Is it possible to build and scale AI tooling for one of the world’s most risk-averse industries while instilling trust in your users? Today on Next Gen Builders, Francois Ajenstat explores just how to do it with Eleanor Lightbody, CEO of Luminance, the pioneer in Legal-Grade™ AI for enterprise.
Is it possible to build and scale AI tooling for one of the world’s most risk-averse industries while instilling trust in your users? Today on Next Gen Builders, Francois Ajenstat explores just how to do it with Eleanor Lightbody, CEO of Luminance, the pioneer in Legal-Grade™ AI for enterprise.
With 700+ customers in 70+ countries, Eleanor shares how Luminance evolved from early machine learning back in 2015 to where it is today, employing sophisticated AI agents that automate contract review, negotiation, and compliance workflows. Throughout their conversation, Francois and Eleanor highlight different strategies for building user trust. Some are complex: using multiple models at once to check one another’s work and reduce hallucinations. Some are simple: if your models can’t deliver an answer with confidence, let the human know so they can verify.
You’ll also hear Eleanor discuss how her team uses AI across the business, why speed is a competitive advantage, and what it means to lead a global company outside Silicon Valley.
Whether you’re building AI in a high-stakes domain or leading transformation in a legacy industry, this episode offers practical insights on trust, innovation, and scaling with purpose.
—
Guest Bio
Eleanor is CEO of Luminance. Formerly a Director at cyber-AI leader Darktrace, she established and scaled Darktrace’s operations in Africa before serving as Global Head of the Darktrace Industrial Division. Eleanor has a wealth of experience in growing technology businesses, having driven Luminance’s global expansion and led the company’s $75 million Series C funding round in 2025. Named in Management Today’s “35 Women Under 35,” Eleanor is also the winner of Law.com’s prestigious ‘Innovation Trailblazer Award.’ She also serves on Luminance’s Board of Directors.
—
Guest Quote
“I always ask: why can’t AI do this first? Then we figure out what the human should do. The speed advantage is massive. Not just for delivery, but for getting the feedback loop faster, and that is gonna set you up for success. Speed has always been important, but in this new era that everyone’s operating in, I would say it’s probably one of the most important things.” – Eleanor Lightbody
—
Time Stamps
00:00 Episode Start
01:23 What is Luminance?
02:34 Why specialized AI is primed for legal teams
07:20 The largest roadblocks to widespread adoption
09:52 How do you measure success?
11:56 Building confidence across your organization
14:50 Staying ahead of the curve
18:06 Eleanor's journey to Luminance
22:12 Optimize for learning
24:07 Leading globally
26:56 Advice for aspiring CEOs
31:15 Eleanor's "Oh Sh*t Moment"
—
Links
0:00:00.1 Eleanor Lightbody: 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, 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 is there, we'll be able to move fast on it.
0:00:25.6 Francois Ajenstat: 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.
0:01:15.4 Eleanor Lightbody: Thanks for having me.
0:01:16.7 Francois Ajenstat: Let's start with some background. What is Luminance? What do you guys do?
0:01:21.8 Eleanor Lightbody: Yeah, good question. So the best way to think about it is we're trying to build these legal 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 or 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 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, our AI will basically start to understand how you do that on a daily basis and then start to do that on your behalf.
0:02:16.1 Francois Ajenstat: That's exciting. Now, normally, 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 and legal is actually a big opportunity? Or how did you come about this innovation and this inspiration area?
0:02:35.3 Eleanor Lightbody: Yeah, I actually would say that I think it's one of the biggest places for AI to start and to see early successes. Because if you think about what a lawyer in any organization of any size is faced with, well, I suppose, taking a step back, every single company has, it's really broken down into three things. It's broken down into its people, it's broken down into its process, and it's governed by contracts, whether that's with suppliers or whether that's regulations or whatever it might be. There was definitely a lot of paperwork that they have to work through. 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 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. A lot of it's repetitive. A lot of companies have got people just creating NDAs on an hourly basis.
0:03:42.4 Eleanor Lightbody: A lot of it's quite expensive. And until recently, a lot of it's been done manually. And so artificial intelligence, and I would say specialized AI, because actually with legal, you need to have something that really understands legal contracts, understands what's in them, and doesn't make up answers, is prime to help accelerate and to help drive efficiencies within that work.
