1 00:00:06,367 --> 00:00:12,809 Welcome to LawNext PR, the podcast where we put a spotlight on the latest news coming out of the legal tech industry. 2 00:00:12,889 --> 00:00:22,232 This is Bob Ambrosie and in each sponsored episode of LawNext PR, I interview a legal tech company about its latest news and developments. 3 00:00:22,492 --> 00:00:31,066 topic today is the launch of Lighthouse AI Search by the global e-discovery and information governance company, Lighthouse. 4 00:00:31,066 --> 00:00:38,894 And joining me to discuss this news is Dan Brassil executive director, search and information retrieval at Lighthouse. 5 00:00:38,894 --> 00:00:40,531 Dan, Welcome to the show. 6 00:00:41,344 --> 00:00:42,576 Thank you for having me. 7 00:00:43,209 --> 00:00:53,337 So before we get into talking about what you've announced here, can I ask you to kind give me in a nutshell what Lighthouse is for anybody who might not be familiar with the 8 00:00:53,337 --> 00:00:54,187 company? 9 00:00:54,640 --> 00:00:57,140 Yeah, I think you did a great job at the very beginning, right? 10 00:00:57,140 --> 00:01:03,820 We're a global e-discovery and information governance company. 11 00:01:03,820 --> 00:01:07,760 We've been around for, I think, like since 1995. 12 00:01:07,920 --> 00:01:20,292 We've been in this space for a really long time and really evolving with the technology, but also pushing forward, being on the vanguard of what's possible as we incorporate new 13 00:01:20,292 --> 00:01:22,301 and different types of technology like 14 00:01:22,301 --> 00:01:23,231 AI. 15 00:01:23,419 --> 00:01:24,240 Yeah. 16 00:01:25,145 --> 00:01:27,313 And what about your own background? 17 00:01:27,313 --> 00:01:29,659 What's, how did you get into all of this? 18 00:01:29,659 --> 00:01:37,670 Yeah, well, so actually, this month, it will be my 20th anniversary in the industry. 19 00:01:37,750 --> 00:01:39,030 Thank you. 20 00:01:39,030 --> 00:01:44,482 Like a lot of people, you you don't choose eDiscovery, eDiscovery chooses Right. 21 00:01:44,482 --> 00:01:47,162 I have a background in linguistics. 22 00:01:47,182 --> 00:01:52,260 And I found my way into at the time back in 2005. 23 00:01:52,260 --> 00:02:02,500 And they were doing really cutting edge things about modeling language from its information retrieval space for responsiveness and hot docs. 24 00:02:02,560 --> 00:02:15,375 And it was really, really interesting that I was able to take the training I had about looking at language sort of how variable can be to help lawyers do practical things with 25 00:02:15,375 --> 00:02:16,455 my training. 26 00:02:16,455 --> 00:02:19,815 And I think that's the cool thing about 27 00:02:19,885 --> 00:02:30,676 the AI, right, which is now we have large language models that can also really understand the variability of human language and being able to melt. 28 00:02:30,676 --> 00:02:37,005 So my expertise with this really powerful analytic tool is really interesting. 29 00:02:37,005 --> 00:02:38,126 Yeah, that is really interesting. 30 00:02:38,126 --> 00:02:43,019 mean, legal work is all about words, ultimately, and it comes down to that. 31 00:02:43,700 --> 00:02:50,476 So Lighthouse has just announced the launch of Lighthouse AI Search, and we can get into kind of more details about what it is. 32 00:02:50,476 --> 00:02:54,908 But at a high level, why don't you kind of describe what it is that you've just, what you've launched here. 33 00:02:54,973 --> 00:03:10,640 Yeah, you know, at a high level, Lighthouse AI Search is an AI driven or AI first, as we like to say, search platform that allows someone to just ask a normal everyday question of 34 00:03:10,640 --> 00:03:11,680 the tool. 35 00:03:11,680 --> 00:03:21,719 And what you get back are answers to that question as opposed to getting back documents that then you have to find the answers in, right? 36 00:03:21,719 --> 00:03:32,208 It does give you the documents, right, that it generates the answers from so that you can always, you know, do that sort of level of analysis, but it gives you back an answer to 37 00:03:32,208 --> 00:03:34,599 your question, which is incredibly powerful. 38 00:03:34,599 --> 00:03:35,060 Yeah. 39 00:03:35,060 --> 00:03:39,625 So you're able to ask questions against the corpus of documents that you have. 40 00:03:39,625 --> 00:03:43,748 Can you kind of give me an example, perhaps, of a question you might ask? 41 00:03:44,461 --> 00:03:48,192 Yeah, so again, like I said, you can ask a simple question. 