00:17:27,023 --> 00:17:27,597 You agree? 193 00:17:27,597 --> 00:17:28,717 Yeah, totally. 194 00:17:28,717 --> 00:17:29,537 I totally agree. 195 00:17:29,537 --> 00:17:38,537 think that that's why you name it with its core litics, our focus for really the first eight months of our development was on data, right? 196 00:17:38,797 --> 00:17:44,737 We said, of course, all the discussions is about AI and how AI is going to change the world. 197 00:17:44,737 --> 00:17:51,817 And all of that is obviously true, but AI doesn't really do anything for you if your data is terrible, right? 198 00:17:51,817 --> 00:17:57,410 So of course, there's all this information about hallucinations, And there's lots of public. 199 00:17:57,410 --> 00:18:03,195 data about examples of cases that ended up in court filings that never really happened and so forth. 200 00:18:03,195 --> 00:18:15,245 So what we did is focused on the data to say, we're going to collect all of this data, but we're going to collect it from primary sources, so actual court documents, court filings, 201 00:18:15,645 --> 00:18:18,137 actually government sources, what we call primary sources. 202 00:18:18,137 --> 00:18:23,972 We're not going to rely on just what somebody else is saying about illegal development and have AI going to read that. 203 00:18:23,972 --> 00:18:24,959 So we spent. 204 00:18:24,959 --> 00:18:37,857 a lot of time on the data, but also we had lawyers work with us to actually review our AI's output and say, this the type of output that you would use if you were working on a 205 00:18:37,857 --> 00:18:39,508 case or you're rising a client? 206 00:18:39,508 --> 00:18:49,854 The same thing with in-house counsel to say, in terms of your workflows, have to present to your board, you have to present to your executives, you have to deal with other 207 00:18:49,854 --> 00:18:51,825 stakeholders within the business. 208 00:18:51,853 --> 00:18:57,413 how is our AI sort of able to produce the information that you need that fits into those workflows? 209 00:18:57,413 --> 00:19:05,333 And I think having spent those eight months validating all of those things makes everything else that we're doing much easier. 210 00:19:05,333 --> 00:19:11,173 But also I think in the legal space, as you probably know, trust is critical, right? 211 00:19:11,173 --> 00:19:20,941 Perhaps more so than even some other industries where it's like lawyers, the first reaction to AI is typically, is it trustworthy? 212 00:19:20,941 --> 00:19:23,701 Can I actually go in and validate the information? 213 00:19:23,701 --> 00:19:28,541 And what we also did is we actually got lawyers that, for example, litigated certain cases. 214 00:19:28,541 --> 00:19:41,521 And we said, can you review our AIs description of the case and timelines and all the other things that we're producing in our platform to confirm that everything is correct? 215 00:19:41,981 --> 00:19:48,181 And most of the time, the reaction we got was, your information is actually even more detailed than we would have thought you could have gotten. 216 00:19:48,181 --> 00:19:50,541 So we knew we had information. 217 00:19:50,770 --> 00:19:55,204 in our platform, they were like, I didn't know you could do that using AI, right? 218 00:19:55,204 --> 00:19:57,346 So it was very good feedback. 219 00:19:57,346 --> 00:20:00,518 But I think to your point, I totally agree with the approach that you described. 220 00:20:00,518 --> 00:20:05,552 em I more broadly, I think it's really about using AI to fit into workflows, right? 221 00:20:05,552 --> 00:20:16,141 To say, data is obviously the starting point, but how does that data actually, how is it presented to actually let somebody who's doing a particular task, not only do that task 222 00:20:16,141 --> 00:20:18,925 faster, but to make it more valuable. 223 00:20:18,925 --> 00:20:20,145 for the business, right? 224 00:20:20,145 --> 00:20:26,045 So your example was right on about the risk that you describe with respect to employment, right? 225 00:20:26,045 --> 00:20:31,165 Someone can say, well, this is something that's happened and here's how it actually impacts us. 226 00:20:31,165 --> 00:20:32,045 Why do I care? 227 00:20:32,045 --> 00:20:33,245 Why should I care? 228 00:20:33,245 --> 00:20:35,025 And why should others in the company care, right? 229 00:20:35,025 --> 00:20:39,245 So that's the critical part, I think, which I think we're both kind of tied into the same thing, right? 230 00:20:39,245 --> 00:20:42,965 So say, well, data has to be actionable and it has to be quantitative. 231 00:20:42,965 --> 00:20:46,605 So somebody knows how to prioritize this information, right? 232 00:20:46,605 --> 00:20:48,299 You can't just say the world's on fire. 233 00:20:48,299 --> 00:20:56,469 You have to say, are the top five things that matter to you and how do they rank right amongst those top five items. 234 00:20:56,622 --> 00:21:09,163 You know, I'm not sure if people, I know people in legal that have been around to multiple firms realize this, but I'm not sure people outside of legal or people who have only been 235 00:21:09,163 --> 00:21:16,109 at one or two firms realize how bad law firm data is in what shape it is. 236 00:21:16,109 --> 00:21:24,488 um And this goes across the spectrum from structured, you know, rows and columns, you know, 237 00:21:24,488 --> 00:21:27,039 very little like master data management. 