A (3:06)

Yeah. And like the product is literally a Word plugin, like a Microsoft Word plugin. Like that's essentially the product.

C (3:13)

Right.

A (3:13)

Maybe like distilling this down, like make it a little simpler but so how does it work? Exactly.

B (3:18)

Yeah, so it's, it's a lot like cursor or you know, GitHub. Copilot was our original inspiration and the core of the product sits on top of Microsoft Word, which is where most lawyers are doing their drafting and reviewing work. So mo, the vast majority of contracts all go through Microsoft Word and we sit on top as sort of this like intelligence layer. Now we do have another separate desktop app as well that's a little bit more something like Claude code where it can do these kind of like complex multi document projects. But the original core of the app was kind of based on top of, you know, where lawyers work. And yeah, like our idea of a great product for lawyers is that it should be like an electric bicycle. So, so lawyers know how to ride a bike already. They're already drafting by hand. We want it to be an electric bike. So they're still steering, they're still pedaling, they're in the same environment that they were before. It's not like, you know, they got a cyber truck and now they're, you know, it's like auto self driving them around town or a plane. You know, they're still just driving their bike, but now they can get up over the hills a lot easier. And I think that's like I come from an engineering background and that's what I liked about a lot of the coding tools is that, you know, I'm still very much in the driver's seat, still in control, not, you know, completely doing something completely different than what I was before.

C (4:38)

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A (5:45)

So for somebody who is not a lawyer, and maybe this actually might be helpful for any lawyers listening, but what is an example of something you can do with AI here? Beyond. I guess I'm thinking when you say cursor or GitHub copilot, I'm thinking I'm typing something and then it just fills out the line for me and it starts to write for me. Can you just kind of explain just how the product kind of works? And then I think the Word plugin is kind of an interesting dynamic where there's not very many products that are word plugins that have gotten like, you probably are the biggest Microsoft Word plugin ever. So I'm just interested in just how this actually works.

B (6:21)

That should be our claim to fame. Like biggest, biggest Microsoft Word plugin ever. Yeah.

A (6:27)

Oh man. So then instead of the VC saying don't invest in like chat GPT wrappers, you're like, you're like a word plugin. So I'm interested just like what are the things you do with it? And then like, how do you, how does like a word plugin work as a product?

B (6:40)

Yeah, so yeah, the first thing we had is what you mentioned. So like this sort of autocomplete functionality where you start typing and you know, it continues that. And that was like what GitHub Copilot was. We do a lot more than that today. The biggest thing that we do and the most popular thing is contract reviews. So you can take any contract, say like a lease, a sales agreement, and you can instantly kind of review it for risks and issues and it will learn over time what you tend to flag and what you don't. So it gets better and better. And I think, you know, a misconception people have about, you know, legal AI and contract review is that there's some right answer, but it's actually contract review is completely subjective. You know, it's almost more like a YouTube recommendation algorithm of like, you know, what, what do I think this lawyer is going to care about in this contract? So they can run it against a contract, get sort of a sorted list of things we think changes to the contract that we think they'll care about. You know, maybe they really care about payment terms, maybe they really care about data security and data privacy. We bubble those to the top and then we make suggested edits to the contract. So a lawyer using the product can kind of go through all of these suggestions and accept or reject them, and it will automatically apply that to the contract with track changes on and everything. So it makes it really easy to do these.

A (8:01)

This is like the redline thing. If you ever got in a legal doc, there's always a redline version of it where you people who don't know, it's basically the same document, but there is a second version that has a red line through everything got deleted and then bolded things that were added. And it makes it really easy if you get something back. You can look at like the three changes or whatever.

B (8:20)

Exactly.

A (8:21)

Redline version.

