When AI Meets Legal feat. Eleanor Lightbody, CEO of Luminance

Speaker A: We've got like a blue sky thinking team where basically like I metaphorically lock them in a room and I go, I don't want, don't get distracted by anything that the company's doing. I want you just to be head down thinking about all the weird and wonderful things that might come and start prototyping them so that one day when the technology's there, we'll be able to move fast on it.

Speaker B: This is NextGen Builders, the show for the growth and product leaders of tomorrow. Now you might trust AI to write your emails or plan your vacation, but would you trust it with your legal contracts? Today we're going behind the scenes at Luminance, where AI meets one of the most risk averse regulation heavy industries out there, legal. We'll talk about building AI that can't afford to get things wrong. Shifting culture in a field steeped in legacy and tradition and what it takes to lead a global AI company from outside Silicon Valley. Joining us today is Eleanor Lightbody, CEO of Luminance. Welcome to the podcast, Eleanor.

Speaker A: Thanks for having me.

Speaker B: Let's start with some background. What is Luminance? What do you guys do?

Speaker A: Yeah, good question. So the best way to think about it is we're trying to build these legal, uh, brains for any company, for organizations. So you can really think of Luminance as building these personal legal agents that can understand you as a user and can understand the work that you do and can start to help you automate augment that. We are very much helping enterprises around the world, whether that's the likes of AMD and DHL or Panasonic and Hitachi automate any, uh, interactions they have with legal contracts. So whether that's creating contracts, whether that's the process of negotiating contracts, whether that's finding key information within their contracts, uh, AI will basically start to understand how you do though on a daily basis and then start to do that on your behalf.

Speaker B: That's exciting. Normally, uh, legal isn't necessarily what most people think of as tech forward or the core industry where AI comes in. Why do you think AI in legal is actually a big opportunity? Or how did you come about this innovation and this inspiration area?

Speaker A: Yeah, I actually would say that I think it's one of the biggest places for AI to start and to see early uh, successes. Because if you think about what a lawyer in any organization of any size is faced with like, well, so it's taking a step back. Every single company has, um, it's really broken down into three things. It's broken down to its people, it's broken down to its process and it's governed by contracts, whether that's with suppliers or whether that's uh, regulations or whatever it might be. There was definitely a lot of paperwork that they have to work through. And so, and then if you think about what someone in a business will be doing, well, they will be receiving contracts on a daily basis, they'll be reading them, they'll be either going to go backwards and forwards to negotiate them, or they will be answering business questions, having to find key information within existing contracts, or they're having to understand what new regulations are, what new compliances are. And all of that at the moment is so time consuming. It's uh, a lot of it's repetitive. You know, a lot of companies have got people just creating NDAs on a, you know, hourly basis. A lot of it's quite expensive. Um, and until recently a lot of it's been done manually. And so artificial intelligence and um, I would say like specialized AI, because actually with legal you need to have something that um, really understands legal contracts and understands, you know, what's in them and doesn't make up answers, um, is prime to help accelerate, um, and to help drive efficiencies within that work.

Speaker B: Well, I mean, I think AI is really good at unstructured data reading and writing text, which would seem to make it really applicable to AI. When do you think about the start of luminance? Ah, was it always grounded in like AI first and applying AI to the field of legal? Or did you guys make your way into like figuring out this, this perfect marriage of the technology and the domain, uh, to unlock value?

Speaker A: Yeah. So, um, unlike a lot of companies, we very much have always been in, I would say we were a machine learning company before AI.

Speaker B: Really the pre AI.