238 00:21:27,039 --> 00:21:31,193 I was how this InfoDash and the predecessor company got started. 239 00:21:31,193 --> 00:21:31,954 I'm a data guy. 240 00:21:31,954 --> 00:21:36,617 I used to work at Microsoft on the SQL team and I have an undergraduate degree in math. 241 00:21:36,617 --> 00:21:40,340 like I can geek out with the data with you. 242 00:21:40,340 --> 00:21:41,541 I'm a little rusty. 243 00:21:41,541 --> 00:21:52,188 I'm sure I'd have to, I'd be a little slow, slower than you, but um you know, it is, I have seen, ah it is incredible how 244 00:21:52,880 --> 00:21:56,162 the lack of data hygiene in law firms. 245 00:21:56,162 --> 00:21:59,505 again, structured and unstructured even more so. 246 00:21:59,505 --> 00:22:13,054 So a law firm DMS is comprised of 87 drafts and red lines of the same document and email attachments and binary objects, know, videos. 247 00:22:13,234 --> 00:22:15,616 it's in such poor shape. 248 00:22:15,616 --> 00:22:16,877 And this is universal. 249 00:22:16,877 --> 00:22:21,860 Like I have, it's not one firm, it's most firms. 250 00:22:22,070 --> 00:22:27,764 And then you also have the issue of there's been a lot of M &A in big law. 251 00:22:27,764 --> 00:22:40,563 So you'll take one mess when a law firm merges and import that into another mess of a different nature, which makes things even more difficult to untangle because, maybe they 252 00:22:40,563 --> 00:22:49,900 weren't using matter centricity in the one DMS or, you maybe they have another problem over here and then you slam them together and you've, really got something to untangle. 253 00:22:49,900 --> 00:22:50,680 So 254 00:22:52,044 --> 00:22:55,386 I wonder sometimes, I don't know, how do you see this playing out? 255 00:22:55,386 --> 00:23:03,172 Like how do firms get their data in order to capitalize on whatever's coming next? 256 00:23:05,005 --> 00:23:06,965 Yeah, I mean, I think there's a couple of things there. 257 00:23:06,965 --> 00:23:15,945 So I think the first step is really to solve the problem of external data and client data. 258 00:23:16,225 --> 00:23:26,385 So a lot of the legal services that are provided typically look at what is happening in terms of all the things that I described, regulations, litigation, et cetera, which 259 00:23:26,385 --> 00:23:29,005 generally is external information. 260 00:23:29,105 --> 00:23:31,173 But then the second part of that is 261 00:23:31,350 --> 00:23:32,170 the client, right? 262 00:23:32,170 --> 00:23:40,524 So the client's own uh data and the client's own information that translates all of those things into what actually matters for that particular client, right? 263 00:23:40,524 --> 00:23:52,499 So a lot of law firms, they try to collect that client information and like as you described, DMS and all kinds of folders and information, all this is all over the place. 264 00:23:52,739 --> 00:23:58,914 So one way I think to really start with the solving the problem is to look at 265 00:23:58,914 --> 00:24:08,759 how the external data and the client data can be handled so that law firms are not ingesting that data and having to circulate that internally and so forth. 266 00:24:08,759 --> 00:24:18,814 So one way that, a technology perspective, that most companies deal with that is to say, the client has the data on the cloud and their own cloud, can we use technology like APIs, 267 00:24:18,814 --> 00:24:28,255 et cetera, to really feed into that information and get lawyers the information that they really need and not have all of these documents and information that's 268 00:24:28,255 --> 00:24:30,446 in all of these different places. 269 00:24:30,446 --> 00:24:38,548 um And then with respect to law firm uh data itself, uh I I agree that's a huge, huge problem. 270 00:24:38,548 --> 00:24:43,119 lot of law firms have tried different systems and procedures and so forth. 271 00:24:43,119 --> 00:24:50,431 And some of them are still reluctant to rely too much on the cloud and the cloud infrastructure to manage data. 272 00:24:50,471 --> 00:24:58,273 So part of that is solving those governance issues about using cloud and cloud infrastructure to house this. 273 00:24:58,601 --> 00:24:59,742 this data. 274 00:24:59,742 --> 00:25:02,145 But I think the other part is also platforms, right? 275 00:25:02,145 --> 00:25:11,313 So if a lawyer is using a platform like ours, they don't really need to have that data on their network or their folders and so forth, right? 276 00:25:11,313 --> 00:25:22,794 So especially if it's litigation cases and regular regulations and disclosure requirements and news and so forth happening around the world, we have that data stored in our cloud, 277 00:25:22,794 --> 00:25:23,174 right? 278 00:25:23,174 --> 00:25:24,385 So I think it's... 279 00:25:24,461 --> 00:25:32,664 know, adopting the process of sort of using a platform to feed into that data would be a good, a good step. 280 00:25:32,664 --> 00:25:39,347 I mean, in the financial industry, for example, people don't download and save all of the company financial documents, right? 