B (8:22)

Yep. Yeah. So yeah, that's how lawyers operate always, you know, with red lines or track changes turned on. So we do that and then we have another version of that. So we've been growing very quickly in the enterprise in house segment. So that's been a huge, huge focus of us for the past year. At the beginning of the last year, we had about almost no revenue from enterprise legal teams like ebay and Dropbox use us. And now it's almost 60% of our revenue. So it's growing very quickly. And what they love is our Playbooks feature. So Playbooks is like a review except, you know, the legal team can set up a set of rules. Maybe they have 30, 40, 20 rules that dictate, you know, how they negotiate contracts, what they allow, what they don't, what they'll bend on. And so if you're reviewing a company reviewing, you know, thousands of contracts a year, thousands of NDAs, thousands of sales agreements, you can run them all through kind of your set of standards in your negotiation playbook, and it will kind of automatically do that negotiation.

A (9:22)

So these are almost like skills and cloud or something like you make or like an artifact you like?

B (9:27)

Yeah.

A (9:27)

Make your Playbook, basically.

B (9:30)

Yeah, yeah.

A (9:31)

So what are some things a lawyer might do? Like what might be in like a pretty standard playbook that someone might have Maybe data residency.

B (9:37)

So if you're, you know, a large company that really cares about, you know, your data security, maybe you mandate, you know, data residency needs to be, you know, in the US or Canada or something like that. So you could flag that on every sales agreement, make sure no one signs an agreement with data residency in another place or something like that. So that's just one example. You know, there's payment terms are really popular, making, you know, auto renewals and all of these sorts of like commercial terms. Limitation of liability and is definitely like another big, big negotiated term that, you know, depending on your negotiating power, you're going to have different stances on what you will allow there and what you won't. Yeah.

A (10:20)

And how do you build a Word plugin? Like, I'm, I'm like super curious just like how that even works. Like, is there like a, like a Microsoft Word?

B (10:27)

You gotta go to Microsoft University.

A (10:29)

Yeah. So how do you actually build a Word plugin?

B (10:31)

It's pretty simple. It's actually just a web page. You know, like what actually is shown in the plugin is basically a web app that has. Connects to kind of the Word API to be able to, you know, do certain things. So it's pretty straightforward.

A (10:43)

Is it pretty simple to build? Like what, like, would I have to like, go back and like, learn something or if I know, like Java TypeScript.

B (10:51)

Yeah, yeah, it's, it's, yeah, based on JavaScript. So. Yeah, yeah, okay. Yeah, a JavaScript TypeScript. So yeah, you can, you can use that. Yeah, it's pretty somewhat straightforward. The hardest part though is dealing with the actual manipulation of the Word document. And this is a file format that's been around since the 90s at least. And there's so many nuances when you actually look under the hood, how these documents are represented, it's incredibly complex. You can have embedded software inside a contract, you can have embedded spreadsheets. There's all sorts of weird hidden features.

A (11:28)

People do that a lot.

B (11:29)

No, but, but, but like every now and then there's like, you know, a lawyer or a law firm who just has this really weird, you know, file that they've kind of been adding onto since like 1997. And you know, they throw it into Spellbook and you know, if some error will come up because there's some, something in it that like we've never seen before. But we've, you know, we've hammered, we've been around since 2022, so we've hammered all those issues out. And then the formatting is really nuanced lawyers really care about is the formatting pristine? Are the sections labeled correctly? And anyone who's done complex formatting in Word knows it can be pretty challenging to deal with. So we've spent a lot of time on. That's the hardest part of integrating with Word.

A (12:12)

Okay. I actually one of my first job out of college, I worked in a bank as a credit analyst, lending money to businesses. And we just write a memo on each company, like, here's what they do, here's their cash flow profile. Can they pay back a loan? We did a collateral analysis, and all these things are pretty simple. But we did have actually embedded spreadsheets. Like, our template memo that you're supposed to use was literally like a spreadsheet, like a cash flow model that was embedded into Word. And we did the same thing with the collateral. And it was just basically to make sure everyone just used the same standard. We're all on the same page. Which is, I always thought was, like, it was super frustrating because if I ever had to do, like, anything that was not standard, which is pretty much every time you have a separate spreadsheet and then you're like, figuring out how to get this thing into the Word memo. And it was weird.

