Winning Cases with Wexler AI: Gregory Mostyn on Why The Future of Litigation Starts Here - S10E25

Speaker A: You're always marketing yourself in one way or another. I think litigators are going to want to prepare like an athlete preparing for the big race. AI that can help you play out different scenarios using the facts, training up, putting someone on the stand, having a sort of sparring partner with a judge or arbitrator. Those are all the things that we're really interested in. The workflow in Wexler would be to build a chronology. And then we even have a verify button where you go through and you verify each of the facts that are in that chronology at the point of finalizing it. That process of verification doesn't just make sure it's accurate, but it also makes sure you've got your fingers on the dock documents. So you're actually reading the documents themselves as they should be read, exactly as they would look if you printed them out, which is really important. It might take three hours to do a chronology in Wexler. It doesn't take three minutes and it doesn't take three days. But those three hours not only help you get the best output, but they also help you become an expert in the case.

Speaker B: On today's Legally Speaking podcast, I'm delighted to be joined by Gregory Mostin. Gregory is the co founder and CEO of Wexler. Wexler offers advanced AI driven solutions for e discovery, document review and litigation services with clients and including the likes of Clifford Chance, Herbert Smith, Three Hills Kramer and Burj Salmon, Wexler's on a mission to solve the most complex cases. So a very big warm welcome to the show, Gregory.

Speaker A: Thanks Rob. Nice to see you.

Speaker B: It's uh, absolutely pleasure to have you on the show and looking forward to diving into all the great stuff you're doing in and around the world of law. Before we do that, I've got a couple of serious questions. What is your favorite beverage and what is your preferred choice of footwear on a typical workday beverage?

Speaker A: Um, I'm a sucker for Diet Coke, I'm afraid. Bit of a bad habit. D.C. as I used to call them. Um, yeah, I think I powered my university with a, with a, a, uh, sort of diet of Diet Coke if you like, revising for finals where I got addicted. So try, I try and limit my Diet Coke intake footwear very much. Trainers, um, I'm not sort of running around in Birkenstocks like some of the tech founders I know, but, uh, trainers, um, but not, not sort of suited and booted every day. Um, unlike one of my colleagues who's a former lawyer who insists on Remaining smart even in the kind of startup environment. So yeah, there we go.

Speaker B: Like it, Bit of chalk and cheese. And yeah, I must agree that I do like the uh, the odd DC as well. All right, before we get in too much of a rabbit hole, let's talk a bit more about you. Would you mind telling our listeners a bit about your background and career journey?

Speaker A: Yeah, so I've had a peripatetic career, if you like. I, um, yeah, I studied at Oxford a while ago now and uh, I, I think the first thing you should know about me is that I'm from a family of lawyers. So my dad was a barrister and then a judge. My brother's a partner at Cleary, my stepmom's a barrister. Uh, I've grown up surrounded by the law, um, barristers, you know, um, every kind of dinner party my parents hosted was talking about this or that case. You know, Sunday lunches, big debates about the latest and greatest in what was going on in the kind of world of law and how that, you know, more broadly impacts the world in general. So that's the kind of personal connection to this in terms of me, myself. So I was very set on working in the music industry when I was in university. I founded a few different events businesses and I thought I was going to be a kind of music mogul and went into that, realized, well, it was actually great. I mean I learned so much and I very quickly rose, rose up in the, in a sort of events company, but realized there's definitely a bit of a ceiling there and wanted to sort of challenge myself a bit more. So moved on to something which was sort of bridge between the tech world and the music world. And my thing was marketing, so I did marketing and sales, but all about kind of selling in a B2B and a B2C way. So my, my thing, I suppose I'm good at commercializing, solving problems and thinking through, um, you know, what makes people tick, both B2B buyers and consumers. And so that's what I was doing in kind of the music world. But then I realized I had this kind of untapped entrepreneurial urge, which was from those days of running those events businesses at university which kind of funded my whole way through without doing catering or tutor or anything like that. And so I joined Entrepreneur first, um, which is an incubator set up by a couple of ex Cambridge grads whose peers were all becoming consultants and dare I say lawyers and uh, bankers. And they wanted people to be more entrepreneurial and think bigger and especially in A UK context, like bring some of that, you know, we're a largely services industry and bring some of that innovation to the UK economy. And so I did that, realized that I had a sort of unfair advantage in, um, in the law and legal. AI was kind of blowing. Well, it was initially blowing up. It was basically just Harvey at that time, but wanted to do something that was more special, specific, more specialized, which was really about facts. And this came from a conversation with my dad, which was about having the facts to your fingertips as a litigator is more important than anything. And as a sitting judge, he was, you know, seeing, um, people standing in front of him, barristers, and they didn't know the facts in their cases really, they didn't know the chronology. And so we set out doing that and you know, maybe my faith was tested at first. Maybe we should be broader, maybe we should be doing everything. But actually I'm very, very glad that we went deep, went specialized, and that's now allowed us to create something really durable and really deep that really can, can grapple with these very messy litigation data sets and can produce work product that's kind of client ready if you like. So that is the, uh, that's the overview, um, uh, and uh, a kind of short backstory to Wechsler and me.