42 00:03:48,192 --> 00:03:59,359 It can be as simple as what are Juul's products, again, I'm talking about what is the product we're talking about, the Juul data that we were using to show a client as publicly 43 00:03:59,359 --> 00:03:59,951 available. 44 00:03:59,951 --> 00:04:06,476 And then we asked other questions like what were the first flavors that Juul had, right? 45 00:04:06,497 --> 00:04:17,754 Again, these are things that if you didn't know the answer to them, you would not, it would be a hard time figuring out how to craft a search to find all that information, 46 00:04:17,754 --> 00:04:17,994 right? 47 00:04:17,994 --> 00:04:26,279 You'd have to sort of dig through the documents that a search like flavor within five of Juul would give you, right? 48 00:04:26,279 --> 00:04:30,011 So this just gives you a list and says, these were the flavors at launch. 49 00:04:30,011 --> 00:04:32,813 If that's your question, what were the flavors at launch? 50 00:04:32,813 --> 00:04:34,029 So again, it's 51 00:04:34,029 --> 00:04:45,208 It's sort of what it allows you to do is just ask a question as if you're asking a colleague or if you are a senior associate or a partner at a law firm, what you would ask 52 00:04:45,208 --> 00:04:46,923 a junior associate to do. 53 00:04:46,923 --> 00:04:47,655 Right. 54 00:04:47,655 --> 00:04:49,127 I need to find out this. 55 00:04:49,127 --> 00:04:50,837 Can you go find it for me? 56 00:04:50,837 --> 00:04:53,923 it sounds like it may be a situate like the example you just gave. 57 00:04:53,923 --> 00:04:59,457 You probably could have found that answer eventually without AI search, but it would have taken you a long time. 58 00:04:59,457 --> 00:05:04,511 And maybe you wouldn't have been absolutely sure that you were covering all the bases in terms of what you were looking for. 59 00:05:04,902 --> 00:05:05,803 Yeah, most definitely. 60 00:05:05,803 --> 00:05:12,620 You know, as someone who built his career on helping people use linguistics to find things, right? 61 00:05:12,620 --> 00:05:18,385 I, one of my expertise in this field is the crafting of search terms, right? 62 00:05:18,385 --> 00:05:23,310 Using syntax and then you have to know the arcana of syntax, right? 63 00:05:23,310 --> 00:05:27,353 and that can be difficult because sometimes you're like, did I get all the right words, right? 64 00:05:27,353 --> 00:05:30,367 Because that's how search engines work. 65 00:05:30,367 --> 00:05:40,773 and then what you get back when you write when you do a search like that you get that document again you don't get back the answer to your question with Lighthouse AI search. 66 00:05:40,773 --> 00:05:42,101 You don't need to know. 67 00:05:42,101 --> 00:05:54,114 search syntax you just need to ask your question in a normal way then again what you get back is the answer to your question backed up by the documents that. 68 00:05:54,114 --> 00:05:57,878 the AI search was able to use to generate your question. 69 00:05:57,878 --> 00:06:00,101 Again, I think it's really powerful, 70 00:06:00,204 --> 00:06:09,692 So, I mean, you know, traditionally in e-discovery, you would have keyword search, Boolean search, as you say, there are also tools like technology assisted review or predictive 71 00:06:09,692 --> 00:06:11,514 coding or those kinds of things. 72 00:06:11,514 --> 00:06:15,268 So where does this fit in that universe? 73 00:06:15,268 --> 00:06:17,170 Does this replace those kinds of search? 74 00:06:17,170 --> 00:06:19,242 Is it a supplement to those kinds of search? 75 00:06:19,242 --> 00:06:22,662 I think it works in tandem with a lot of different types of things. 76 00:06:22,922 --> 00:06:41,582 So first and foremost, this is a tool that allows litigation teams to get early insight to query their documents when they're doing sort of like investigations or litigation. 77 00:06:41,582 --> 00:06:48,586 It's not just an e-discovery tool, more of a, you know, again, I think investigation is a great example. 78 00:06:48,586 --> 00:06:52,186 or early case assessment, is there a there there, right? 79 00:06:52,186 --> 00:06:59,261 Even prior to sort of litigation or anything like that, you can say, what is my exposure, right? 