Speaker A: Exactly. The pre AI definition of AI. I mean it's still AI, but, um, we, we were founded by mathematicians who, uh, from the University of Cambridge. And their whole take was they had friends of theirs who were lawyers who were, um, complaining about, you know, similar hours that they were putting into a lot of very repetitive work on contract reviews. They had been working in the machine learning space for the best part of 20 years and between them and they were like, oh, wow, okay. Like you know, supervised and unsupervised machine learning, you know, going back all the way through to like Word to Vec and um, Cayman's clustering can help lawyers understand and sift through data faster. So that was um, their initial idea and obviously that's matured, uh, as the techniques, uh, have got better and have developed. So I think if you look at all the different kind of areas, different methods that we've gone through, it was Word2Vec, then it was like embedding models. And then it was, and then it was like we were using uh, BERT transformer based technology like models before this huge hype of generative models. And now it's very much, we use a combination of lots of different models. Um, and that's really, really key because actually lots of models are good at certain tasks. And it's actually how you orchestrate the models to work with each other. And if you can fine tune them with the data that you've got, you can basically get the models to check each other's own homework. And that will allow for limitations of hallucinations, which, um, is really key when working with lawyers because they need to trust the outputs. But actually what's even more interesting is that you can start to train the models, um, to look at getting probabilistic consensus and answers. And if there isn't probabilistic consensus, then the AI can go back to the human and say, actually I don't know the answer. And that then allows for the lawyers to trust the system more and more. So that is one of the layers that now happens underneath the hood. But actually again, if you're building agents on top for legal, you have to trust the output even more. And so the great thing about it is that if the model output says, I don't know what the answer is here, then the agent will go back to the human and say, I don't know what steps to take next, because we're not quite sure what it is. These are some of the options. And it allows for the human to start to really feel empowered to use it rather than to feel quite resistant to the change.

Speaker B: Fascinating. You've used the word trust a couple times. And I think it's something that we all struggle with as we're deploying AI is do you trust the results? Is there hallucination? Uh, you know, what are the guardrails that you would put in place? Like, tell me a little bit of how you guys are applying trust as part of the legal profession. The models you're putting out there, is that a, you know, the most important thing or are there other challenges to adoption?

Speaker A: Uh, I think that any new software there are always going to be, um, certain specific challenges and certain quite generalized challenges. Actually, I think that generalized challenges for the adoption of technology or kind of new platforms is change management and making sure that a UI is both flexible enough and comprehensive enough and simple enough to uh, encourage that change of behaviors. I was talking to someone the other day and they, it was super interesting. They said actually to change humans behaviors you can't just be like doubly as good or something or the technology can't be doubly as good, it's got to be 10x as good. And that's, yeah, I really was like really stuck with me. And then when with lawyers. Yeah, you know I keep on going but I always talk about trust because like lawyers are risk averse by nature. You know they, they have spent years in their profession making sure that they are giving the best advice to the companies that they're working for. Making sure that they are custodians of the most important things for that business. And so if they're going to use AI, if they're going to um, rely on it and there's huge amounts of benefits that they can get from it, they need to have a level of confidence that the technology is on par with how they think and how they work. And they need to trust that actually the systems aren't always giving you an answer. I think that's really key like the laws preferred systems to say, you know, I just don't know. Um, then for it to give you four different answers to the same questions and then you go, hang on a second like I'm, I'm um, can't really see, you know, whether there's value here or not.

Speaker B: Very interesting. And so if you think about that change that a, ah, lawyer will have to go through or the profession has to go through, you know, how do you know that, you know they are, they are getting the value that they wanted. Uh, do you have know a couple of KPIs that you look for? Is it the number of times they come back or the quality of the results? How do you think through that?

Speaker A: Yeah, I mean anecdotally lawyers are not ones to not tell you whether they're getting value or not. So you get a very, very quick feedback loop cycle there. But uh, beyond that, yeah, you look at the different parts of the platform and you can very much quantify how many hours is it taking for the sales teams to create these contracts waiting for them to come back to legal. What's the repetitive work and how much uh, time is that taking. And great examples for us is that we've taken customers like Hitachi from days of creating contracts to getting them signed to now five minutes. Um, we then have Customers who are Yokoga, uh, another customer when it comes to answering business questions. So things like when are my payment terms coming up? Or I'm a sales rep. I think I've got an auto renewal on this contract. Like, can I bank on that as a part of my kind of commission plan? Or, um, do I need to go and talk to my customer? Because actually there's no renewal and I'm late to having a conversation. Um, they would have taken like 10 days, that seven to 10 working days to answer any business questions. And now it takes them five minutes, um, five to seven minutes. So you can see that huge amount of time savings. Um, but it's not, it's beyond time savings. It's uh. Actually the great thing about, uh, certainly what we do is the best moments I've had with customers is when they pick up the phone to me and they go, your system, your AI has found things that I don't know if I wanted to know about, but I'm very glad that we found them. Or it's found early payment discounts that they'd agreed to with suppliers that they hadn't been getting paid early and hadn't been receiving those discounts. Real monetary value or real risk mitigation, which, um, I think is, is one of the most powerful parts of the technology.

Speaker B: That's amazing. That's amazing. Uh, and so these, these users, you know, they see the, the power, they see the benefits. Are you seeing a lot of fear as well? Like fear of having your job replaced or fear of whatever change?

