Letting Go Of Old Ways Of Thinking In The Legal Tech Space | With Vishal Sunak

You know, I imagine like this space, you know, there's a lot of old, you know, kind of old practices that are kind of kept in people's mind and the people who you're targeting, like the general counsels, they have an old way of thinking. How do you kind of you know, navigate around that? And can you, you know, as the expert in the space, can you share some specific, you know, challenges and risks when, you know, running the legal tech business? And maybe because other people in this space are considering getting into this industry, what would you suggest to them?

Yeah, sure. Uh, the general, the general counsel is a persona, uh, that, like I said, is, is rooted in A, um, being risk, risk-averse, and B, um, reputational risk to being unsuccessful in an implementation. And so when you're thinking about that, when you're thinking about legal technology, you're thinking about a tool where, first of all, we're a system of record, so security has to be top-notch. Like, you know, we're SOC 2 Type 2 certified. We have an amazing security posture. We actually had that for years and years and years, right, that enabled our growth. security as a way to enable your growth, especially if you're a system of record. You have to think about that, doing all the right things, encryption and all that stuff. Um, but the second thing is, is really, uh, focused on, uh, making your commitment to them, right? Like, if you say that the onboarding is 45 days, then the onboarding is 45 days. And being able to continue to build trust, I mean, it's, it's a fun persona. It's a really, like, I I believe it to be the last department inside of a company that's going through like a technology evolution. Like I think finance was most kind of most recent with ERPs and subscription billing platforms and stuff like that. CLM, though it's not a new space, the way that we've thought about it and approached it post-signature first is new, right? We're not, we're not walking into a customer and they're using some other AI-powered contract analytics platform. I mean, the vast majority of people in our target market have chosen to do nothing, or they don't even know that something better exists. They may have been indoctrinated with the pre-signature tools that are already available to them, and they may have tried or failed or had different perspectives of whether it's the right fit. So there's a little bit of that evangelical sale, a little bit of that market education, which is really cool. Like, honestly, it's really cool. It's not for everyone. Uh, you're gonna have to educate people, especially if you're doing something new.

Yeah, yeah, that makes sense. Um, so we've also heard you on several other podcasts where you mentioned, you know, you talk about leveraging AI as your differentiating factor in organization— organizational success. Can you speak a little bit more about that? And also, what's your thoughts on really, you know, the need for implementing AI into your SaaS program? Because I know maybe many SaaS founders are like always considering that to give them the edge. Um, you know, what's been your experience with that?

Yeah, um, implementing AI, uh, implementing this kind of machine learning, it's, it's got a lot less to do with the technology, although the technology is super important, right? It's more about that, like, there's a problem that can be solved with this technology, right? Technology on its own don't get people to sign order forms. Just the presence of technology doesn't do it, right? Even the presence of technology with AI doesn't do it, right? It has to be rooted in the solution to a problem that only a machine can do because of large, large volumes of data, complicated datasets, variations, patterns. That's really what you have to think about when you're going on a journey. If you're thinking AI could be the right case, the technology is super important. But even more important than that is Is there a problem that can be solved with it? And sometimes it doesn't have to be, right? It doesn't have to be an AI journey. You shouldn't really start an AI company for the sake that you can tell your buddies that you have an AI company. You should use AI in a meaningful way. And by the way, it's not fun, cheap, or fast. It actually takes a lot of specialized talent like data scientists and PhDs. There's kind of like an operationally kind of company commitment you have to make. to take this journey, which also like impacts your burn rate and impacts your cash forecast and impacts your expense structure, impacts your ability to hire people with this skill set. I mean, every company in the world wants NLP scientists, right? Data scientists with PhDs. You may be in a market where it's easy, like on the coasts. You may be out of market somewhere else. Finding that talent, although we're in kind of like a remote workforce, could be easier. It's also hard to do. Right, it's hard to do. And the impact of hiring the wrong person in a, in a data scientist capacity that takes you down a, a, a path where you may not have the knowledge of how to actually get what you need out of it could be a mess also. So don't do it just to say, for the sake of saying that you have AI. Do it because it solves a real problem you believe in and you believe that the AI can solve it. I mean, that's my take on it.

No, no, that makes perfect sense. And if somebody's, you know, looking to kind of go down that path and they're considering you know, the real cost of, you know, what that looks like, you know, uh, can you share a little bit more? What does, what does a team look like? What are some, you know, maybe estimated cost of what they should expect if they go down that route?

Yeah, sure. Um, so because we're a web application and an AI component, we essentially have a web application team and then we have a separate data science team, right? And so you have to commit first to managing basically 2 different types of teams in parallel to get the objectives. Now, deeper inside a data sciences foundation, it's kind of like right brain, left brain type folks, right? You have people who are like PhDs, they're mathematicians, they're physicists, they have deep understanding of the math, the actual core mathematics of how, uh, NLP or data science works. You need to also pair that with someone who can ship production-level code that can be deployed on our infrastructure. And so like, that's a big kind of learning moment that we had, was that we actually need both skill sets in the data science team. Because you may be a great physicist and wanting to work, you know, out of academia and wanting to work at a company, they, they probably don't have the same skill set as someone who ships production-level code and thinks about beautifully executed, testable, fully kind of edge case cornered code that can actually be deployed. So there's kind of that side of it. Then you need a great infrastructure engineer who understands how to run this on a cloud infrastructure and then consider things like costs and stuff like that. So, I mean, our engineering team is, is like, you know, 20-ish people growing every day. And really ultimately is a partnership between me, my CTO, and my VP of Product, and our lead data scientist to kind of mesh together the intersection and the uniformity of all these things we're trying to do at once, right? So, I mean, at 25 people, you can probably assume kind of what it costs to hire talent like this and great talent. It's not a— like I said, it's not a cheap journey, a fun journey, a fast journey. The other thing is with AI is it's academic, and it's academic until it's not, right? You have to tinker with it and experiment with it and try different algorithms and try different variations. And there's that academic part of it where, like, someone managing a schedule can say, well, how long could this take? Well, 2 weeks from now, I'll know if it'll take 17 weeks. But like, I have to wait 2 weeks to tell you whether it'll take 12 more weeks.