281 00:25:39,347 --> 00:25:43,879 So analysts don't have like thousands of financial statements and reports, right? 282 00:25:43,879 --> 00:25:47,551 They use Bloomberg, they use capital IQ, they use a bunch of other sources. 283 00:25:47,551 --> 00:25:54,133 So I think looking at those types of models would be, would be helpful to say, you know, we don't need every single. 284 00:25:54,156 --> 00:25:56,961 document saved in our files, right? 285 00:25:56,961 --> 00:26:02,296 We can access uh that information using external platforms. 286 00:26:02,296 --> 00:26:17,062 Yeah, I spent 10 years at Bank of America and uh in primary, primarily risk management roles and uh internal audit, uh anti-money laundering, compliance, consumer risk. 287 00:26:17,062 --> 00:26:24,145 I bounced around all over the place and got a really, again, kind of a good cross-sectional view. 288 00:26:24,145 --> 00:26:31,728 even the banks have extremely mature risk management and governance processes. 289 00:26:32,104 --> 00:26:41,211 and even they have challenges, you know it's like law firms, law firms have, their data is a mess for a lot of different reasons. 290 00:26:41,211 --> 00:26:54,611 I talked about kind of the M &A scenario and they've under invested, um they have under invested in technology and they have taken a very, very much a wait and see attitude and 291 00:26:54,611 --> 00:26:59,124 some of this is not all their fault, know outside council guidelines have 292 00:27:00,096 --> 00:27:02,598 slowed down their migration to the cloud. 293 00:27:02,598 --> 00:27:16,188 um But also, again, back to the partnership model, getting lawyers to even profile their documents in the DMS, assign metadata, um pretty basic. 294 00:27:17,469 --> 00:27:25,325 Two attributes before you check in a document, um you wouldn't believe how many say other or are blank. 295 00:27:25,325 --> 00:27:27,396 And it's just really 296 00:27:28,046 --> 00:27:30,437 creates a lot of challenges around data hygiene. 297 00:27:30,437 --> 00:27:36,758 um And there hasn't been that enforcement mechanism because it hasn't really mattered. 298 00:27:36,758 --> 00:27:37,409 You know what I mean? 299 00:27:37,409 --> 00:27:40,980 Like, yeah, it creates a little bit of friction here and there. 300 00:27:40,980 --> 00:27:44,440 But now we're entering into a phase where it does matter. 301 00:27:44,441 --> 00:27:56,094 And you having 97 versions of the same document with all the red lines and you can't tell which one's the final um and all these irrelevant emails, everything that was just dumped 302 00:27:56,094 --> 00:27:56,984 in there. 303 00:27:57,066 --> 00:28:04,725 Now it does matter because we want to leverage AI and we got to make sense of it. 304 00:28:04,725 --> 00:28:07,708 So it's a real challenge. 305 00:28:07,863 --> 00:28:16,941 Yeah, I mean, think there's even larger business uh opportunities for law firms to try to fix this issue, right? 306 00:28:16,941 --> 00:28:25,578 So for example, having been at both the big forehand and big law, one question that typically comes up is something happens around the world. 307 00:28:25,578 --> 00:28:31,733 There is a sanction, there's a tariff or environmental issue and so forth. 308 00:28:31,733 --> 00:28:36,657 And there's typically a scramble to say, who's the right person to advise? 309 00:28:36,713 --> 00:28:38,354 on that issue, right? 310 00:28:38,354 --> 00:28:40,485 Basically trying to win that work for that firm. 311 00:28:40,485 --> 00:28:49,980 And I think a lot of law firms have started the process of collecting CRM data on experiences of their attorneys. 312 00:28:50,281 --> 00:29:01,137 But still, what I've witnessed and seen is that because the data is so spread out, it's very hard for a law firm to even meet that kind of requirement to say, hey, this is the 313 00:29:01,137 --> 00:29:05,029 group of partners that gives us the best chance to 314 00:29:05,165 --> 00:29:09,065 to not only win the engagement, but to get the best result for the client. 315 00:29:09,065 --> 00:29:16,685 And on the client side, they're looking for the same thing, to say which lawyers are going to be the most helpful, if it's litigation, who has the best track record, who's going to 316 00:29:16,685 --> 00:29:20,865 win the case earlier, who's going to get a better result for me based on their experience. 317 00:29:20,865 --> 00:29:28,085 So there are some business examples of why the data can hinder those types of business opportunities for law firms. 318 00:29:28,285 --> 00:29:30,933 I mean, that's one of things that we're working on is to say, know, 319 00:29:30,933 --> 00:29:34,566 Our platform provides a lot of that data on the development itself, right? 320 00:29:34,566 --> 00:29:35,246 What has happened? 321 00:29:35,246 --> 00:29:36,518 Where has it happened? 322 00:29:36,518 --> 00:29:38,959 Obviously, and lot of that information. 323 00:29:38,959 --> 00:29:50,879 But if law firms have their data kind of set up so that we can help link it with the CRM type of solutions, then essentially law firms can be in a better position to win uh legal 324 00:29:50,879 --> 00:29:51,569 work. 325 00:29:51,586 --> 00:29:53,477 Yeah, so yeah, that's interesting. 