B (13:04)

Deep, deep features hidden in that format. Yeah, it's almost like a programming language of its own. But yeah. And then we do have what we call Spellbook Associate, too. So as I mentioned, we have like a separate surface area that's, you know, a little bit more of like a chatgpt or cloud code kind of shape, but really geared towards working on legal documents in Word. So. So it's. It's like using, like, cursors agent or using Claude code. And yeah, you can take like a term sheet, ask it to draft 10 other docs, or you could even, you know, throw in like a thousand docs for something like a data room review and have it build a table for you, you know, extracting all the data, know, surfacing anything that's concerning and so on.

A (13:49)

Interesting. And I mean, I guess this kind of like, leads into some other stuff I want to talk about. Like, legal AI is probably one of the hotter areas of AI. There's just a lot of momentum around. Seems like it's useful and, like the adoption is there, but I'm just interested, like, as somebody who's in it. So, like, what is kind of going on right now?

B (14:09)

Yeah, yeah, yeah, yeah, yeah. I think, like, if you're outside it can. You might wonder, like. Yeah, is the hype real? Like, it is probably one of the hottest verticals besides like AI for coding. It's maybe the hottest and most talked about vertical for AI right now. And I think there's like a reason why it has taken off so quickly. You know, one analogy I use is that large language models launching in like 2022 or GPT2 were kind of like the spreadsheet moment for lawyers, like accountants. Back in the 80s when spreadsheets were introduced, they started to be able to automate a lot of the work of like running a financial model. Before spreadsheets, it used to take basically a building full of people to run a complex financial model. After spreadsheets and databases, we were able using computers to be able to automate a lot of the basic rudimentary math and formulas of financial models. And finance maybe back in the 70s and 80s was run by an army of people, humans. And now today it's maybe actually 95% automated. If you think about the volume of transactions, how much bookkeeping is semi automated. You know, software like Stripe and all of the tools we have to like automate finance, you know, like we've automated a lot over many decades. That has not happened in law or has not really started to happen until large language models in 2022. So that's like most verticals have seen decades of software adoption and automation, whereas law basically up until 2022 still just ran 100% on an army of humans. Like the biggest advancements we had were like the word processor and email and that made things go a little bit faster. But still the core problem was software could not deal with unstructured text. So it couldn't read unstructured text and it couldn't write unstructured text. We had no. And that's all lawyers do is they deal with, you know, 60 page documents of unstructured text. And we just had no way to ingest it, to understand it.

A (16:27)

So you would just kind of have to just read it and. Or you had 10 years of experience and just know these are the things I care about and I kind of know where to find them. And it might only take me 20 minutes or something, or five minutes. But it's still.

B (16:39)

Yeah, and, but you still have to read like every word. Like if you're reviewing a contract for a client, 60 page contract, like you have to read every word basically. You know, there could be something in there that you're missing. So, you know, I think there's just been radical inefficiency in, in the practice of law and now it's like the dam is breaking. There's all this pent up demand for legal efficiency just like in every other vertical that has been able to adopt software and now it's finally able to be met because large language models are finally allowing us to actually help with the actual work that lawyers do.

A (17:16)

So what was like the software stack of a lawyer, I don't know, five years ago, like pre.

B (17:21)

LLMs, word, outlook,

A (17:26)

do they have like a phone, like maybe Zoom or something?

B (17:30)

Maybe they. I'd say five years ago they're still doing a lot of calls by phone and like the phone bridges.

A (17:36)

So basically they. And they were doing all these. They were producing documents basically and reviewing written. They're reviewing and writing written documents with basically Word and email and then talking on the phone to communicate of what would be changed in the document essentially.

B (17:53)

Yep. I mean that was essentially the lawyer stack.

A (17:56)

Okay.

B (17:57)

It's some like deeply specialized software for things like entity management. Like if you have a complex entity structure with, you know, parent and children. Org, she might have like a chart of that.

A (18:06)

Yeah, like a design to like a design new. Org structure or something. Yeah.

B (18:09)

But you know, beyond that there's, especially for commercial lawyers, which is where we're focused. There really hasn't e. Signing. Oh, DocuSign. Forgot about that. Docusign.

A (18:18)

Oh, that's fair.

B (18:19)

Okay.

A (18:21)

And I think, I don't know if you mentioned it, you might have talked about it earlier when we got lunch, but there's about 30 trillion in sort of contracts that are signed per year.