0:04:08.2 Francois Ajenstat: Well, I mean, I think AI is really good at unstructured data, reading and writing texts, which would seem to make it really applicable to AI. What do you think about the start of Luminance? Was it always grounded in AI first and applying AI to the field of legal? Or did you guys make your way into figuring out this is perfect marriage of the technology and the domain to unlock value?
0:04:37.6 Eleanor Lightbody: Yeah, so unlike a lot of companies, we very much have always been in, I would say we were a machine learning company before AI really.
0:04:46.2 Francois Ajenstat: The Pre-AI.
0:04:47.7 Eleanor Lightbody: Exactly, the pre-AI definition of AI. I mean, it's still AI, but we were founded by mathematicians from the University of Cambridge. And their whole take was they had friends of theirs who were lawyers who were complaining about similar hours that they're 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 between them. And they were like, oh wow, okay, supervised and unsupervised machine learning, going back all the way through to like word2vec and okay, it was clustering, can help lawyers understand and sift through data faster. So that was like their initial idea. And obviously that's matured as the techniques have got better and have developed. So I think if you look at all the different kind of areas that different methods that we've gone through, it was word2vec, then it was like embedding models. And then it was like we were using BERT, transformer-based models before this huge hype of generative models. And now it's very much, we use a combination of lots of different models.
0:06:01.1 Eleanor Lightbody: 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 is really key when working with lawyers because they need to trust the outputs. But actually what's even more interesting is, is that like you can start to train the models 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, I mean, is one of the layers that now happens underneath the hood. But actually, again, if you're building agents on top of like for legal, you have to trust the output even more. And so the great thing about it is that like, if the model output says like, 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.
0:07:08.6 Eleanor Lightbody: Like, 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.
0:07:20.1 Francois Ajenstat: Fascinating. You've used the word trust a couple of times. And I think it's something that we all struggle with as we're deploying AI is, you know, do you trust the results? Is there a hallucination? 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?
0:07:48.9 Eleanor Lightbody: I think that any new software, there were always going to be certain specific challenges and certain quite generalized challenges, actually. I think that, you know, generalized challenges for the adoption of technology or kind of new platforms is that change management and making sure that a UI is both flexible enough and comprehensive enough and simple enough to encourage that change of behaviors. You know, I was talking to someone the other day and it was super interesting. They said, like, actually to change humans' behaviors, you can't just be like doubly as good as 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 with lawyers, yeah, you know, I keep on going back. I always talk about trust because like lawyers are risk averse by nature. You know, 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 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.
0:09:08.4 Eleanor Lightbody: And they need to trust that actually the systems aren't always giving you an answer. I think that's really key. Like the lawyers preferred systems to say, you know, I just don't know. Then for it to give you four different answers to the same questions. And then you go, hang on a second, like, I can't really see, you know, whether there's value here or not.
0:09:29.1 Francois Ajenstat: Very interesting. And so if you think about that change that a lawyer will have to go through, or the profession has to go through, how do you know that they are getting the value that they wanted? Do you have 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?
0:09:53.4 Eleanor Lightbody: 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 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 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. We then have customers who are Yokogawa and other customers 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 in this contract. Can I bank on that as a part of my commission plan? Or do I need to go and talk to my customer because actually there's no renewal and I'm late to having a conversation? They would have taken like 10 days, like seven to 10 working days to answer any business questions.
0:10:55.0 Eleanor Lightbody: And now it takes them five minutes, five to seven minutes. So you can see that huge amount of time savings. But it's beyond time savings. It's actually like the great thing about what we do is the best moments I've had with customers is when they've picked 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 found early payment discounts that they've agreed to with suppliers that they hadn't been getting paid early and hadn't been receiving those discounts. So real monetary value or real risk mitigation, which I think is one of the most powerful parts of the technology.
0:11:40.2 Francois Ajenstat: That's amazing. That's amazing. And so these users, they see 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?