Speaker B: I'm just sat here just nodding. There's so many parallels with myself almost hearing what you say. I came from a legal family. Our listeners will know my late grandfather was my inspiration for getting into law. I, at university was also running lots of events, club night and just had visions of me sort of handing out the flyers or promoting certain events, monster trucks and things like that, which I've been able to bring in recently into the law. And yeah, and then you obviously notice you had that entrepreneurial flair and you've been hugely successful. So, um, love that story. Thanks for, for sharing it. And I love that, you know, I saw very openly about Specific is terrific and being Inc Wide mile deep. And you absolutely lasered that, um, approach from your own entrepreneurial perspective. So as then the co founder and CEO of Wexler, talk us through a little bit more, what it is, how it works, go deeper.

Speaker A: The fact intelligence platform is, is Wexler. So, um, that's a kind of phrase that I coined before. It's kind of like the exercise of establishing the facts in any dispute or investigation. So that's extracting the facts, I. E. Trawling through millions of documents, pulling out, um, the discrete observable happenings if you like. They may Be verified facts. They may be true or not. That's kind of for you to do with the platform, but basically the events mentioned, like Wexler's being founded in 2022 or the date of this podcast, et cetera, um, and then analyzing them against context, that's against the issues that might be from the claim form, the complaint, the pleadings, etc. And then verifying them. So using the system to corroborate that with evidence and then turning that into work products. So chronologies, looking for inconsistencies and discrepancies, using that to draft, you know, documents, answer questions. And so in practice, where does this get used? It gets used right at the genesis of a case. So client self produces 10,000 documents and says, get back to me by Friday with where we stand. And there's four panel firms all in that process, you know, who can get to the facts quickest and best. Or it's for the sort of deep strategic layer, the kind of focused review for trial prep. We have a lot of barristers using the platform plus witness, you know, the whole witness process, deposition prep in the us Summary judgment briefing, and kind of anywhere in between. And it's jurisdictional agnostic because as my dad says, facts have no frontier. And, uh, it's. It, uh, can be used across arbitration, litigation, investigations. So very simply, we kind of distill massive data sets, no matter how messy they are. The different file types, the different formats, the complex fact patterns. We extract the discrete observable happenings from those events, if you like, we assign them relevance based on a list of issues that you supply and then you use that as the unit of analysis to create work product. So. Yeah. Does that explain it?

Speaker B: Very much so. Very much so. And, uh, I love it, you know, when the facts are the facts. Pound the facts, as they say.

Speaker A: Exactly.

Speaker B: For sure. Um, I want to talk about the mission of Wexler, because I strongly believe that it's mission focused companies are the ones that are going to thrive and survive in this bubble. So, you know, I think historically people have just sort of put values and missions and things on walls and they haven't really meant too much, but I believe in modern entrepreneurship they carry more weight than ever. And your mission, I understand on a mission to solve the most complex cases, and you're going deep there, but can you share the meaning, why you went for that specific mission?

Speaker A: I think it was funny, I was talking to my, uh, again, my dad about this, and in the Prince Harry versus the Mirror Group case, they had to Just take a very small selection of the, of the kind of exhibits to rely on at trial because of the overriding objective of like, you know, not wasting court time, etc. And it meant that actually they were only looking at a very slim segment of the total evidence which could be considered to resolve the dispute. And I think that isn't necessarily just, you know, you're not looking, you're not considering all the angles. It could be a critical piece of information. And if you're a client, you're paying for something and you're not actually being represented to the fullest of the, of the potential that you could be, because there could be a critical piece of information that never sees the light of day because there's just too many documents to review. And so I think it's about, um, making sure that we're not limiting justice by um, just how many documents humans have the capacity to read. And usually it's like paralegals reading them in the middle of the night or Busy Associates at 3am So I think it's about making sure that clients and in house teams have the best representation and they can have their interests represented in the sort of fullest sense, not just about the kind of reputation of the firm, um, who's got more capacity to read more documents, etc. And then I think it's about, um, you know, and this is actually a noble claim, you know, like I think ensuring those lawyers get to go home a bit earlier to see their wives and children and not have to do the sort of drudgery and the grunt work, um, that, you know, some people say it's kind of par for the course, but actually we should be thinking bigger than that. So I think it's about, you know, access to justice or maybe just, you know, ensuring that the full corpus of documents related to something can be analyzed and you're not just limited by the number of documents you have the capacity to read and also making the quality of life for those people better.