Yeah.

And so if it does take— if it does take 12 more weeks, then I may have to tell you every 4 weeks it'll take another 8 weeks, right? And so there's kind of that academic part of it also, where like, because the things that we're creating are unpublished on the internet, we're the ones that have to invent it, and that takes time. And if You can't be pressured by like a schedule in some sense that you can work it on a schedule, but it's not like building a web application where you can estimate like, okay, a feature like this, you know, kind of estimate it. It'll take 2 sprints or it'll take 4 weeks. Right. It's not like that at all. A lot of it is like academic research and then pivots quickly into, you gotta now productionize it and then be able to ship it onto the infrastructure.

Super, super cool. Interesting stuff. I mean, that's a, that's a massive challenge. And I mean, you can pull it off, that's fantastic. So regarding the industry, you also mentioned that, you know, SaaS itself is solving the problem that the industry created. What exactly do you mean by that, and how can maybe other SaaS founders leverage that for growing their own SaaS company and how they're building their businesses?

Yeah, I mean, we all— we're all prioritizing kind of one unifying thing, which is recurring revenue if you're in this type of business model. And so because you're prioritizing that and that helps you increase the value of the company, that helps you raise money, that helps you validate that you're on the right journey, that helps you kind of provide that evidence, um, the, the challenges are linked to each other, right? You want to grow your subscription revenue because, hey, listen, there are companies that are doing a billion ARR, there's companies that are doing $100 billion of ARR. You want to be in that. We're all rated the same way, right? We're all rated the same way. And so the things you'll do to enable that are also opposed to the kind of risks that you have on the contract side, right? And so, um, as you think about when you kind of reach, you know, a scale, eventually if you're the CEO of the company or the CFO of a company not knowing the answer will end up hurting you a lot more than, than, uh, you think, right? And so making sure that as you continue to grow your company, you also think about the debt you're creating in these contracts. Like, do you know them inside and out? Yeah, I, I've negotiated hundreds and hundreds of agreements myself, but also I don't remember, like, you know, 400 agreements ago what I agreed to, like, in 2017, or maybe that paper that we were using, the contract has changed. And so there's that debt that's growing. Understand that you can't, you can't solve it when you have 10 people in the company, and it may not be a problem, but one day it will become a problem. And so to the extent you can get out ahead of it, that's the best path forward.

Love it, love it. Vishal, let's kind of shift gears here going into kind of the rapid-fire questions. You know, tell me, you know, looking back, I don't know how old you are right now, but What's one advice you wish you had known and maybe would tell your, let's say, 25-year-old self?

More than 10 years ago, how about that? More than 10 years ago, 25-year-old self. Yeah, continue to be selfish about learning from experts. I had the joy of learning from so many great experts, be it product experts, sales experts, marketing experts. I honestly wish I could have done it even longer. just riding sidecar with these great minds that grew all these amazing companies I used to work at. I can continue to try to seek opportunities to learn from more experts, especially if you want to take the founder game, because there's a lot of things that I have to solve in the company where like if it was an easier decision, someone who works for me or works for them would have already solved it. And like to the extent where like you have to make a decision, but you don't have a lot of knowledge in whatever area, How do you enable yourself to at least have a great mentor network, or yourself, you have the knowledge of it, right? Founders start from all different perspectives, but continue to learn from masters as much as you can, Vishal, 25-year-old Vishal.

Love it. I guess that's the key, right? At the end of the day, you want to make better decisions, and, you know, having those people, they'll give you that little tip and push that'll set you in a much better direction than if you keep going on what you thought was the right way.

Yeah, yeah, yeah.

Rashad, what are some of the biggest challenges you're currently facing in order to continue to grow Link Squares? And what I mean by that is, or what keeps you up at night?

Not, not so much, you know, keeps me up at night other than my 2-year-old periodically. But I would say the same things that have always kept us up at night, continuing to make customers happy. and continuing to innovate on the product side and continue to push ourselves to truly change the way that people work. And then kind of the equally important is building the best place in Boston for folks to work at, where they seek us out, they join the company, we deliver on all our promises. That's a big thing for me, whether on the customer side or the employee side. Delivering on the promise that we made to someone like you're gonna come and work at Link Squares, and whether you stay for a year, two years, ten years, forty years to the end, you look back on that time and you say this was really amazing. This was really great. I learned so much. I did so much. I had such an impact, right? And so both of those things always keep me up at night, and those are the things that we got the company started on those same principles.