Speaker A: Yeah, I think that, I mean, I can speak for myself. I'm not sure that I love change and I'm not sure that humans are programmed to love change. So I think that, uh, there's a lot of conversation around what this means, but I'm not sure that that's necessarily specific to the industry that we're working on. This is like, across the board. All of us need to think about adopting technology like AI today. Um, we need to think about being AI native rather than just kind of, uh, AI implementers. Because the change is here whether we like it or not. There's nothing that we can do about it. I think that if I think optimistically there's a huge amount of societal impact and benefits that we're going to see. There's obviously a huge amount of risk that we need to consider, but we, um, shouldn't be paralyzed by fear because we will be left behind. And so we have to think about, right, what are the areas that, um, what are the Low, like, low risk, high reward areas that I actually think that this is going to really help me. And then you can kind of mature from there.

Speaker B: And do you think in that way, in terms of how you guys are building the business and the product or in your go to market or a little bit of both?

Speaker A: Oh, yeah. We talk about AI across the board. So, um, we use it everywhere, as much as we can. Uh, you know, obviously our developers are all using, um, AI to help them speed up the process of coding. We have AI for our sales teams. I mean, I think they're pretty upset now because I get to jump in and listen to their calls when I have time. I haven't done it as much as I'd like to, but I can get summaries of everything that they're saying, you know, that they're saying, um, we, we use obviously our own software to negotiate deals much faster. They come in and eat our own dog food. But I always am encouraging the teams. The question I ask first is, why can't AI do this first? Um, and then let's think about how the human can do it second.

Speaker B: Right. That's great. For me, it's all about velocity. As somebody who builds products and businesses, I think of how do we move fast, how do we move faster than the competition? And speed's an advantage. And anything that gives me that extra ounce of speed, I think is something I want to push and lean into. And that includes my own work. Right. Not just my team's work, but my own work because I can do more.

Speaker A: Yeah. Speed is, uh, you know, obviously, I think it's like, it's always been important, but in this new era that everyone's operating in, it's, I would say, probably one of the most important things. And so. And if you can deliver things faster to your customers, but also if you can get the feedback loop faster, then that is going to set you up for success.

Speaker B: Absolutely. So on this topic of speed, not only are businesses moving fast, but AI is moving extremely fast. It seems like the models are getting exponentially better and cheaper every six weeks or so. How do you stay abreast of the changes that are happening? How are you evolving as. I mean, the whole foundation that you're built on, the bedrock is shifting right under your feet every single day. Um, how do you think about what that means for the business and for the product and for the industry?

Speaker A: Yeah, I suppose for us, um, because we use different models for different tasks. If a model. And we have like a model leaderboard. If a model comes out tomorrow, that's better than us, and then we just rip and replace it. It's great. We're like, okay, cool. Yes, it really is as simple as. Which is great. Um, I have the tech teams to thank for the way that they've put that all together. But, um, I think what we all need to be aware of, uh, is these models are obviously getting cheaper, they're obviously getting better, um, and also their capabilities are expanding. So the things that I focus, um, on and I try and get the team to focus on, which is like, the model can't do that today, but if it can do it tomorrow, would our technology and would our stack be able to cope with it? Would it be able to manage it? And so always kind of thinking about what's three steps ahead. And I mean, no one knows. But, um, and it might be that there's something else that's next from generative models that none of us have ever thought about, but that's really, really key.

Speaker B: Interesting. Uh, and are the teams receptive to that? Are they leaning in and getting excited or.

Speaker A: Yeah, we've got like a blue skies thinking team where basically like, I metaphorically lock them in a room and I go, I don't want. You can't get distracted by anything that the company's doing. I want you just to be head down thinking about all the weird and wonderful things that might come and start prototyping them so that one day when the technology is there, we'll, um, be able to move fast on it.

Speaker B: That's exciting. Uh, and is that a separate team from the core team?

Speaker A: Yeah, yeah.

Speaker B: Embedded across everything.

Speaker A: They then get embedded if there's something that works. Basically they're silent off until something works.

Speaker B: Got it. So they explore and then when there's something that's unlocked, it gets promoted into. Got it. Got it.

Speaker A: Exactly.

Speaker B: Uh, it's always this interesting challenge of how you allocate your resources for evergreen innovation, uh, or blue sky thinking versus the core. The reality is everybody should be innovating. It's not just one team, it's everyone. You know, if you don't really know if the bet's going to pay off, how do you let them explore without the same constraints as everybody else?