326 00:29:53,477 --> 00:29:55,818 I know a little bit about this because we integrate. 327 00:29:55,818 --> 00:30:05,243 we uh we provide a firm directory and we do what we call user profile enrichment and we tap into that experience data and that usually. 328 00:30:06,545 --> 00:30:10,587 Foundation is kind of the the big platform. 329 00:30:10,587 --> 00:30:15,790 Our friends at Lutera uh have that manages experience data. 330 00:30:15,790 --> 00:30:19,792 Do you compete with uh a foundation at all? 331 00:30:20,938 --> 00:30:22,259 No, directly. 332 00:30:22,259 --> 00:30:26,945 No, no, we really compete with those companies. 333 00:30:28,078 --> 00:30:31,749 Okay, so um I wanted to talk a little bit about agents. 334 00:30:31,749 --> 00:30:37,251 You and I had a good conversation about agents and I thought you had some interesting insights. 335 00:30:37,291 --> 00:30:49,214 We talked about the development of domain-specific AI agents in certain practice areas like employment law, trade law, ESG, kind of other practice areas. 336 00:30:49,214 --> 00:30:55,736 Are you guys playing around with any of that or do you have any vision for what that might look like? 337 00:30:56,625 --> 00:31:03,849 Yeah, so basically, our vision is really to build a vertical AI agent network. 338 00:31:03,849 --> 00:31:09,601 And the way we were doing that process is, as we discussed, starting with the data, right? 339 00:31:09,601 --> 00:31:15,254 So bottom up, getting the data correct, validating the data, and everything we've talked about so far. 340 00:31:15,354 --> 00:31:21,463 But then the next step is really to then make the AI and AI agents use that data. 341 00:31:21,463 --> 00:31:23,894 to work into the workflows that people are doing. 342 00:31:23,894 --> 00:31:39,643 So our vision is really to say, if you are a lawyer, let's say in ESG or trade or cyber, tax, et cetera, that our uh AI agents is essentially working with that particular lawyer 343 00:31:39,643 --> 00:31:43,685 to take the information that we have in our platform and to make it actionable. 344 00:31:43,685 --> 00:31:50,870 So if the lawyer wants to do legal research, that's probably one of the basic tasks that we do, or if they want to produce uh 345 00:31:50,870 --> 00:31:53,521 memo or client alert and so forth, right? 346 00:31:53,521 --> 00:32:00,744 Specific to their, not only their legal area, but also the information that they've been looking at in the past, right? 347 00:32:00,744 --> 00:32:05,206 So we track things like, you what is the user actually spending time on, right? 348 00:32:05,206 --> 00:32:13,179 So what we call behavior analytics and to customize the AI agent so that the user is getting that information. 349 00:32:13,179 --> 00:32:14,859 So that's, guess, know, step one, right? 350 00:32:14,859 --> 00:32:19,661 But then what we do is say, well, that person is in one legal area. 351 00:32:19,735 --> 00:32:29,210 But then when we roll up the different workflows for lawyers within a particular area, we want the AI agents essentially to talk to each other. 352 00:32:29,210 --> 00:32:45,319 So if somebody asks a business question, like there's a new tariff, what are my options as far as uh product flows and so forth that I can do to essentially eliminate or minimize my 353 00:32:45,319 --> 00:32:46,659 tariff impact? 354 00:32:46,900 --> 00:32:49,089 And what we know is from the legal space is that 355 00:32:49,089 --> 00:32:51,120 Tariffs is not simply a trade issue. 356 00:32:51,120 --> 00:32:59,495 Tariffs impact a whole bunch of other things, because what you can't do is say, I've already had a tariff, but now I've got a huge tax liability in other areas that all of 357 00:32:59,495 --> 00:33:01,086 sudden has come up. 358 00:33:01,086 --> 00:33:09,941 So uh what we have to do with these agents and what we want to do with scoreletics is say these agents should talk to each other and say, here's a potential solution to the tariff 359 00:33:09,941 --> 00:33:12,622 problem that solves the tariff problem. 360 00:33:12,622 --> 00:33:17,931 But the agent is looking at the tax rules and the employment rules and cyber, et cetera. 361 00:33:17,931 --> 00:33:22,006 are going to also chime in and to say, what is the impact on those areas, right? 362 00:33:22,006 --> 00:33:33,579 So at the end, our overall agent gives the company an actionable intelligence to say, here is a solution or potential solutions that could work that reflects the input from all of 363 00:33:33,579 --> 00:33:36,161 these different areas. 364 00:33:36,684 --> 00:33:38,625 Well, that is super interesting. 365 00:33:38,625 --> 00:33:41,787 know, a couple of things pop into my head when you talk about that. 366 00:33:42,087 --> 00:33:57,056 Are you borderline, are you walking the line on the practice of law there um with advising clients and interpreting regulations and creating guidance? 367 00:33:57,056 --> 00:33:58,006 Is that? 368 00:33:59,310 --> 00:34:00,710 Yeah, no, I think it's a good question. 369 00:34:00,710 --> 00:34:04,770 And that's the question that we deal with quite often. 