0:11:56.8 Eleanor Lightbody: Yeah, I think that, I mean, I can speak for myself. I'm not sure that I love change. I'm not sure that humans are programmed to love change. So I think that there's a lot of conversation around like 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. And we need to think about being AI native rather than just kind of AI implementers because the changes here, like whether we like it or not, like 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 gonna see. There's obviously some huge amount of risks that we need to consider, but we shouldn't be paralyzed by fear, but because we will be left behind. And so we have to think about, right, what are the areas that... What are the low risk, high reward areas that I actually think that this is gonna really help me? And then you can kind of mature from there.
0:13:03.5 Francois Ajenstat: 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
0:13:11.6 Eleanor Lightbody: Both? Oh, yeah, we talk about AI across the board. So we use it everywhere as much as we can. Obviously our developers are all using 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. We use obviously our own software to negotiate deals much faster that come in and eat our own dog food. But I always am encouraging the teams. The question that I ask first is like, why can't AI do this first? And then let's think about how the human can do it second.
0:14:02.3 Francois Ajenstat: 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 speeds and advantage? And anything that gives me that extra ounce of speed, I think is something I wanna 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.
0:14:27.0 Eleanor Lightbody: Yeah, speed is obviously, I think it's always been important for in this new era that everyone's operating in. It's, I would say, probably one of the most important things. And so it's, if you can deliver things faster to your customers, but also if you can get the feedback loops faster, then that is gonna set you up for success.
0:14:49.5 Francois Ajenstat: Absolutely. So on this topic of speed, not only our business is 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. How do you think about what that means for the business and for the product and for the industry?
0:15:24.6 Eleanor Lightbody: Yeah, I suppose for us, because we use different models for different tasks, if a model, and we have like a model leaderboard, so if a model comes out tomorrow that's better than us, then we just rip and replace it. Like, it's great. We're like, okay, cool. As simple as that. Yes, it really is as simple as that, which is great. I have the tech teams to thank for the way that they've put that all together. But I think what we all need to be aware of is these models are obviously getting cheaper. They're obviously getting better. And also their capabilities are expanding. So the things that I focus on and I try to 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, 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. I mean, no one knows, 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.
0:16:25.5 Francois Ajenstat: Interesting. And are the teams receptive to that? Are they leaning in and getting excited or?
0:16:30.8 Eleanor Lightbody: Yeah, we've got like a Blue Skies thinking team where basically I metaphorically lock them in a room and I go, I don't want, you can't, 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 is there, we'll be able to move fast on it.
0:16:52.5 Francois Ajenstat: That's exciting. And is that a separate team from the core team?
0:16:56.9 Eleanor Lightbody: Yeah, yeah, they're kind of, they're signed off.
0:16:57.3 Francois Ajenstat: Or are they embedded across everything? Okay.
0:16:59.9 Eleanor Lightbody: They then get embedded if there's something that works. But basically they're signed off until something works.
0:17:05.4 Francois Ajenstat: Got it. So they explore and then when there's something that's unlocked, it gets promoted into, got it, got it.
0:17:13.0 Eleanor Lightbody: Exactly.
0:17:14.5 Francois Ajenstat: It's always this interesting challenge of how you allocate your resources for evergreen innovation or blue sky thinking versus the core. The reality is everybody should be innovating. It's not just one team, it's everyone. But 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?
0:17:38.3 Eleanor Lightbody: Yeah, and so you basically, you have everything 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 a space and a team who are not distracted by the noise to think about, okay, all of these different areas that might be coming.
0:18:00.3 Francois Ajenstat: Yeah, I agree. I'd love to switch gears a little bit and talk about you, Eleanor. There's a lot of like interesting things in the background. Like first off, you're running a legal company or a company in the legal space, but you're not a lawyer by training or by training. You haven't gone to school in that. How did you end up in this domain? How did you learn the category? How do you adapt to this space? I have a million questions.
0:18:30.9 Eleanor Lightbody: I suppose I was working in AI before AI was trendy as well. And I worked at a company, my first career out of university was... First job out of university, I should say, was at a company called Darktrace. And Darktrace went from, I was the first, how many people in the London office? And so it grew from, it just, well, I left just before the IPO and it just got bought by Thomas Brava for over six billion, which in the UK is a real success story. So I got, and there I had a few roles, but I was the first one in the ground in Africa, set up a South African office and went to run a global African division, then went to run the Middle East in Africa for a bit, and then looked after a global product that looked at securing national critical infrastructure. So I suppose my skillsets 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, just for my family business.