B (18:31)

Yeah, but 30 trillion moves through contracts every year.

A (18:35)

Okay. This is like economic value that is under a contractual agreement of some kind of.

B (18:39)

Yeah, that's right. Yeah. So there's just this massive money flow moving through these contracts. And what inspired us to start the company is if you think about how inefficiently it's happening, these contracts are probably taking 10 times longer than they should to be drafted and reviewed. And then things are still being missed because I mean, just imagine just being a human reviewing a hundred page contract and it's like 8pm you have a deadline. It's an almost hilariously impossible task to actually review 100 pages with a fine tooth comb on a tight timeline.

A (19:21)

So did they not do it or did they do like. Well, how did they do this? Back before AI existed, I mean, they

B (19:28)

would try their best. Yeah, yeah. Lawyers would try their best and contract managers would try their best. I mean one approach is standardization. Like before AI came around, I think the hope was things would standardize more and more. So you could kind of see, okay, here's the standard SaaS agreement. Like YC has SaaS agreement, that's pretty common that most startups use. And you can kind of do a diff between like, oh, what is different about this agreement compared to the YC standard agreement? And so that was one method, like shortcut you could use if there was a standard template. And like with venture capital financing transactions, you know, there's a standard set of templates. You can easily see what's changed. So that was one approach that would make these reviews easier, especially with complex transactions. But I think most of law has been like surprisingly resistant to standardization because the deals people want to make are all kind of unique and bespoke. Yeah, that actually connects to where we started with Spellbook. So Spellbook was the second product we launched, or one of the kind of the last product we launched in 2022. But we initially thought we were going to drive standardization with templates and that was kind of where we had started.

A (20:42)

Yeah, I definitely wanted to ask you about that, but I think maybe while we're still talking about just general kind of like AI, non spellbook specific stuff, like, I don't know, when I think of a couple months ago, made a contract, I used to go to like ChatGPT and just say, like, make me a contract, like make no mistakes, et cetera. Like, couldn't lawyers kind of do that? Like, is there like some sort of thing where sort of the generic AI products trip up on legal stuff?

B (21:08)

Yeah, good question. So I mean, the first thing I would say is lawyers don't actually draft anything from scratch for the most part, like especially contracts, because they want to start with a trusted precedent that they understand inside out. Because if they go to chatgpt and chatgpt outputs a whole contract, then they have to review every single word of that and make sure they understand it completely. That's very difficult. So ChatGPT is not great at modifying existing work or building on your existing library. So we have a few features in Spellbook. Like one, you can start with the precedents that you're familiar with and we'll kind of modify those. You can start with a sales agreement and be like, make this GDPR compliant and it will kind of surgically make those edits for you. We also have a feature called Library where you can kind of have your whole history of all the deals you've ever worked on and use that to kind of influence the output of Spellbook as well. So these are one of the things I would Say is working off of your existing corpus of docs as a lawyer is really, really important and ChatGPT doesn't do that super well. But two, I think lawyers want things built into their existing workflow. I think like the chat interface is great, but it's still like the terminal UI of AI. Like I don't think chat is the be all and end all and we just have a lot of unique user experiences that would just never fit inside the shape of ChatGPT. For instance, one thing you can do in Spellbook is compare it to the market. So if you're say signing a commercial lease in Manhattan, you can say, compare this to the average commercial lease in Manhattan and tell me what's not normal. And then you can actually dig into the data, into the charts and actually look at the data that we've collected in real time from millions of contracts and explore that through this visual interface that has nothing to do with chat. It's very, very distanced from that. So I think, you know, there's a huge number of experiences that people want that, you know, don't fit in a chat box.

A (23:21)

Yeah. And so this is all within the Word interface.

B (23:25)

A lot of this is in the Word interface. Some of it is in our Spellbook Associate product as well. Yeah.

A (23:30)

Okay. And the, this like market data, it's basically you take everything that's run on Spellbook of every customer and it's like anonymized and you can see what dates of comps or something like that. Or it's like some kind of database of data where you can compare it to the contract.