370 00:34:05,490 --> 00:34:08,750 No, we are not practicing law. 371 00:34:09,030 --> 00:34:22,870 If you try to that clear, really what we're trying to do is to help come up with information that lawyers can use to then either they have outside counsel review and time 372 00:34:22,870 --> 00:34:25,570 in and make that more actionable. 373 00:34:26,337 --> 00:34:31,970 But what we're trying to do is improve that process of the actual decision-making, right? 374 00:34:31,970 --> 00:34:36,843 To say, you know, here are the potential options that you may have. 375 00:34:36,843 --> 00:34:44,687 Of course, have to, like I said, you have to have lawyers and consultants and audit company firms and so forth to review. 376 00:34:44,687 --> 00:34:49,030 But we've actually saved you a whole bunch of time in that process. 377 00:34:49,030 --> 00:34:55,213 if that process of doing it manually would have been a month, maybe you've done it in three or four days. 378 00:34:55,327 --> 00:35:05,392 It still requires the same people to be involved in the process, but we're not looking to replace lawyers or to say that this is actually legal advice that the company can simply 379 00:35:05,392 --> 00:35:07,895 just take from our platform and run with it, right? 380 00:35:07,895 --> 00:35:12,391 It does require legal input and lawyer review and so forth. 381 00:35:12,600 --> 00:35:13,120 Interesting. 382 00:35:13,120 --> 00:35:17,851 Yeah, we just went through a process called QSBS, qualified small business stock. 383 00:35:17,851 --> 00:35:24,983 It's a federal government innovation program where if you're a, you got to check all these boxes, which we do. 384 00:35:24,983 --> 00:35:30,585 And if you're an LLC, you convert to a C corp and essentially you have a five year holding period. 385 00:35:30,585 --> 00:35:35,596 And if you maintain that period, that holding period, you get 10 times your basis. 386 00:35:35,596 --> 00:35:41,748 So you get evaluation when you convert to a C corp and then you get 10 times that essentially tax free. 387 00:35:42,102 --> 00:35:44,453 upon a liquidity event. 388 00:35:44,753 --> 00:36:00,918 Great, very, I mean, it's awesome for small software companies like us, but in the process of getting there, we had to pull in, know, tax, general counsel, um counsel that's 389 00:36:01,398 --> 00:36:04,819 specific to, has QSBS expertise. 390 00:36:04,819 --> 00:36:08,420 We even had a third party, um they're a fellow. 391 00:36:08,546 --> 00:36:18,083 TLTF portfolio company called QSBS expert, essentially a non lawyer, but consultant, former ENY guy who knew this stuff inside and out. 392 00:36:18,083 --> 00:36:20,985 So I had all these parties at the table. 393 00:36:20,985 --> 00:36:25,187 Like one phone call would cost like thousands of dollars. 394 00:36:26,429 --> 00:36:29,651 in addition to my team's time, that's just like external costs. 395 00:36:29,651 --> 00:36:37,436 If I could find a way to prime the pump a little bit and you know, have some uh 396 00:36:37,474 --> 00:36:42,049 have some things together that we could review rather than kind of starting from scratch. 397 00:36:42,049 --> 00:36:48,537 And it would eliminate a lot of the, it would make things much more efficient and streamlined. 398 00:36:48,537 --> 00:36:55,704 And it sounds like you're straddling the fence tax and law regulations. 399 00:36:56,109 --> 00:37:01,689 Yeah, I mean, we're looking to cover essentially every area of law. 400 00:37:01,929 --> 00:37:08,309 know, cyber I mentioned and IP law and employment law, tax, et cetera. 401 00:37:08,389 --> 00:37:15,569 It's going to be a process to actually get all of that data and integrate and get the AI agents all integrated as well. 402 00:37:16,529 --> 00:37:22,009 But yeah, I our view really is that the process as you described it is very manual. 403 00:37:22,009 --> 00:37:24,653 A lot of people are involved, but also 404 00:37:24,653 --> 00:37:30,213 we think it can be a lot more efficient if all of those people have the information that they need. 405 00:37:30,453 --> 00:37:36,093 So if, for example, the AI tells all the parties, are the rules. 406 00:37:36,093 --> 00:37:39,133 Here are the rules in all the countries. 407 00:37:39,633 --> 00:37:47,433 AI can make some recommendations, but then say, if you want to actually validate any of this, we can give you the underlying core documents, cases. 408 00:37:47,553 --> 00:37:48,933 We can give you the regulations. 409 00:37:48,933 --> 00:37:50,793 We can give you all the underlying data. 410 00:37:50,793 --> 00:37:52,110 So instead of having 411 00:37:52,110 --> 00:38:01,510 lawyers go out and find that information across all of these countries and areas of law, the AI is actually put you in a position where you're actually validating, right? 412 00:38:01,510 --> 00:38:07,310 More than going out and having associates and others, knowledge management lawyers, research, all of these things. 413 00:38:07,310 --> 00:38:18,050 Because in our view is that's one of the best benefits of AI is that, you you don't need to do that research and that review by hand, but manually every single time you have a 414 00:38:18,050 --> 00:38:21,865 transaction or an issue, there are a of efficiencies to having. 