Speaker B: Two huge points and two things we absolutely support here on the Leaps being podcast is absolutely access to justice and tech for good. I think the pandemic taught a lot of people the importance of, you know, we're only here once work, life, balance genuinely matters and you know, actually using this technology to have those, you know, hours I speak quite openly about, um, you know, my late grandfather, who ran a very successful law firm, remember this time quite vividly asking my mother, um, you know, what your, um, memories of your, your dad, my grandfather. She said, to be honest with you, Rob? I don't have many. He was always in the office. Yeah. And of course, you know, that was. That was the generation back then, in 1950s, running a law firm, you know, being competitive, but actually, if nothing changes, nothing changes. And with innovation, you know, change should be good, and it should have impacts on society for good if we've got good actors doing good things. And that's why we welcome great things like you're doing and other entrepreneurs we have on the show, because I think you can make a not just within the league profession, but lives better. And so that's why I think technology done right is incred. And I touched on before values. You know, I see a lot of values. We speak. You know, we've done 500 of these episodes, but yours really landed with me. I love them because they're a lot of what I'm about. So hopefully I'm getting these right. Hustle with purpose, Fail fast, learn faster. And one team. Yeah, tell us about those.

Speaker A: So I think that someone, uh, asked me, what's the one thing you look for in, um, in new joiners? And I do think it's this hustle with purpose piece. So it's basically, will you go out of the. Out of your lane to solve something, even if it's not in your job description? And will you hustle? But will you do it in a way that's like, you know, disciplined and is not gonna do anything immoral or illegal or. You know, that's obviously an extreme example. But are you doing it with a kind of bigger picture in mind? Because we're all about that at Wexler when we don't want people who are like, oh, I've done. You know, I've been a sort of lead engineer for years, and this is my way of doing things. Like, our engineers, when we were an early stage company, used to come to conferences with me and do the sales pitching, even though they had no. They've never done sales before, because I was the only person doing sales in the company. And that's the kind of example of hustle with purpose kind of embodied. So really hustling, but making sure you're doing it in a deliberate way and not sort of randomly flailing around is super important. Fail fast. This is something I learned as a marketer. So it's a growth mindset. You know, it's better to. To test something and it fail than to not test at all. The silent killer is doing nothing right. That's what one of my sales mentors told me back in the day. And um, again, a few caveats here. We don't want to deliver something that's a poor product experience. You know, we don't want to put something else half baked into the world. But I think it's a bias to action and to testing things rather than sort of strategizing and hypothesizing. And one team, I think, you know, we see a lot about, we hear a lot about 9, 96 and working and all that kind of thing. And of course we work extremely hard, we go out of hours, we do things. But like Kush and I, our micro um founder, Kush that is, you know, we're not straight out of Stanford. We're not 23 years old. Like, we have perspective. We understand that actually our most valuable resources are human capital. And we want our team to be rested so that they can perform at the best of their abilities. And that means having, as you say, like a work life balance and ensuring that you go home at the end of the day to your family and you take, you get a bit of space and then you come back, um, and hit it as hard as you can. And so the way we think about it is an elite sports team rather than a family. You know, we're not, um, we're not like family. We don't spend all of our time outside of work together. But when we come in, we're an elite sports team and we are very high performing and then we sort of have that um, separation. So, so yeah, that's a bit of, uh, elaboration on the values.

Speaker B: I think it's good, isn't it, that you have it that branded as that elite sports team because typically that is the bar and everyone's going to strive to deliver at that bar if they buy into the mission and they see that. And so, you know, you keep that level, um, up there. I think it's really smart. And yeah, I like the way that you, um, described that as well. And ultimately, like you said in the current format and the world that we're living, I think you have to be adaptable. You, a job spec could potentially turn itself on its head. And I mean I run a legal recruiting business and you know, traditional legal roles are going to be transformed and are being transformed. So you know, you're going to have to be a little bit adaptable, particularly if you're an entrepreneurial, tech focused business. So I love that you actually have that really into the core DNA of what you're, you're building and what you're about. Okay. So you've talked to us about Wexler, but I want to get a chance for you to really sort of go deep on this because I think it's a fascinating, um, you know, thing that you're building. So talk us through and any granular extra detail on the sort of workings of it, Sort of those different stages because documents in fact extracted, evidence connected, then the contradictions exposed and a single factual view. There's a lot to digest there. So take it away.