B (23:49)

So it's like an opt in model and most of our customers have opted in. And the way it works is yeah, we take anonymous aggregate statistics so we only capture things like what's the average, I don't know, price per square foot in a commercial lease in Manhattan? What's the average late payment interest rate for SaaS agreements? So the only thing we end up capturing is these very high level statistical pieces of data and that's what gets exposed. So it allows it to be really privacy friendly. And yet it's an alternative to the approach of fine tuning. This idea of fine tuning was really hyped for a while and every, I think every founder wanted to like sell VCs on fine tuning because it sounds very complex and defensible and like you're going to have this great moat, but it actually works pretty terribly for like a whole bunch of reasons. And I can talk about that if you want.

A (24:49)

Yeah, I think It'd be interesting just because, because that was kind of like the meme or like the meta was. If you are an AI company, you must build your own model because there's no defensibility. And you, you know, you probably need to buy a bunch of GPUs and you need to train them all. And you know, it's like if you're just a chatgpt rapper, like it was like a, it was like this derogatory like slur basically to like call someone a chatgpt rapper.

B (25:14)

Yeah. So I think, I think that like was very like wrong. Like I think, I think this is an idea where like there are narratives that founders learn, investors are hungry for and then they pitch them because it's very legible and easy to understand for an investor. Like this idea of training models, you're like, oh, it's going to be like OpenAI. OpenAI trained their model and it was expensive and cash was like a moat, basically became a moat. But that did not really pan out in really many other areas for a bunch of reasons. You saw Bloomberg made Bloomberg GPT, that was one of the early ones and they spent I think millions training it and then GPT4 came out and just completely beat it at finance tasks. So it was a waste. Similar things have happened in legal AI where a number of companies have tried to train their own legal specific models and fine tune them. I do not know of a single one that is still in use today at any of the major application providers. So it ended up being this kind of big waste of time. I think the much better approach is to build value around the models and I think there's a lot of really great ways to do that. I think RAG is actually really, really good and actually superior to fine tuning. Are you familiar with RAG retrieval, augmented generation?

A (26:32)

Yes. It's basically when you take the model plus just the Internet or external sources essentially.

B (26:37)

Yeah, exactly. So it's like the way I think of it is fine tuning and training is kind of like injecting things into the long term memory of a model

A (26:45)

or

B (26:48)

almost putting it into the evolutionary fiber of the model, giving it evolutionary instincts as well. But if you're asking a model to cite case law for a litigation case, you don't want it looking at its long term memory or its evolutionary instincts. You want it to actually look up the information and make it a hard citation that you can actually cite. And it's a much actually less hallucination prone method of getting legal specific data to work in these systems. So like One, you know, relying on rag, which we did from, from the early days, like yeah, you actually get citations that you can trust and inspect. Whereas if you're fine tuning models, you're still going to hallucinate and you have no way to inspect the data. Two, when you use rag, you can filter. So like we can filter, you know, if you're a lawyer in London UK and yeah you work for healthcare company, you know, you can actually filter down the data to say, you know, I want to compare my contract to only other, you know, healthcare related contracts in the, in the uk so you can filter the sources down. Whereas with when you train a model you end up with this kind of one size fits all model. And like a lot of lawyers will complain, well, you know, ChatGPT is too biased towards the US or it's too biased towards like public company contracts because those are the only ones that are available to train on. Which gets to my third point is like no one wants to ingest private legal data into these proprietary models because there's always a chance it could be spit out again. So by using our approach with the statistics, people are actually comfortable putting private, allowing us to ingest private data because it's fully anonymized, whereas people are not comfortable with training models on their private legal data because there's the chance that it could be spit back out again. And that's not something they can accept. For those three reasons I think RAG and what we're doing with this kind of real time data with market comparison and Spellbook is just much, much more superior, are very superior to fine tuning for the most part. I think it was this case where the herd just ran in completely the wrong direction. And I tweet about this all the time, things that are hyper legible. The story just sounded right. Data is the new oil and fine tuning. These models cache as a moat. It sounded right, but the reality I think is just much more nuanced and complex. And now we see a lot of these companies from 2022, 2023 who went did that fine tuning approach. A lot of them are shutting down and they're probably every two to three weeks I'll hear from one of these companies, they're now looking to get acquired because the market's matured so fast. They spent a lot of time kind of doing this deeper R and D that they actually wasn't that effective. And that's like very core to our culture at Spellbook is like I wrote a blog post about this back in 2022 when we launched and it's called is GPT3 too easy? So we use the foundation models, and back when we launched, people would say, well, is this too easy? Where's your team of machine learning engineers? Shouldn't you be training your own models? And I cited this book. Have you ever read it or seen it? It's called Playing to Win. It's by David Serlin. He's a professional Street Fighter player. Have you seen this before?