415 00:38:21,865 --> 00:38:23,325 AI do that for you. 416 00:38:23,325 --> 00:38:26,606 again, our view is we need to be transparent, right? 417 00:38:26,606 --> 00:38:31,748 All the sources, anybody in our platform can click on the source information. 418 00:38:31,748 --> 00:38:43,301 So like I said, documents, underlying news stories, the government sites, et cetera, and validate it down to the last sentence, so to speak, in the actual rules. 419 00:38:43,301 --> 00:38:48,892 So it has to be actionable, but also has to be auditable by anybody. 420 00:38:48,892 --> 00:38:51,553 And that's how we built our platform. 421 00:38:52,055 --> 00:38:58,549 So I, mean, M &A is actually a very good example of this as well. 422 00:38:58,549 --> 00:39:09,454 So as you can imagine in &A transaction, you have a whole bunch of business teams, legal teams that are around a table talking about how to do due diligence, where to focus on. 423 00:39:09,454 --> 00:39:20,621 And again, our view is if a platform like ours really tells you what are the key legal risks, for example, across all the countries where the target operates, also based on 424 00:39:20,621 --> 00:39:21,617 their, you 425 00:39:21,617 --> 00:39:27,120 how they operate, are they manufacturing, are they warehouse, are they service provider, do they work with third parties. 426 00:39:27,120 --> 00:39:36,067 That information should make that process a lot more efficient because people know here's where I need to focus my due diligence effort versus starting with blank slate and say, 427 00:39:36,067 --> 00:39:45,849 I'm just going to canvas the world and go country by country and really engage lawyers in all those countries to tell me what are the risks in that country. 428 00:39:45,985 --> 00:39:50,701 So the savings obviously both in cost but also timeliness is really important. 429 00:39:50,701 --> 00:39:54,255 That's what we feel AI can really add a lot of value. 430 00:39:54,902 --> 00:39:55,672 Interesting. 431 00:39:55,672 --> 00:40:09,288 Well, there's one topic I really wanted to talk about that you've got some perspective on given your time at KPMG and that is the big four entering the US legal market and the most 432 00:40:09,288 --> 00:40:23,862 recent iteration of that with KPMG law in Arizona who has been granted approval and you know, I'm I see a lot of I'll call it complacency 433 00:40:23,862 --> 00:40:32,826 in big law with respect, they're not, most people aren't that worried about um the big four entering in the legal market. 434 00:40:32,826 --> 00:40:35,097 And I feel like they should be a little bit. 435 00:40:35,097 --> 00:40:50,963 um And a lot of people point to other things like, you know, the big four have tried in the U S before, you know, the legal services act in the UK enabled non lawyer ownership of 436 00:40:50,963 --> 00:40:52,834 law firms and 437 00:40:52,834 --> 00:40:54,675 nothing really has changed there. 438 00:40:54,675 --> 00:40:59,006 In fact, the ones that have tried have haven't performed that great. 439 00:40:59,006 --> 00:41:09,669 um But I think what's different now is the means of legal production are changing and technology is now going to be a key driver. 440 00:41:09,669 --> 00:41:11,319 And that part isn't up for debate. 441 00:41:11,319 --> 00:41:20,644 If you don't think that's true, then I don't know what to say to you, but it's going to be a key component. 442 00:41:20,644 --> 00:41:23,324 of how legal services get delivered. 443 00:41:23,504 --> 00:41:35,464 And when I look at what the big four have with KPMG being the smallest at 40 billion, Deloitte being the biggest at 80 billion, you know, so six, five, six times the size of 444 00:41:35,464 --> 00:41:38,804 the biggest law firm in the world at K &E. 445 00:41:39,744 --> 00:41:44,804 DNA is in, I'm sorry, innovation is in their DNA. 446 00:41:45,084 --> 00:41:47,204 And it's not illegal. 447 00:41:47,204 --> 00:41:50,654 In fact, it's really innovation 448 00:41:50,654 --> 00:41:55,586 is a uh real struggle um in law firms. 449 00:41:55,586 --> 00:41:59,827 It's starting to get easier, but historically it's been a challenge. 450 00:42:00,068 --> 00:42:03,519 So I see some real advantages that the big four have. 451 00:42:03,519 --> 00:42:14,194 And I don't think the big four is going to come in and eat Big Law's lunch overnight, but I think incrementally they're going to start with the blocking and tackling, the ALSP type 452 00:42:14,194 --> 00:42:16,074 of services. 453 00:42:16,074 --> 00:42:20,676 then they're going to work their way up the complexity. 454 00:42:20,868 --> 00:42:26,788 food chain over time as they build credibility in the marketplace and demonstrate that they can deliver. 455 00:42:27,508 --> 00:42:42,468 you know, I had the chief legal officer at DHL on maybe two, three episodes ago, and I asked him, would you send work to the big four who set up a law firm? 456 00:42:42,468 --> 00:42:44,308 said, hell yeah, I would. 457 00:42:44,768 --> 00:42:50,788 You know, so, I mean, they have 97 % of the Fortune 500 on the 458 00:42:50,788 --> 00:42:53,608 audit side market share. 459 00:42:53,728 --> 00:42:55,828 So huge install base there. 