Speaker A: Yeah, so I think it's probably the easiest way to explain this is sort of with reference to an imaginary case. So if you imagine like a multi party fraud claim with um, let's say there's 80,000 documents, information scattered across, uh, board packs, committee minutes, emails between six individuals, late produced text, third party reports, you know, as you'll know, things are privileged, things are in different languages, there's spreadsheets where there's um, you know, data hidden in different sheets. It's like been overwritten by other documents, etc. The claimant would say your client knew. The client says they relied on external advisors. Advisors say they flag concerns the documents. And this is the thing about litigation, like they don't support any single narrative. It's very messy. Litigation data is very messy. And so I think what Wechsler does is we process this sort of process elucidates and rationalizes this mess because we extract the facts from those documents as discrete sourced units which are then deduplicated. So you know, we're not just looking at that mess of documents and asking the AI to sort of work out what's really important. And also it runs out of context. We've had issues, we've heard of other platforms. Like a long document, it just like stops on page 10. And so what happens is we first extract all the relevant facts into those discrete units and then use that as the unit of analysis. And so that's deduplicating. And actually we've then got this massive index starting at the first year, let's say it's 1910 or something, and ending in 2026. And every single fact, and a lot of them are deduplicated and they're also given relevance against the list of issues. And so what this means is it doesn't just tell you sort of, here's a general summary of the documents. It tells you exactly what happened, when it happened, why it mattered and what it means to your case. And that's the kind of best way I can explain it really. Like litigation is very messy and you need something that doesn't just sort of run out of context, sort of summarize. It tells you in very concrete terms, these are the facts that matter. This is why I've extracted from the documents. And that's why.

Speaker B: And I love that, because that key point I always talk about, just generally, you know, everyone's interested in their own favorite radio station. What's in it for me? And that last point about, you know, why it's important to them, I think is so important. So they, you know, get that. Absolutely love it. Okay, we talked a lot about Wechsler as the brand and the organization. Let's talk about you, you know, as a co founder and CEO, uh, what does the role look like day to day at Wexler? What are some of the main responsibilities and things you get up to?

Speaker A: So it's funny, I was speaking to someone, a friend who has just done an mba, and he's like, I want to do a strategy role at a startup. And I was like, there aren't strategy roles at startups. Either you're building or you're selling. Right. And that is the thing. And as a, uh, as a CEO, my main job is selling the product and then obviously feeding back about all of the things that I'm hearing from customers and then taking that to the product team. Hiring is a key part of it, but I actually have a bit of secret weapon there, which I'll tell you about in a second. And then obviously fundraising. So those are, like, the core responsibilities. Right now we're in this massive growth phase. Like, we're growing an insane amount. So my main responsibilities are selling, you know, to new clients, uh, account expansion. So, uh, we have like a really high. We have over 300% NRR because all of our customers want to expand with us all the time. So, uh, a lot of my, you know, day to day is working with customers about planning the expansions, um, and then, yeah, networking and building up the book of business, plus thinking more strategically about bigger picture. What does fact intelligence mean in the wider market? So, yeah, I think if you can't, in a sort of direct sales environment like this, if you can't sell, you're not going to get very far. So, you know, selling is a huge part of my role. Um, but then it's also selling to future employees and then selling to investors. Like, you're always marketing yourself in one way or another.

Speaker B: Today's episode is brought to you by Clio. If you're spending more time managing your practice than practicing law, it's Time for a change. Clio is the intelligent legal work platform built for modern solicitors. Combining context aware AI with trusted legal research to help you work smarter, not harder. Switching is easier than you might think. Clio's dedicated migration teams handle your client and case data, uh, every step of the way so you can focus on what matters Most, your clients plus award winning support 24.5 via live chat, phone and email means help is always on hand when you need it. Visit clio.com forward slashuk to find out why thousands of UK solicitors trust Cleo. Uh, now back to the show. Mark Cuban, famous shark tank inventor, says sales cures all. And I think that's so true. You know, without sales you don't have any business. However hard that hits people, that is a fact of life. It is the lifeblood along with cash flow, which is always helpful as well. In terms of the journey. Again, I'm really curious because, because you've obviously thought very long and hard about the mission, your values, the way you're going about growing, etc. Etc. And when you co founded Wexler, after seeing these firsthand, lots of the inefficiencies of litigation which you've referenced, you know, critical facts buried across all these huge documents, varying documents, et cetera, et cetera, different formats. What are some of the challenges in the legal industry specifically you are seeking to address and you really want people to know. That's where we need to go to