Speaker A: Yeah, and so like, you basically you have everything that you want to innovate for today. So, like, all kind of the use cases that, you know, you can do or want to get to or like improvements, but then you have all you do think, I think you need to have space, um, and a team who are not distracted by the noise to think about. Okay. All of these different areas that uh, might be coming.

Speaker B: Yeah, I agree. I'd love to switch gears a little bit and talk about you, Eleanor. There's a lot of interesting things in your background. First off, you're running a legal company or a company in the legal space. But you're not a lawyer by train or by training. You haven't gone to school in that. How did you end up in this domain? Uh, how did you learn the category? How do you adapt to this space? I have a million questions.

Speaker A: I suppose I was working in AI before AI was trendy as well. I, um, worked at a company. My first career out of university was um, first job out of university, I should say was at a company called darktrace. And Darktrace went from. I was first handful of people in the London office. And so it grew from um, it just. I left just before the IPO and it just got bought by Thomas Bravo for over six um, billion. Which um, in the UK is a real success story. So I got there, I had a few roles but I was the first one in the ground and um, in Africa set up the South African office and went to run a global African ah, division, then went to run the Middle east and Africa for a bit and then uh, looked after a global product that looked at securing national critical infrastructure. So I suppose my skill sets were originally distribution and sales. And anyone that knows me is, even though I sadly don't get enough time to sell anymore, I am obsessed with sales and I love it so much and I find it fascinating. And that probably comes from my background because I grew up selling at the age of 10 I think, um, just for my family business. And then the investors of Luminance basically were similar to those at Dartrace. And I got a phone call to say, even though you're not a lawyer, even though you're not a technologist, we feel that you'd be quite um, well suited to joining the founders at ah, Luminance and taking them um, through the next journey, the next skill, ah, step. Um, I know a lot of lawyers, I grew up with lawyers. So um, I know enough to be dangerous. But you learn on the job and actually what we're trying to do and what we are doing. The legal understanding is important, but actually it's the bigger picture. It's every company in every industry, what are the things that they're doing that's repetitive, what could be automated and how do we do that? And as long as you keep that to mind. Um, of course you need to surround yourself with lawyers, need to surround yourself with AI experts. But um, it's actually probably quite good to have that uh, different lens to build a product that can translate across different industries.

Speaker B: Interesting. And do you think that as a leader you've learned, I mean there's a lot of pattern matching that happens there, but have you learned that through selling and doing discovery in deals and like how you see the world or um, like how did you develop the skill?

Speaker A: I suppose you start where you, where you're strong at and you go from there. I think, you know, you can learn on job and I think obviously you pattern match and you know, you, it gets easier because you kind of recognize things that you haven't seen before and, or that you have seen and then you know how to deal with them. But I think you've got to, you've got to want it, you've got to not have like a plan B and you've got to um, you've got to remain focused. You know, you can come in as a leader and like really choose to change everything or choose to change nothing, but you've got to really the key thing that I learned quite quickly was and um, everyone's got an opinion, everyone's got um, things that they want to focus on. But actually what does the data say and what are the things that are really going to move the needle? Because that's the area that you as a leader need to focus on.

Speaker B: That's great. If you um, if you could go back to, you know, your first day, you know, you get promoted to CEO, if you knew then what, you know now, what do you think you would have done differently?

Speaker A: You know what? Nothing. Because I think everything, even all of the stuff I've got wrong and I'm, you know, I'm sure I've got lots wrong. I know that I've got lots wrong and it's presented an incredible learning experience. And so I would hope that I don't redo that, rehash that. And you know, that's not to say that it's been easy or that uh, there are things that I haven't wished we probably would have in hindsight done differently. But I think that up until today they've all provided unbelievably like high level lessons.

Speaker B: So you're still optimizing for learning, whether it's for successes or failures, as long as you're doing as much learning as possible. Yeah.

Speaker A: But from your failures you learn, I think, more than your successes and Those that then lead hopefully into your successes. So, like, some of the hardest conversations, some of the hardest moments that we've been through as a business, you look back on it and you go, yeah, that's now why we're where we are. Or, like, and it forces you to, like, address things head on. And, and so, you know, I'm sure all of your listeners, like, hopefully they know this, but like, if you're going to build a business is that there was no easy way of doing it. If anyone ever tells you that, then, like, I'd love to meet them. But you, you've got to find, like, the lessons in it. You've got to find the fun in it. You've got to surround yourself with people that you want to do it with because otherwise, you know, uh, I actually think it's pointless. You just got to keep on going.