80 00:06:59,261 --> 00:07:02,781 And you can just ask the tool this. 81 00:07:02,781 --> 00:07:12,721 Now, going back to your question, how does this sit along, sort of say predictive AI or predictive coding, I think you said, and other types of tools. 82 00:07:12,721 --> 00:07:17,121 Again, it has a different use case. 83 00:07:17,549 --> 00:07:23,222 I would say this about what AI search or Lighthouse AI search does, right? 84 00:07:23,222 --> 00:07:37,600 It is a combination of predictive AI to find the information you're looking for in your question and then generative AI to generate the answer to the question you ask based off 85 00:07:37,600 --> 00:07:41,742 of the information it found using the predictive AI. 86 00:07:41,742 --> 00:07:46,851 I know I got into the weeds there, but I think it's really important to understand that we're using 87 00:07:46,851 --> 00:07:51,873 the best of both worlds when it comes to the different types of AIs that are out there. 88 00:07:52,044 --> 00:07:52,305 Right. 89 00:07:52,305 --> 00:07:53,448 That makes a lot of sense. 90 00:07:53,448 --> 00:07:59,735 You talked about the fact that this search not only gives you the answer, but gives you. 91 00:08:00,018 --> 00:08:05,453 the sources that it relied on in terms of creating or generating that answer. 92 00:08:05,453 --> 00:08:13,874 And, you know, I wonder if you could talk a little bit more about that because again, we all know that lawyers are skittish about generative AI because of their fear that it's 93 00:08:13,874 --> 00:08:16,942 gonna make stuff up or hallucinate answers. 94 00:08:16,942 --> 00:08:23,628 So how confident can they be of the kinds of answers that they're getting using this AI search? 95 00:08:24,107 --> 00:08:25,198 That's a great question. 96 00:08:25,198 --> 00:08:27,959 So yeah, we all know about hallucinations, right? 97 00:08:27,959 --> 00:08:30,881 Especially when it comes to generative AI. 98 00:08:30,881 --> 00:08:34,985 What I think is really special about Lighthouse AI Search is it's 99 00:08:34,985 --> 00:08:40,027 a RAG model or RAG stands for Retrieval Augmented Generation. 100 00:08:40,148 --> 00:08:47,093 So what that allows you to do is instead of looking for answers out in the internet, right? 101 00:08:47,093 --> 00:08:52,835 It restricts its answers to the corpus of documents you point the system at. 102 00:08:53,036 --> 00:08:58,195 So it won't go outside if it doesn't find the answer within the document population. 103 00:08:58,195 --> 00:09:06,449 that you're querying, it will say there is no information that supports that answer. 104 00:09:06,449 --> 00:09:08,070 there is, I can't find an answer. 105 00:09:08,070 --> 00:09:10,616 It will say use better words than that, obviously. 106 00:09:10,616 --> 00:09:18,700 But yes, it really reduces the likelihood of a hallucination, like rape date. 107 00:09:18,700 --> 00:09:22,759 In fact, I haven't exactly, right. 108 00:09:22,759 --> 00:09:27,400 document that I used or here's the sources and you can go check them yourself basically, right? 109 00:09:27,400 --> 00:09:27,775 Yeah. 110 00:09:27,775 --> 00:09:28,456 Most definitely. 111 00:09:28,456 --> 00:09:34,917 And then if it doesn't, if it can't find an answer, it will say, there is no answer and there will be no documents. 112 00:09:34,917 --> 00:09:35,157 Right. 113 00:09:35,157 --> 00:09:37,994 So it won't, it won't make something up. 114 00:09:38,313 --> 00:09:39,173 Yeah. 115 00:09:39,335 --> 00:09:40,036 Yeah. 116 00:09:40,036 --> 00:09:40,841 Very good thing. 117 00:09:40,841 --> 00:09:41,661 Yeah. 118 00:09:42,121 --> 00:09:46,281 So you talked a little bit about use cases, but I wonder if you could expand on that a little bit. 119 00:09:46,281 --> 00:09:52,981 mean, where are the appropriate use cases for AI search in the eDiscovery process? 120 00:09:53,490 --> 00:10:01,070 Right, yeah, so as I said, you know, the first one that comes to mind is any form of internal investigation, right? 121 00:10:01,070 --> 00:10:03,770 Where you're like, is there a there there? 122 00:10:03,770 --> 00:10:07,990 Right, and you want to point it at a small subset of data to understand your exposure. 123 00:10:08,010 --> 00:10:10,570 The same thing with PCA, right? 124 00:10:10,570 --> 00:10:12,230 Early case assessment, right? 125 00:10:12,230 --> 00:10:22,738 Where you're, you don't need to find all of something, you need to find information that lets you make a strategic decision about what to do next. 126 00:10:22,738 --> 00:10:23,078 Right. 