Speaker A: Wexler for the challenges in the legal industry specifically. There's a few things. So one is there's so much more data being created now. There's like email obviously was why e discovery was created because suddenly there's so many more things written down, there's so much more evidence to trawl through. Then we had instant messages, that was like the next sort of step change in terms of data volumes. And now we've got AI which can generate anything basically instantly. A lot of it is sort of slop that's, that's irrelevant or is homogenized or whatever it is. So I think one of the main challenges we're solving is this kind of information overwhelm and trying to find the kind of winning thread when there's just so much data to trawl through. Um, so I think that's, that's a kind of key point. I think another thing is like as the doc review tasks become less important, um, and let's say less valuable because AI is doing them all, you know, it's not going to in the let's play this forward five years time, or maybe that's too far, maybe two years time. You know, it may not be as much about who can find the killer smoking gum because AI will help to do that. Obviously there'll be ways of doing it faster and better, but it's about how you then use that. And so the kind of human side of practice, so mediations, arbitrations, um, you know, trials, depositions, I think lawyers, litigators are going to want to prepare for those like an athlete preparing for the big race. And so AI that can help you play out different scenarios using the facts, training up, you know, putting someone on the stand, having a sort of sparring partner with a judge or arbitrator. Those are all the things that we're really interested in. Um, that's going to become even more important. Like the pressure is sort of countercyclical. It's going to go, it's gonna be even more important to do a good job on the day. And the advocacy part is going to be even more important because it's going to be less about who can find the killer document or the killer fact. It's going to be about how that gets delivered. So I think that's a, that's a key challenge, like an emerging challenge which we're sort of very aware of.

Speaker B: Yeah. And clearly well at the heart of it as well. And thinking forwards for sure, you touched on eDiscovery. It's where I wanted to go next really, because you know, you described earlier as Westler as the fact intelligence platform, which I really like by the way. What does that actually mean in practice though? How does that actually differ from the traditional legal AI, AI or indeed those sort of E discovery tools that are so many out there.

Speaker A: First of all, we complement eDiscovery and we, you know, we integrate with a number of the leading Ediscovery providers. And um, you know, what we don't do as much as is like the sort of first level review the cull, going from millions of documents to 10,000 or 20,000 or even 100,000 documents. What we do is we then turn that into actionable kind of case winning inside insights. So that means extracting the facts, like I've said, assigning them relevance and then using that as the unit of analysis. So the thing that we do differently to eDiscovery is the data model of eDiscovery is rigidly built around documents as the unit of analysis. You know, they say here's these documents are responsive to these search terms. What we do is we start with the fact that is what's actually happened and why does it matter? And then we build up our scale based on that. So kind of a totally different data model and re architecting it, ah, from an E discovery perspective, you know, requires a complete overhaul and a reinvention of that data model. Whereas for us it's about, we've got the quality and now we're kind of increasing the scale. But as I say we, we integrate with ediscovery platforms. Um, and it's a slightly different use case. You know, it's not sort of the paralegal led first level review. It's like the kind of barrister or the associate or the sort of trial attorney who's saying how do we then use that from a smaller universe of documents? But, but why does it matter? And how is that going to help me drive forward my case? And then um, with generalist, you know, legal AI, I think it's really about the um, again it's about uh, that kind of distilling the documents into these facts, no matter how complex and messy the different the data set is. So it's, it could be a, like, I'll give you an example, generous legal AI, it has a bias towards more structured information, tends to be trained on corporate databases. You know, like, it's very pro forma, it's very kind of rote transactional work. Um, you know, the documents are sort of neatly laid out and it's kind of a lot clearer. Uh, you know, there's only so much variance and it's usually like a few sentences that have changed. Whereas litigation data sets could be like a WhatsApp message, medical record, handwritten doctor's note, an image, a sort of scrap of paper or marginalia, whatever it is. And so um, you need to do this pre processing first to get the best insights. And that's what you're looking for in the kind of complex theater of litigation. Like in very concrete terms, the sort of vault and tabular review features. They'll sort of summarize a document as a single row in that platform. And the document could be 10,000 pages long, like it could be a whole trial bundle. And the assistant will also run out of the sort of context to answer those questions. So it's all about that kind of pipeline. We put the documents through to rationalize and elucidate data sets into the facts and then use that as a unit of analysis. And that is what helps you find the kind of critical piece of information, even if it's buried on page 949 of 4062. Right.