Speaker B: I love it. I mean, there's grit involved in building and every day, every day, not easy, but I think high, high value and high reward because you can see the impact of the work.

Speaker A: Exactly.

Speaker B: Now you're leading a global company, right, and based out of the uk. Um, how do you think, like, leading in a UK company is different from in other places? Does that give you a different mindset, thinking about customers? How do you break through the Silicon Valley noise that might happen in the tech sector over here versus in other markets?

Speaker A: I don't think it changes the way that you approach customers because I think customers need to be at the center of any decision that you make. Um, and you are as successful as your least successful customer. I would say almost, um, customers have to be at the heart. But I don't know, I very much believe in customers and they're kind of at the center of everything that we do. Um, I think being a British company, yeah, I mean, obviously, um, your background, your history will influence who you are, who you are as a leader, what that means. You know, I, um, actually grew up between Portugal and the UK and went to school in France for a bit. So like very, uh, from a young age had like a lot of visibility into, kind of into different cultures. And I think that probably helped massively when leading teams in different areas, which was an appreciation that we are all a bit different. But actually, uh, we all probably have quite similar, um, strands and threads that go through us. And so, uh, how can we channel those in a really productive way to make sure that we're all proud of what we're doing and we want to be part of it and we all are in an environment where we feel very proud of what we're achieving.

Speaker B: That's great. And do you think in how teams are led, um, do you adapt your style or adapt the culture based on where they are?

Speaker A: Um, yeah, I mean, like, you, you, you tweak it and, um, I think that that was kind of one of the things that I learned quite. I, like, had to learn, I suppose, over time, which was, I'll go to the us, I spend a lot of time in the us and I'll say things like, you know, they were like a piece of furniture in the office and people would look at me and be like, what are you talking about? And I'd be like, oh, sorry. That's the saying that you say in the uk. No one really understands it. Um, and you'd be like, you know, green shoes, which obvious UK means, like, there's loads of really exciting, positive things and I will have half a team just look at me as if I'm speaking a totally different language. So, yeah, you definitely have to change your style. You have to change the way that you kind of, um, you articulate things and the way that you communicate things. But, um, fundamentally, again, the themes are the same and you just, um, adapt them depending on, uh, on who you're talking to.

Speaker B: All right. And now furniture in the office, what

Speaker A: do you mean by that exactly? That just means they've been around for a long time. Exactly.

Speaker B: Sometimes you got to change it and bring innovators and new ways of thinking

Speaker A: or they're great and they're just, you know, that absolutely loved sofa that you're never going to get rid of and they are so trusted and like, why would you ever do anything with it?

Speaker B: Yes. Always. Opportunities.

Speaker A: Exactly.

Speaker B: Um, so any advice for women in technology that are trying to aspire to be CEO or aspire to be leaders in global companies?

Speaker A: Do it. I think sometimes just do it. Sometimes, like, we overthink things. Sometimes we are, you know, we don't necessarily. Like, some people, like, don't believe in themselves. They don't have the networks and the, um, the mentorship, uh, that other people might have. But my views are, get your foot in the door, just go join a company, start at the bottom, work your way up. You'll learn so much that way. And it's so funny, about six years ago I was considering doing an MBA and I think MBAs are great still. But one of my mentors, uh, at the time was like, Eleanor, you're working for a scale up that's growing at. And you, like, if you want to get more experience, go do it again. Don't like that. The idea of learning it through a textbook, like, you learn more by living it. And that's kind of my advice to anyone, which is, like, if you're interested in it, just go do like, find yourself, um, somewhere. And, uh, you'll be surprised at what opportunities come thereafter.

Speaker B: Totally. I, uh, had the same advice when I worked at Microsoft. Steve, uh, Ballmer told us, you know, if you want to have the best business experience, don't do an mba, Just come over here and do the job. Because you will actually feel it in practice. You will feel everything you need to know about an MBA with real world experience. It'll be so much more valuable.

Speaker A: Exactly. Maybe more painful, but definitely much more.

Speaker B: Well, I think one of the interesting, uh, you know, good points of, you know, schooling, education, all that is also just the network that you build, the connections you build. That's extremely valuable.

Speaker A: Of course.

Speaker B: Um, and you brought up the word mentorship. Like, how do you approach mentorship? Is that, you know, do you have formal mentors? Do you mentor other people? Um, how does that look like?