127 00:10:23,078 --> 00:10:24,958 You need information. 128 00:10:24,958 --> 00:10:26,398 You don't need documents. 129 00:10:26,398 --> 00:10:26,538 Right. 130 00:10:26,538 --> 00:10:28,538 That's a quick way of putting it. 131 00:10:28,538 --> 00:10:40,698 Another case is when you're doing, you know, motion prep or you are looking to see what's coming in from incoming production. 132 00:10:41,358 --> 00:10:42,058 Right. 133 00:10:42,058 --> 00:10:51,358 All these things were again where you don't need to find all of something, but rather you need to you need to get grounded in what's in this data. 134 00:10:51,714 --> 00:10:52,976 What do I need to know? 135 00:10:52,976 --> 00:10:56,342 And then you can then make strategic decisions based off of what you learn. 136 00:10:56,342 --> 00:11:02,577 Traditionally, what people often do is do just a full man review of all the data, right? 137 00:11:02,577 --> 00:11:12,937 They might use keywords to help navigate what to look at first, but they do tend to do sort of, if not exhaustive, quasi-exhaustive review of that data. 138 00:11:13,119 --> 00:11:21,415 This is a situation where you can just start interrogating the information right away or the data right away to get at the information you need. 139 00:11:21,415 --> 00:11:29,087 And then if you decide based off of what you learn to do a more fulsome review, you can, but you don't need to start there. 140 00:11:29,087 --> 00:11:32,327 Does it matter the type of data in a sense? 141 00:11:32,327 --> 00:11:43,127 If I've got a collection of documents and I've got say medical records in there or deposition transcripts or that sort of thing, can it be used against those kinds of 142 00:11:43,127 --> 00:11:43,927 documents? 143 00:11:44,796 --> 00:11:45,497 It can, right? 144 00:11:45,497 --> 00:11:49,311 I mean, it's based off of, you know, it's like all AIs, right? 145 00:11:49,311 --> 00:11:51,804 It's based off of large language models. 146 00:11:51,804 --> 00:11:58,262 And so it does require text, but as long as you have text, you can use it. 147 00:11:58,837 --> 00:12:00,129 Yeah. 148 00:12:00,129 --> 00:12:04,032 And what about in the in the litigation context? 149 00:12:04,032 --> 00:12:05,954 mean, kind of post discovery a little bit. 150 00:12:05,954 --> 00:12:06,486 I don't know. 151 00:12:06,486 --> 00:12:11,238 I don't know whether you're you've talked to any of your customers who are kind of using it in this way. 152 00:12:11,238 --> 00:12:21,590 But I'm curious whether you can do things like go in and find out whether I don't know whether something a witness just said might have ever been contradicted in something they 153 00:12:21,590 --> 00:12:24,001 said before that sort of an application. 154 00:12:24,001 --> 00:12:25,421 mean, that's funny. 155 00:12:25,421 --> 00:12:31,601 You should mention that we were just talking about that yesterday amongst my internal team about what are some other use cases. 156 00:12:31,601 --> 00:12:36,281 And that was one of them, which is someone said something. 157 00:12:36,861 --> 00:12:38,721 Can we find contradictory information? 158 00:12:38,721 --> 00:12:39,361 Right. 159 00:12:39,361 --> 00:12:50,201 And that's a great use case because here you have something very specific and then you can say, is there any information in this data that contradicts? 160 00:12:50,201 --> 00:12:52,689 You can literally just say that that contradicts 161 00:12:52,689 --> 00:12:54,771 this and whatever this is, right? 162 00:12:54,771 --> 00:12:56,231 So yeah. 163 00:12:56,231 --> 00:13:07,558 And again, you can just ask it that type of question as opposed to thinking about how do I graph Boolean searches or proximity searches to try to get at this sort of information. 164 00:13:07,558 --> 00:13:11,002 Dan I've been asking you lots of questions but I wanted to give you an opportunity. 165 00:13:11,002 --> 00:13:16,217 there other features you wanted to point out or anything else you wanted to say about this that we haven't talked about? 166 00:13:16,217 --> 00:13:23,545 Yeah, I think buried in sort of what we've been talking about, right, is sort of the value proposition, right? 167 00:13:23,545 --> 00:13:30,580 And I think the value proposition for Lighthouse AI Search is speed to knowledge, right? 168 00:13:30,580 --> 00:13:32,912 That is incredibly valuable. 