460 00:42:55,828 --> 00:42:56,088 I don't know. 461 00:42:56,088 --> 00:43:03,368 What is your take on how, what impact the big four entering the market in the U S. 462 00:43:03,528 --> 00:43:04,362 Um, how do you see that? 463 00:43:04,362 --> 00:43:11,188 I think this is a topic that think a lot of law firms obviously are talking about as well. 464 00:43:11,188 --> 00:43:24,520 Having spent time both at, as you noted, with the big four and big law, I think one of the challenges for accounting firms generally is, you're right, they're much larger, the 465 00:43:24,520 --> 00:43:30,685 resources they have, historically they've been a lot more innovative than obviously law firms have. 466 00:43:30,871 --> 00:43:33,902 But they also have sort of governance issues of their own. 467 00:43:33,902 --> 00:43:39,084 uh For example, they have so many different services that they offer, right? 468 00:43:39,084 --> 00:43:49,419 Often structured across practice, know, kind of broad areas, assurance and tax, and the ones that still have consulting, right, as well, that sometimes they're not obviously 469 00:43:49,419 --> 00:43:52,470 aligned when it comes to innovation strategy, right? 470 00:43:52,470 --> 00:44:00,558 So for example, compliance work, audit work, some of those types of things are high volume, high fees. 471 00:44:00,558 --> 00:44:08,578 type of work, whereas some of the things that we're talking about with respect to innovation really help to automate those tasks. 472 00:44:09,698 --> 00:44:13,838 as an economist, I can tell you what happens with those types of things, right? 473 00:44:13,838 --> 00:44:22,638 Is that once you do technology that is really improving productivity, ultimately that savings is passed along to your customer, right? 474 00:44:22,638 --> 00:44:25,798 So you're not going to charge them more, certainly not the same. 475 00:44:26,477 --> 00:44:34,277 if you implement efficiencies and how quickly you draft a contract or a compliance document and so forth. 476 00:44:34,277 --> 00:44:48,577 So I think the challenge for the accounting firms is really going to be, can they really get past that need or that kind of request internally to say the first thing they should 477 00:44:48,577 --> 00:44:54,757 be doing is diving headfirst into the areas of law that are the ones that 478 00:44:55,021 --> 00:45:04,761 AI is actually trying to automate the contracts, as I mentioned, drafting documents, maybe even some high level review of the documents and so forth, or whether they can actually 479 00:45:04,761 --> 00:45:08,541 look at innovation as a way to transform law. 480 00:45:08,641 --> 00:45:17,101 So going to the consulting side, they have lot of experience with all kinds of technology there, deploying it for clients as well. 481 00:45:17,101 --> 00:45:21,825 Can they really connect that part of the business to say, we can actually 482 00:45:21,825 --> 00:45:27,269 help to use technology to transform how legal services are provided, right? 483 00:45:27,269 --> 00:45:38,589 So more of a, we're providing a value to the client that is saving them a bunch of money or creating new revenue sources for that client versus, like I said, doing what they're 484 00:45:38,589 --> 00:45:41,411 already doing, but doing it faster and cheaper than big law. 485 00:45:41,411 --> 00:45:51,726 think that sort of that dynamic intention between the two types of opportunities may make it difficult for the big four to actually make 486 00:45:51,726 --> 00:45:55,786 headway in the legal space in the near future. 487 00:45:56,686 --> 00:45:58,786 But if they can somehow get past that, I agree. 488 00:45:58,786 --> 00:46:02,306 think the opportunity with them is obviously really, really large. 489 00:46:02,366 --> 00:46:07,606 I think the billable hour model is, again, law firms have it, but big four have it as well. 490 00:46:07,606 --> 00:46:18,605 So it's also a question of how do you actually charge a client for the services if the model is billable hour, then obviously the incentives, regardless of whether you're 491 00:46:18,605 --> 00:46:27,025 If you're a big four or big law is the same, you want to maximize the number of hours and billing rates that you're charging to clients and not really come up with a model. 492 00:46:27,025 --> 00:46:33,665 Like I said, you're adding value, maybe a charging based on the value that you're adding or value based pricing and so forth. 493 00:46:33,705 --> 00:46:34,865 So I think that's going to be the challenge. 494 00:46:34,865 --> 00:46:44,325 think in my personal view, I think the internal dynamics at big four will make this a problem and a challenge as you've noted in Europe, right? 495 00:46:44,325 --> 00:46:46,905 They've been in Europe for a while practicing law. 496 00:46:47,053 --> 00:46:54,895 and it hasn't really translated to them becoming huge players in the same sense as some of the big law firms are. 497 00:46:54,895 --> 00:47:04,078 But if they maybe think about acquisitions, right, that's typically a way for lot of these companies to say our internal culture governance makes some of these things a big 498 00:47:04,078 --> 00:47:04,578 challenge. 