Speaker B: Yeah, that is the reality. Right. Some of these things are huge. We talked earlier about, you know, challenges and you're looking forward. Let's just get into the mind of your general innovating, um, approach here because there are lots of changes happening in and around the legal sector. So, you know, with the ever changing technology, you know, looking at the horizontals as well and what some of them might be doing, you know, involving client expectations around billable hours, etc. Etc. How do you adapt as a business to market forces and changes to the legal landscape?

Speaker A: It's interesting, I think. We obviously always, we have like customer advisory boards and we're always like, listening to our customers and trying to understand how we can best position Wexler to, uh, work with them on this kind of journey. There's obviously rightly apprehension, but there's also a lot of excitement. So it's about making sure that people have the, you know, we host these dinners every quarter where we invite kind of both litigators and innovation folks and barristers and we do them in New York as well and we kind of have a roundtable about what this all means and how people are taking practical steps to stay on top of things. So, yeah, it's about being that kind of trusted advisor, training people. We actually do AI workshops for some of our barristers, which are not about Wexler, they're just about AI and something that people don't quite understand, which is like, how can AI be so good at solving a really complex reasoning problem and also not know the number of Rs in a strawberry, in the word strawberry. Ah, people to try and explain how that works. So we position ourselves as this trusted advisor. We're always keen to discuss, you know, how people are thinking about billing and value based pricing and fixed fee arrangements and all those kind of things. Although obviously that's not, you know, it's not really up to us. But we will support how Wexler plays a part in that journey. But I think it's just about being like best in breed in terms of the quality of the output. So you're genuinely useful to the lawyers and then supporting them on the market dynamics and, and making sure they're across all of the big changes that are happening in the world of AI. So, yeah, it's about being that kind of trusted advisor.

Speaker B: I love that. Yeah, and you clearly you are. I want to go back also to when you were talking about your MBA friend now, because you're Talking about different stages and, you know, principally on the funding side of things because, you know, I think, you know, entrepreneurship is the biggest MBA of life you can get. Getting out, living it, being the experience, all of that good stuff. Because nothing hits you between the eyes more than real life. Um, you know, and you've experienced some great growth, you know, significant rounds of funding, you know, backed by, you know, Purbies, Seedcamp, Legal Tech Fund, Myriad Venture Partners, etc. Etc. I think you've raised over $5.3 million in seed funding in 2025 alone. You know, congratulations. What have been some of the biggest lessons for you when scaling Wexler?

Speaker A: It's a good question. So, on the fundraising side, uh, we learned a lot at, uh, ef. Um, they taught us a lot about, about how to run that. I think, um, it's a bit like life, really. Like you need to create competitive dynamics like people. Unfortunately, VCs are also humans in that they get FOMO and they want what they can't have. And so you have to be really disciplined in running the process. So that's been a big learning and something that I've learned a lot. But I think also it's important not to kind of over optimize for a funding round. Like, you need to just focus on building the best business possible and then good things will happen if you're growing fast enough. In terms of scaling after, you know, once it was a bit of a, It's a funny like, mentality shift when you, you go from being like, basically profitable, which we were before, to then venture backed and that kind of mind shift. Like, it's in my nature to be, obviously be growing really fast, but to like, not spend sort of in a profligate way. Um, but then, you know, that's a bit of a mind shift and a mindset shift which, which we've definitely got our heads around now. But I think every founder goes through that thing when we, like, you've suddenly got loads of money in your bank account and now you're like, right now we need to spend. But actually our revenue is growing basically in line with our, with our funding position as well. So it's like we'll probably never spend even half of the money that we've, that we raised. Um, and then I think on a team, from a team perspective, like, we trying to keep as much as possible a kind of flat structure. Um, we don't want to be bogged down with, you know, like multiple levels, you know, tons of internal meetings. I'm sure I think people come to an early stage startup because they have real agency and real ownership, and they don't want to. Want to be sort of stuck in strategy sessions and meetings and those kind of things. They want to be either building or selling, like I said. Um, so, yeah, I think we're kind of at the point now where the next level up is going to have to require a reorganization. But right now we're operating at an extremely high level and kind of bringing the best out of everyone. So we're trying to keep it at that point before we sort of inevitably take the next step up, which is going to involve hiring in the US So we've got like a sub entity in the US and we're hiring in the US and getting boots on the ground at the moment. So I think that's going to be like the next level, very exciting.