A (30:25)

I have read your post, but I've not seen the book.

B (30:27)

Okay. It's an amazing book where this guy professionally played Street Fighter at the highest level, and he talks about what is different about the mindset of a professional player versus what he calls a scrub, or kind of an intermediate or a bad player of the game. And he said the scrub basically loses the game before it even starts because they have this totally wrong mindset. And I'm paraphrasing, but they basically have this romantic vision of the game that if they do play the game this super proper way, this romantic version of the game, that they'll come out on top in the long run. Whereas the pros basically relentlessly exploit whatever they can to win. Even if it looks cheap, even if it's easy. They don't do things because they look hard. Like, if they can, you know, if you've ever played Street Street Fighter, have you ever, like, called someone cheap that you were playing against in one of these games?

A (31:25)

I haven't really played Street Fighter, like, competitively, but it reminds me, if you ever played, like, Halo, Halo 2, you could do this thing called double shotting where you could basically take a shot, but you would shoot two bullets.

B (31:36)

Okay, I never learned that trick.

A (31:38)

So all the. All the best pro players in Halo 2 is like, you could literally do twice the damage with one shot. So everyone got good at double shotting. And if you were like a purist and you're like, I'm not doing that. You can't beat people who do it.

B (31:52)

Exactly, exactly. Yeah. And so I think there's this thing in AI where it's like people almost, I think engineers in particular, who love complexity almost can't accept how simple these systems can be to add value to customers. And there's this attraction to complexity, fine tuning R and D. I think it's started, it's starting to die out now finally. And people are realizing, okay, building on top of foundation models is probably the best approach a lot of the time. But. But, yeah, I think. I think, like, the herd ran. A lot of the herd ran in like the wrong direction. And it's, it's pretty fascinating.

A (32:28)

Do you think part of it is this dynamic of like, what if OpenAI builds this? Or what if Anthropic builds this? Because if you're not building your own model that has any kind of differentiation, like they could just like, you know, tweak chatgpt to like work better for lawyers or something like that. Like, is that, is that a part of this? And how do you then navigate that as a founder of like building a product that's not in the strike zone?

B (32:53)

Sure, yeah, yeah. I mean, I do think that that is a reason why this kind of narrative took off, but I think like the pendulum swung too far in the direction of differentiation rather than customer value. So you had so many companies and investors focused so much on how do we differentiate and doing really complex and hard things that weren't very useful, you know, and seriously, like so many of these companies are like shutting down and selling, selling off now. But so like that's, that's the reason it happened. But, but how, yeah, how do you pragmatically deal with it? Yeah, I mean, that threat is there. I think the vertical AI providers have to work very hard every day to continue to add unique value to these customers. And I tell our team we have to be two years ahead at all times in terms of delivering state of the art experiences to lawyers. Two years ahead. We should be shipping things today that other competitors or other companies will be shipping in two years. I think you have to have this ruthlessly fast culture, continuously adding unique value. I think the way you add the value is 1, through the data. So we have real time data from millions of contracts that we can use to deliver better results to our customers. We also have preference data, so we learn from each of our customers what they care about. And ChatGPT and Claude are not really doing that. For contracts specifically, the data is super important, but then it's, and the features are important, but then it's like, it's like, how do you fit into the workflow of your customers? You know, lawyers are so busy, they have so much going on. You know, if you don't fit super neatly into their workflow, they're just not going to use it. And the reality is like, you know, Claude's not in Word. It's not, you know, it's not designed out of the gate to give a lawyer value. And there's a million little friction points because of that. And I think if you, we always say, you know, our goal is to build A toaster. A toaster product. Like, we are really good at doing one thing, you know, toasting contracts, I guess,

A (35:08)

with a $30 trillion size. Market size or whatever.