0:19:41.9 Eleanor Lightbody: And then the investors of Luminance, basically were similar to those at Darktrace 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 well-suited to joining the founders at Luminance's and taking them through the next journey, the next scale step. I know a lot of lawyers, grew up with lawyers, so 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 can be automated? And how do we do that? And as long as you keep that to mind, of course, you need to surround yourself with lawyers, need to surround yourself with AI experts, but it's actually probably quite good to have that different lens to build a product that can translate across different industries.
0:20:48.8 Francois Ajenstat: 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 how you see the world, or how did you develop the skill?
0:21:07.4 Eleanor Lightbody: I suppose you start where you're strong at and you go from there. I think, you know, you learn on the job. And I think, obviously, pattern match and, you know, 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 want it. You've got to not have like a plan B and you've got to you've got to remain focused. You know, you can come in as a leader and 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 everyone's got an opinion. Everyone's got 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.
0:21:59.8 Francois Ajenstat: That's great. 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?
0:22:12.8 Eleanor Lightbody: You know, nothing, because I think everything, even all of the stuff I've got wrong, and I'm sure I've got lots wrong. I know that I've got lots wrong. 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 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 highly lessons.
0:22:46.0 Francois Ajenstat: 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.
0:22:53.2 Eleanor Lightbody: Yeah, 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 might hopefully they know this, but like, if you're going to build a business, isn't it? 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, I actually think it's pointless. You've just got to keep on going.
0:23:36.5 Francois Ajenstat: 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.
0:23:51.0 Eleanor Lightbody: Exactly.
0:23:52.4 Francois Ajenstat: Now you're leading a global company, right? And based out of the UK. How do you think like leading in, you know, in a UK company is different from in other places? Does that give you a different mindset of thinking about customers? How do you break through, you know, the Silicon Valley noise that might happen in the tech sector over here versus in other markets?
0:24:16.8 Eleanor Lightbody: 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. And, you know, you are as successful as your least successful customer, I would say almost. So customers have to be at the heart. But I don't know, I very much believe in customers and know that they're kind of the center of everything that we do. And I think being a British company, yeah, I mean, obviously, your background, your history will influence who you are, who you are as a leader, what that means. You know, I actually grew up between Portugal and the UK and went to school in France for a bit. So like, very, 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, we all probably have quite similar strands and threads that go through us. And so 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.
0:25:27.9 Francois Ajenstat: That's great. And do you think in how teams are led, do you adapt your style or adapt the culture based on where they are?
0:25:37.9 Eleanor Lightbody: Yeah, I mean, like you tweak it. And I think that that was kind of one of the things I learned quite, I 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 will look at me like, what are you talking about? And I'm like, oh, sorry, that's the same that you say in the UK, no one really understands it. And you'll be like, green shoes, which obviously in the 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 you articulate things and the way that you communicate things. But fundamentally, again, the themes are the same. And you just adapt them depending on on who you're talking to.
0:26:28.6 Francois Ajenstat: All right, and now furniture in the office? What do you mean by that?
0:26:31.6 Eleanor Lightbody: Exactly. That just means they've been around for a long time.
0:26:35.8 Francois Ajenstat: Exactly. Sometimes you got to change it and bring innovators and new ways of thinking.
0:26:43.6 Eleanor Lightbody: Or they're great. And they're just you know, that 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?
0:26:52.7 Francois Ajenstat: Yes. Always opportunities.
0:26:56.8 Eleanor Lightbody: Exactly.
0:26:58.2 Francois Ajenstat: So any advice for women in technology that are trying to aspire to be CEO or aspire to be leaders in global companies?
0:27:07.7 Eleanor Lightbody: Do it. I think sometimes just do it. Sometimes we overthink things. Sometimes we don't necessarily, like some people don't believe in themselves. They don't have the networks and the mentorship 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 at the time was like, Eleanor, you're working for an scale-up that's growing it. And if you want to get more experience, go do it again. The idea of learning it through a textbook, you learn more by living it. And that's my advice to anyone, which is if you're interested in it, just go do. Find yourself somewhere. And you'll be surprised at what opportunities come thereafter.