169 00:13:32,912 --> 00:13:39,430 Again, as I said, I've been working in this space for 20 years, and that is the one thing that case teams are often sort of, 170 00:13:39,430 --> 00:13:42,402 concerned with, which is like, need to know something. 171 00:13:42,402 --> 00:13:45,585 How long is it going to take me to find it out? 172 00:13:45,585 --> 00:13:48,513 This is a tool to really speed that up. 173 00:13:48,513 --> 00:13:50,450 mean, greatly so. 174 00:13:50,450 --> 00:14:02,469 We think about using it all the time to answer basic questions that in the past, when we worked with clients, we would have crafted Boolean searches and then said, OK, within this 175 00:14:02,469 --> 00:14:04,207 set, here's the answer. 176 00:14:04,207 --> 00:14:07,416 we could just now say, here's the answer. 177 00:14:07,416 --> 00:14:15,416 Yeah, seems like, I mean, you mentioned early case assessment, seems like it would be incredibly powerful at that stage of a case when you're still not quite sure what you have 178 00:14:15,416 --> 00:14:19,896 or what you're working with, but where you can dive in and find something really quickly. 179 00:14:20,249 --> 00:14:21,789 Yes, exactly. 180 00:14:21,789 --> 00:14:29,774 You know, we have we have one client who's thinking about using Lighthouse AI Search to do QC over production. 181 00:14:29,774 --> 00:14:31,656 That's going to be an interesting use case. 182 00:14:31,656 --> 00:14:33,577 We'll be helping them with that as well. 183 00:14:33,577 --> 00:14:43,243 We have another client who, again, is thinking about they need to respond to interrogatories again. 184 00:14:43,359 --> 00:14:44,710 great use case. 185 00:14:45,426 --> 00:14:46,546 Right. 186 00:14:46,826 --> 00:14:52,446 And I guess the other thing we didn't really mention, but it probably is obvious to people who listening who work in work in the discovery. 187 00:14:52,646 --> 00:15:03,905 But I'm presuming this can be used as effectively against your own documents and against incoming production from from another side, from their side. 188 00:15:03,905 --> 00:15:08,269 yeah, incoming production is just a primary use case, right? 189 00:15:08,269 --> 00:15:13,002 Because this is a situation where you don't need to review all of them, right? 190 00:15:13,002 --> 00:15:15,483 You have very specific questions. 191 00:15:15,483 --> 00:15:18,877 Or you might actually have a real sort of what's in here, right? 192 00:15:18,877 --> 00:15:21,629 You know what the contours of the matter are. 193 00:15:21,629 --> 00:15:25,112 You can just start interrogating what did they produce to me, right? 194 00:15:25,112 --> 00:15:27,653 Does it have the answers I expected to have? 195 00:15:27,813 --> 00:15:31,473 Are there documents that are expected to be produced? 196 00:15:31,753 --> 00:15:33,273 Are they in here? 197 00:15:33,273 --> 00:15:34,233 Right? 198 00:15:34,373 --> 00:15:38,880 And then you can, again, decide what to do based off of what you find out. 199 00:15:38,880 --> 00:15:42,529 And Lighthouse AI Search, this is now generally available. 200 00:15:42,529 --> 00:15:43,351 Is it out there? 201 00:15:43,351 --> 00:15:46,262 It's out there, but it's not in general availability just yet. 202 00:15:46,262 --> 00:15:49,894 are, yes, it's a great question, right? 203 00:15:49,894 --> 00:15:52,637 We are working with some early access clients. 204 00:15:52,637 --> 00:16:04,956 And then, but we fully expect to, as the summer and fall roll around, we'll really start opening it up to general availability come the end of the year Q4. 205 00:16:04,956 --> 00:16:11,336 Okay, so by the end of the year, he'll be out there and he'll be kind of testing it with customers until then. 206 00:16:11,376 --> 00:16:16,936 Well, sounds really cool and I really appreciate your taking the time to come on the show and talk about it. 207 00:16:16,981 --> 00:16:18,681 Yeah, thank you. 208 00:16:18,681 --> 00:16:19,681 It's been a pleasure. 209 00:16:20,029 --> 00:16:22,130 That's it for today's episode. 210 00:16:22,130 --> 00:16:28,842 If you enjoyed it, please subscribe to this podcast wherever you get your podcasts, or you can also find us on YouTube. 211 00:16:28,842 --> 00:16:31,232 Just search for LawnexPR there. 212 00:16:31,329 --> 00:16:36,234 You can also find it all in the Lawnex Legal Tech directory under the Resources tab. 213 00:16:36,574 --> 00:16:37,705 This is Bob Ambrogi. 214 00:16:37,705 --> 00:16:38,895 Thanks for joining us today.