B (35:12)

Exactly, yeah. You know, if we, if we can just do that one thing well, and if you optimize your product for that purpose, you know, you just make so, so, so many decisions differently that would never make sense for ChatGPT to make. Like, there's so many little nuanced decisions that make, you know, that toasting experience, like, very easy, simple, and effective for our customers.

A (35:38)

And then there's. Because I feel like there's sort of like the seven powers of, like, just how a business has competitive advantage, and I feel like we've maybe almost forgot about them, sort of. But when you were describing sort of some of the different features, when I think of, like, network effects as a pretty powerful business, and it's just the more customers you have, the more market data you have, the more useful that feature becomes. There's maybe a point where, like, you have so much data that, like, one additional point doesn't matter, but like, to get to that point, like, there are. There are some strength to that, like some, some positioning strength.

B (36:12)

And then, I mean, it scales more than you would think because it's like, oh, what does it matter Whether you have 1 million data points or 2 million data points? Well, it's. Do you have data in Manhattan? You know, do you have data in London? Do you have data in sf? Do you have data in the healthcare industry? Do you have data in the aviation industry? Do you have data in manufacturing? Do you have data in energy? When you think of it that way, you know, these are all industries that you have to kind of conquer to deliver the best product to all of the lawyers in those industries. So, you know, it might sound like, well, what's the diff? Yeah, again, what's the difference between having a million Data points and 2 million data points? Well, it's less about that. It's like, how many industries are you in? How much geography are you in? And do you have a statistically significant sample where you can provide useful insights? And, yeah, I think it is legitimately a really, you know, great kind of data network effect that we have.

A (37:08)

Yeah. And I feel like that's like, we almost like, forgot some of these rules for a while and just like, you know, I feel like almost like economies of scale sort of like took over in the sense of, like, you know, the ability to train the models and like, having the capital on the balance sheet that you could like utilize, which like all this stuff is important, but the other stuff still matters too, I guess.

B (37:31)

Yes. Yeah, I think so.

A (37:33)

So one thing you mentioned that we kind of, we kind of like jumped past it, but I wanted to talk about, you mentioned this difference between kind of like top down and bottoms up, kind of like sales cycles in legal AI. Can you just talk a little bit more about that and kind of how that's sort of played out in the industry?

B (37:51)

Yeah. So I think there's sort of been a divergence of products built in legal AI. There's products like Harvey and Lagora, which great companies. We don't actually encounter them that much because we're so specialized and we service kind of a different customer base. But they have sort of had to optimize to sell to the innovation teams at the AMLA 100 firms and back with a previous product. We've done that kind of sales cycle before and it's very different because these innovation teams are kind of going to push top down across a very large firm and mandate usage. And they're usually going to bring you this long list of 50 things that they need in order to move ahead. And it's kind of like a decision by committee sort of thing. And we had an early experience before we hit PMF with spellbook of working with these committees and they send you in very strange directions and ones that I think are maybe not best for the product. So, you know, for example, at a lot of these large law firms, they operate on an hourly billing model and just decreasing all their billable hours is not a positive incentive. Like the incentive structure is really misaligned with AI.

A (39:14)

So you like don't want them to get more work done?

B (39:17)

Almost. Yeah, I mean it's, you know, lawyers make a lot of money, especially in these large firms, from billable hours. And so what you found was a lot of the time what the committee's most concerned about is how do we advertise this to our clients? How do we do a press release to show that we're innovative? How do we just constantly shove into our client's face that, you know, we're an innovative law firm so that we don't look bad, you know, compared to the firm across the street? And we noticed that that was happening and communities would ask for things like client portals. Well, we want our clients to be able to log in and see the innovation firsthand.

A (39:56)

What is that? What is a client portal?

B (39:58)

It's a place where a client of a law firm can log in and interact with the lawyer or do their work there. And we actually built one of these in an earlier product we had called Rally, because we also had this request and clients hated it. They were like, why can't I talk to my lawyer in email? Like, I don't want another login to another website.