Speaker B: And as I always say to entrepreneur, you know, new level, new devil, right? There'll be new exciting challenges, opportunities that come as you keep going through the. The levels of the journey. Um, in a recent Rexler article titled Hallucination the Dirtiest Word in AI, you explore why hallucinations shouldn't stop lawyers actually using AI. So would you mind telling us a bit more about hallucinations? You touched on it previously and how they actually can be mitigated.

Speaker A: I think this article is inspired by a post by Bennett Evans, who's a brilliant, uh, technologist who has a really good perspective on AI. Like, I think it's a healthy amount of skepticism combined with how powerful this technology is. I think his. His, uh, his article was basically that anyone who's completely cracked the accuracy problem with AI is lying to you, but that doesn't mean it's not incredibly useful. So, yeah, I think you need to have both of those things in mind when you're using AI. So Wexler doesn't have, like, fabrications. So what I would say is a hallucination, we like to say internally, is like, if a human given the same information would not make this mistake, like a kind of intelligent human, then I would call that hallucination. Like, it's an unexplainable situation where there's no sort of source, et cetera. And so that Wechsler doesn't have that issue, is it 100% accurate? Of course not. And any AI founder telling you it is is, you know, you need to sell them back the bridge they've just sold you. It's really a question of, like, being able to very quickly independently verify the output, uh, checking it using it, and then using that as you build up your output. In terms of practically how we manage to mitigate that process, um, I think. I know I keep coming back to it, but it does come back to this, like, fact extraction pipeline. So if you're not asking the models to do too much at one go, then you can massively mitigate, you know, uh, context window issues leading to things kind of being invented or fabricated. So we sort of say A to B to B to C to B to C, sorry, to C to D to E to F, et cetera, stitched together, rather than A to Z in one go. Because in doing that process, you're building up this kind of curated, accurate database where it's not sort of having to grasp in the sort of fumble in the dark to find a piece of information which may not exist. Plus, we have a huge amount of guardrails in the back end, which is like, only stick to the, you know, as you would expect. And that's kind of what you can do without that kind of custom architecture. Plus the fact that everything in Wexler is sourced down to, uh, the sentence level and is, you know, easily verifiable within the platform. So the workflow in Wechsler would be to build a chronology. And then we even have a verify button where you go through and you verify each of the facts that are in that chronology at the point of sort of finalizing it. And that process of verification doesn't just make sure it's accurate, but it also makes sure you've got your fingers on the documents. Right? It makes make sure you're actually reading the documents themselves as, as they should be read, you know, exactly as they would look if you printed them out, which is really important. So it might take three hours to do a chronology in Wechsler. It doesn't take three minutes and it doesn't take three days. But those three hours not only help you get the best output, but they also help you become an expert in the case. And so that's why it's important to have that, um, that kind of process. Doesn't mean it's not like, unbelievably helpful in a huge amount of time saving, but it's actually important because you're becoming more up to speed and more familiar with the documents than maybe even if you'd read them cover to cover. Because reading them cover to cover, you get lost in the story of each document. You know, you're not looking at things in terms of the fact patterns.

Speaker B: I love that. Yeah. And you know, trust the process as well. Right. And you know, we, we talked around AI generally, you know, horizontals touched on a little bit. But you know, it's very clear Wechsler is specifically right. Built for disputes, not general use legal AI usage. So why is specialized AI now our future?

Speaker A: There's so much value from generalist legal AI and generalist AI in general. It's unbelievably useful. You use Claude internally. Like the productivity gains you get are just unconscionable from what it would have been even a year ago. You know, it's complete level up. But in the theater of litigation, you're looking for the upper hand against the other side and you're looking for anything that will give you that advantage. And, and not just that. You want something that understands the nuances of disputes. You want, you understand that ah, things aren't always as they seem. Things are obfuscated or unclear or people are speaking in, in code words and you know, investigations, people are like saying very odd speech patterns, those kind of things is where you need something that's bespoke and custom built for that challenge, which is kind of and really deeply understands. Litigation isn't retrofitted and has this way of distilling documents into facts and using that as the unit of analysis. Um, and in litigation, if it gives you the upper hand, it's worth, you know, millions because it could, you could be in a hundred million dollar lawsuit and there's a huge amount at stake. And so that's where, um, we're seeing a lot of pull from the market. People will generally already have a generalist legal AI platform. They may also have Chat, GPT, Enterprise or Claude or whatever it is. And they're using Wexler because it understands litigation. It can do a huge amount. So we can do 250,000 documents of LLM analysis, which is I think more than any other platform I've heard of, like in terms of the deep LLM analysis. And it also will give you that upper hand in the case.