499 00:47:04,578 --> 00:47:13,440 But if you make an acquisition of companies that are already doing some of these things, that might be a better approach, I think, for certainly the big four, maybe others to get 500 00:47:13,440 --> 00:47:14,956 into the legal space. 501 00:47:14,956 --> 00:47:15,496 Yeah. 502 00:47:15,496 --> 00:47:18,457 Now the, the, the big four definitely have their challenges. 503 00:47:18,457 --> 00:47:32,151 mean, they're a partnership model as well, but they've managed to figure out how to scale and you know, lawyers, law firms have really been limited by, in my opinion, um, law firm 504 00:47:32,151 --> 00:47:45,104 leadership is all lawyers and you know, there's a certain lens, legal lens that has a certain shape and shapes the perspective, uh, in a way that 505 00:47:45,104 --> 00:47:51,747 is you need the more diversity you have in leadership and different angles on things, the better. 506 00:47:52,488 --> 00:48:01,453 The lawyer mindset is well documented as being risk averse and conservative and comfortable with the status quo and all the things that Dr. 507 00:48:01,453 --> 00:48:06,066 Larry Richard has documented studying lawyers for 35 years. 508 00:48:06,066 --> 00:48:14,300 that's not always the bit, those aren't always aligned to the best leadership um capabilities. 509 00:48:14,434 --> 00:48:17,810 you know, especially in the entrepreneurship world, like you gotta take risks. 510 00:48:17,810 --> 00:48:19,478 You gotta be uncomfortable. 511 00:48:19,478 --> 00:48:24,803 You gotta be comfortable with uncertainty and taking chances and so. 512 00:48:24,803 --> 00:48:25,737 um 513 00:48:25,737 --> 00:48:27,397 think I totally agree. 514 00:48:27,397 --> 00:48:32,057 And I think that's what my message to many law firms is, right? 515 00:48:32,057 --> 00:48:44,357 Is that actually to fight off competition and to really succeed, you have to be better at innovation and technology than the big four and others that are trying to get into the 516 00:48:44,357 --> 00:48:45,457 legal space. 517 00:48:45,777 --> 00:48:49,917 And oftentimes, we discussed at the beginning, it's not just developing it in-house, right? 518 00:48:49,917 --> 00:48:52,357 As we discussed, that has a lot of challenges. 519 00:48:52,437 --> 00:48:54,283 But a lot of law firms are looking at, you 520 00:48:54,283 --> 00:48:55,574 becoming investors, right? 521 00:48:55,574 --> 00:49:10,574 So investing in technology that they think could make them uh more productive, adding more value to clients and so forth without having to have that internally within their firm. 522 00:49:10,574 --> 00:49:20,171 So I think we will see an increase in law firms that are participating more and more in sort of innovation in that context in terms of becoming investors, but also hands-on 523 00:49:20,171 --> 00:49:20,851 investors, right? 524 00:49:20,851 --> 00:49:24,119 Giving these startups like us access to the lawyers to 525 00:49:24,119 --> 00:49:30,782 test out our products, get feedback and work with them on the workflows and the AI agents, everything which we talked about, right? 526 00:49:30,782 --> 00:49:34,074 To make it actionable for them and benefiting them. 527 00:49:34,074 --> 00:49:43,108 Specifically, I think that's one way I think a law firms will help or stay ahead of the competition is actually beat the competition at their own game by innovation. 528 00:49:43,108 --> 00:49:49,912 But like I said, I think it's, in my opinion, it's going to be through investment versus trying to do it themselves in house. 529 00:49:49,912 --> 00:50:00,120 Yeah, well, you know, the Legal Tech Fund, their first fund, I don't know if it was exclusively law firms as limited partners, but I know several big law firms were the LPs 530 00:50:00,120 --> 00:50:01,341 in that fund. 531 00:50:01,341 --> 00:50:07,745 So that's one proxy through which law firms have, you know, generated some exposure in the legal tech market. 532 00:50:07,745 --> 00:50:12,349 um And it's been great, you know, they're investors in us. 533 00:50:12,349 --> 00:50:13,570 We love working with them. 534 00:50:13,570 --> 00:50:15,231 It's been a good relationship. 535 00:50:15,231 --> 00:50:17,166 um 536 00:50:17,166 --> 00:50:18,786 Well, this has been a great conversation, Moyes. 537 00:50:18,786 --> 00:50:30,110 I appreciate you telling us about scorelytics and it's a fascinating, I think our listeners, you know, that work in cam and innovation functions at these big law firms and 538 00:50:30,110 --> 00:50:34,821 probably inspired some, some, some thought. 539 00:50:34,821 --> 00:50:45,024 And, um, I think we're going to see more of this model in legal where there's spinoffs and subsidiaries and sister companies. 540 00:50:45,442 --> 00:50:46,247 It just makes sense. 541 00:50:46,247 --> 00:50:49,655 So I really appreciate you uh sharing your thoughts with us. 542 00:50:49,729 --> 00:50:50,281 No, that's 10. 543 00:50:50,281 --> 00:50:52,003 Thank you so much for having me on. 544 00:50:52,003 --> 00:50:53,252 Yeah, this has been great. 545 00:50:53,252 --> 00:50:54,612 Alright, good stuff. 546 00:50:54,612 --> 00:50:56,572 Alright, enjoy the rest of your afternoon. 547 00:50:56,952 --> 00:50:57,992 Thanks, Mois. -->