Speaker A: Yeah, so I, um, don't have any formal mentors, and I maybe should get as well, but I have a lot of people that I can call on. So I would say I've got, like, informal, and I've got some people who I constantly call on. So maybe they are formal mentors, but just it's not being formalized. And like, I think it's interesting. When I first took this job, however many years ago, I went up to someone who I really respect and was like, you know, can you be my mentor? And he was like, let's have a few conversations first. Um, because you don't know if you want, like, I don't know if I'm right. And that was a really like. And then like, now where I am and he still is one of my mentors. But, um, that was a really interesting thing, which is like, you, you, there will be mentors for certain areas, and you need to actually understand where you want mentorship rather than just having, like a blank person. And I also think, you know, a lot of people go like, oh, I really want mentorships. Or like, you know, they'll come and ask me for some guidance, but actually they'll just sit and they won't have any specific things that they want to, like, talk to me about. And so, um, if you're getting mentors, like, I think the best mentor, like, and mentee relationships are when someone's Coming and being like, okay, these are the things that I'm focused, or these are things that I'm working on. Uh, these are the things that, you know, this is how I'm thinking about this. But have you ever been in a situation where you've dealt with this and, like, how did you approach it, rather than just asking, like, very blank questions? Because I think when you just ask questions, it's very hard for the mentor to. To really give you advice that, like, uh, is, uh, as impactful as actually when there's a given situation that you're working through.

Speaker B: Right. Yeah. That's great advice. I mean, I always think that the mentees, they own the relationship.

Speaker A: Yeah.

Speaker B: They should be coming for something concrete and saying, here's a concrete situation, here's what I'm struggling with. How would you approach it? Or any advice.

Speaker A: Exactly.

Speaker B: Um, and I always find for the mentor, helping the person think through the problem as opposed to solving the problem is, oh, definitely 100 times more helpful. Yeah, for sure. All right, Eleanor, I. I have to ask you my favorite question, which is, we all have this when you're building. We all have this when we're growing our careers. But what is your favorite oh, shit moment?

Speaker A: I've got so many oh, shit moments, it's hard to have one favorite one. I think one that probably sticks out, though, is having to call an investor over the weekend because all of our money was at SVB and say, I'm not sure that we're going to be able to make bankroll, uh, payroll in that time. Luckily, we're fine. And my m. The reply back was, eleanor, were you not ever bullied at school? Do you not know to have your pocket money in four different, like, pockets in case someone comes and takes it? And I was like, I know. I don't know how this has happened. So it was. It was like the best line back to someone in that situation. So, um, that was a real moment. It was absolutely fine. And actually we actually ended up, like, obviously the whole situation resolved itself, but actually we had much more than. And. But there was a period of window. I thought, oh, we're going to be able to kind of get through the next few months. But it was. Yeah, we actually. In hindsight, even if things hadn't worked out, we would have been fine.

Speaker B: That's scary. Were, uh, did. Did the employees also, like, share their concerns and come to you saying, what's going to happen? Do they even realize it?

Speaker A: I don't think that anyone realized it, but as I said, it was Fine. I would just send messaging out on the Monday to be like that. You would have seen that a lot of companies have been affected by it. But we're not, and we're fine. Which was good.

Speaker B: That's good. And now are you in four different banks?

Speaker A: Uh, yeah.

Speaker B: In different countries.

Speaker A: We're good. We were fine. Generally, the weekend was a bit of a. It was almost like, um, I call it like a response, uh, an instance response. Like a practice run for something. Really feels like we were actually totally fine. Uh, even if, like, SBB hadn't sorted itself out, we would have been fine. But, um, you know, we managed to get the money out in time and luckily we were, like, totally ahead of it. But, um, there was just this moment of, like, share, like, oh, what is going on?

Speaker B: I think of all, like, the crisis situations that you might, like, rehearse or prepare for. That's probably not one that is. Was on the list before that.

Speaker A: But, you know, I've, um, learned a lot now.

Speaker B: It is.

Speaker A: Exactly.

Speaker B: Well, that's amazing. Eleanor, thank you so much for joining us. It's been a delight to have you on the podcast. I've learned so much and I'm inspired by everything that you're doing and how you're building. Uh, so thank you so much.

Speaker A: Thank you. Thank you so m much for having me on.

Speaker B: And thank you all for listening to Next Gen Builders and look out for next episode wherever you get your podcasts. And please don't forget to subscribe.