Speaker B: Yeah, no, absolutely. I think you've justified that very well. You've mentioned U.S. expansion. What else does it look like in terms of the future for Wechsler? Five years is too long a time to say in modern world. But looking at the roadmap, know the coming years, what, where do you envisage Wexler going next?

Speaker A: We think we've built, in fact we know we've built something really unique, um, that is people see a significant delta against these platforms. And so we just want to build up that uh, uh, advantage. We want to build the best software for building the best case for litigators, for investigators. We're exploring some other verticals like claims handling and insurance regulatory stuff which we've seen quite a lot of pull from IT Enterprises for doing some of their in house litigation. Um, so yeah, it's just about consolidating, doubling down, increasing the scale to ingest millions of documents in a sort of, in this same pipeline and just building more of these workflows that can help bring out the human side in a dispute. So what I was saying earlier, you know, for prepping for the big day, in the same way an athlete preps for the big, for the big race and kind of searching for that, you know, identifying every critical fact that could help help bring your, your case together. So yeah, doubling down, geographical expansion. Obviously the US is the biggest market. We have a lot of usage in, in APAC as well, Australia and Singapore. There's a huge amount of arbitration and litigation there. And yeah, just sticking to our guns and being a scalpel, not a Swiss army knife and not losing our focus basically.

Speaker B: Yeah, absolutely. And that, that hustle with passion. Right. I, I love it. So um, look, it's been a masterclass. Really enjoyed it. You know, lots of great lessons, lessons, learnings, um, you know, ambition. Right. Throughout the conversation. So thoroughly and thoroughly enjoyed the chat. Just let, before I let you go, finally, what would be your advice to aspiring lawyers who are interested in pursuing a career in law and indeed AI?

Speaker A: I mean, I wasn't a lawyer is the first thing I'd say. But um, you know, we've got our head of commercial strategy who is a litigator at Mayor Brown and he became the sort of firm expert on AI before making the move. I do think, I mean I've heard a few people say this, but I think it's valid. Like if you are entering the sort of world of legal practice and you already are an expert in AI, like we've got an operations associate who's starting a training contract next year and he is an absolute whiz at Claude and all the other models. Now he is going to command a huge amount of respect and gravitas in the firm even with much older partners. And so you can enter the world of work as an expert. You can enter the world of work with authority that you didn't have before. Because before you were, you know, the lowest on the food chain and now you're the highest. I heard another anecdote of someone at a firm um, who built a sort of self coded or vibe coded application which has now been rolled out across the whole firm when he was a trainee. So I think become an expert. You don't have to be like technical to do this anymore. Just become stay incredibly inquisitive and curious. Uh, make sure you have a healthy dose of skepticism always. But make sure you are the authority. So when you walk in on your first day, you can speak from a position of real knowledge and uh, deep, you know, command some actual respect with people who are 20, 30 years your senior. Because you understand this and they don't and this is what's happening in the world, right? That's what I would say.

Speaker B: I couldn't agree more. I love that advice. I talk very openly about the reverse pyramid in terms of the talent perspective. Actually. Historically it's been like that. Obviously the more senior you are. But now, like you say, coming through, if you put that time and energy and you understand these technologies, you're growing up with them, you're going to be a hugely valuable asset to any organization. So love it. Yeah, absolutely brilliant. Um, Gregory, if our listeners, I'm sure they will want to learn more about you or indeed Wechsler, where can they go to find out more? Feel free to share any websites, any social media handles. We'll also share them as episodes. Episode for you too.

Speaker A: Very easy. It's there. Wexler AI, you can request a demo, you can email me directly, you can contact me on LinkedIn, no problem. Gregory Mostin, I'll definitely get back to you. Um, but yeah, we're always happy to have a chat about what's changing in the world of disputes and AI and obviously can set up a demo as well. So yeah, Wexler AI, very easy to remember. Perfect.

Speaker B: Well, thanks so much once again, Gregory, for joining me today. It's been an absolute pleasure having you on the show from all of us on the League of Sweden podcast sponsored by Clio, wishing you of lots, lots of continued success with your entrepreneurial pursuits and more. But for now, over and out. Thank you for listening to this week's episode. If you like the content here, why not check out our, uh, world leading content and collaboration hub, uh, the Legally Speaking Club over on Discord. Go to our website www.legallyspeaking podcast.com as a link to join our community there. Over and out.