0:28:09.8 Francois Ajenstat: Totally. I had the same advice when I worked at Microsoft. Steve Ballmer told us, 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.
0:28:31.3 Eleanor Lightbody: Exactly. Maybe more painful, but definitely much more. You learn.
0:28:37.8 Francois Ajenstat: Well, I think one of the interesting good points of schooling, education, all that is also just the network that you build, the connections you build. That's extremely valuable.
0:28:48.6 Eleanor Lightbody: Of course.
0:28:49.3 Francois Ajenstat: And you brought up the word mentorship. How do you approach mentorship? Do you have formal mentors? Do you mentor other people? How does that look like?
0:28:59.0 Eleanor Lightbody: Yeah, so I don't have any formal mentors. I know maybe I should get some more, but I have a lot of people that I can call on. So I would say informal. Some people I constantly call on. So maybe they are formal mentors, but it's not been formalized. And 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, can you be my mentor? And he was like, let's have a few conversations first because you don't know if you want. I don't know if I'm right. And that was a really, and then now I am, and he still is one of my mentors, but that was a really interesting thing, which is there will be mentors for certain areas and you need to actually understand where you want mentorship rather than just having a blank person. And I also think a lot of people go like, oh, I really want mentorship. So 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 talk to me about.
0:29:53.1 Eleanor Lightbody: And so if you're getting mentors, I think the best mentor and mentee relationships are when someone's coming and being like, okay, these are the things that I'm focused on. These are the things that I'm working on. These are the things that... This is how I'm thinking about this. But have you ever been in a situation where you've dealt with this and how did you approach it? Rather than just asking very blank questions, because I think when you just ask questions, it's very hard for the mentor to really give you advice that is as impactful as actually when there's a given situation that you're working through.
0:30:31.4 Francois Ajenstat: Right. That's great advice. I mean, I always think that the mentees, they own the relationship. 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?
0:30:46.8 Eleanor Lightbody: Exactly.
0:30:48.5 Francois Ajenstat: And I was trying for the mentor, helping the person think through the problem as opposed to solving the problem is...
0:30:55.9 Eleanor Lightbody: Oh, definitely.
0:30:56.6 Francois Ajenstat: 100 times more helpful. Yeah, for sure. All right, Eleanor, 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?
0:31:15.2 Eleanor Lightbody: I've got so many oh shit moments and it's hard to have one favorite one. I think one that probably sticks out though was 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 payroll in two months' time. Luckily we were fine. And the reply back was, Eleanor, were you not ever bullied at school? Do you not know to have your pocket money in four different 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 like the best line back to someone in that situation. And so that was a real moment. It was absolutely fine. And actually we actually ended up like, obviously the whole situation was solved itself, but actually we had much more than, but there was a period of window where I thought, are we 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.
0:32:16.0 Francois Ajenstat: That's scary. Did the employees also like share their concerns and come to you saying, what's going to happen? Did they even realize it?
0:32:24.8 Eleanor Lightbody: I don't think that anyone realized it, but as I said, it was fine. And we just sent 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.
0:32:38.7 Francois Ajenstat: That's good. And now are you in four different banks?
0:32:42.9 Eleanor Lightbody: Yeah, we are.
0:32:43.3 Francois Ajenstat: In different countries.
0:32:44.8 Eleanor Lightbody: Definitely, but we're good. We were fine. Generally, the weekend was a bit of a, it was almost like, I call it like a response, an instance response, like a practice run for something really like, we were actually totally fine. Even if, I guess, if we hadn't sorted ourselves out, we would have been fine. But we managed to get the money out in time and luckily we were like totally ahead of it. But there was this moment of like sheer like, oh, what is going on?
0:33:11.8 Francois Ajenstat: I think of all like the crisis situations that you might like rehearse or prepare for. That's probably not one that was on the list before that.
0:33:20.2 Eleanor Lightbody: Well, you know, I've learned a lot.
0:33:24.3 Francois Ajenstat: Now it is.
0:33:25.4 Eleanor Lightbody: Exactly.
0:33:26.8 Francois Ajenstat: 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. So thank you so much.
0:33:39.4 Eleanor Lightbody: Thank you. Thank you so much for having me on.
0:33:41.7 Francois Ajenstat: 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.