A (40:21)

So is this thing like tracking what each piece of work that's done and like automatically puts it in? I can log in and see what you did or something like that?

B (40:30)

Yeah, it's like you can log in and like see the documents together or like collaborate on the documents together, but. And then the lawyers didn't really like it when we launched it because they didn't want to decline to see all their messy, how the sausage was made kind of stuff. So this feature, I've seen this feature request a ton, a lot from the really large firms like the MLAW, 100 firms that want to show the innovation to clients. But I've mostly just seen it end up failing. And so with that experience, we decided to take a really different approach where we're going to sell bottoms up, bottom up to the actual end users, the lawyers and the vast majority of our customers. We don't even do a sales call or a demo call. It's like, welcome aboard, Turner. You are going to use Spellbook today and in five minutes you're going to be set up and actually using Spellbook for some real work or demo work and clicking around and getting value from it. And so that evolutionary pressure has enabled us to, I think, build a very different product that is much more like, you know, cursor, like in how it's baked into, you know, the user's existing workflow. You know, it's not this grand design thing that you're rolling out across a massive firm. It's like a really practical tool that is always within arm's reach. That's kind of like a win at the back of the lawyer. And because of that we have like amazing retention metrics. Like our net revenue retention of like 130% plus. Like we're, you know, we're kind of doing more of a land and expand motion rather than top down. But we have a lot of customers and they're, you know, they're growing their usage, expanding their seats. And yeah, we really like that way of building a product because it subjects you to kind of different influences, the influences of the actual end user who's going to be using this thing to get work done.

A (42:26)

So there is quite a few different kind of like legal AI software products. That have gotten like a lot of like a revenue like there people are, people are using it, whatever. Like what is kind of like the market leaders in some of these different like legal AI categories kind of look like. Because I think we talked about two Harvey and Lagoa, but I think there's like a lot more. I don't know, is there like an easy way to kind of like educate people for a couple minutes on like what's kind of working in all these different like subcategories of legal?

B (42:55)

So yeah, Harvey and Lagora, fairly similar, started with the law firms and like the Amla 100 type types of customers like what I've been kind of talking

A (43:04)

about and what is their product like what do you use when you're using.

B (43:08)

It's a very broad platform that's broadly kind of like ChatGPT for law. They have a number of different things they do, but it's quite broad because they're rolling it out to a whole legal team that might include litigation teams and transactional teams and so on. So it's kind of like if you imagine tuning ChatGPT or Claude for a

A (43:35)

legal use case, is it kind of like a whole sort of operating system

B (43:39)

to run your law firm on the work? Yeah, I think that's more what they're building. It's like this all encompassing kind of operating system kind of thing for a firm, but very tuned towards the law firms. Whereas we've had really amazing product market fit with the in house legal teams who don't care about the billable hour. So this is the other type of customers is the in house legal team and they're starting to sell to that customer base too. But it's very different because they don't care about the billable hour. They don't care about showing the clients, their clients the legal innovation they're doing. They really just want tools that they can switch on and deal with. This hair on fire problem of I have too many contracts to deal with, I need to clear my queue, I need some way out. And so that's kind of where we've really been shining is in that segment. In terms of other companies it's probably

A (44:33)

like personal litigation type of stuff.

B (44:35)

Oh yeah, there's litigation, there's even ups done really well.

A (44:41)

For instance, that's a personal litigation.

B (44:44)

Personal injury.

A (44:44)

A personal injury. Okay, there's that one. Even up people that know Even up listing us will they can write in the comments what Even up does. I've definitely heard of that one before.

B (44:53)

Yeah, even up yeah. Is kind of AI for personal injury cases.

A (44:57)

Okay. And then there's. There's like other. There's sort of like corporate advisory type stuff. Like, isn't there a company called. It's called Hebia.

B (45:05)

Oh, yeah, yeah, yeah, yeah. Hebia was pretty broad at first, but from what I see, they've. They're really trying to take the position of being for finance now, so.

A (45:15)

Oh, interesting.

B (45:16)

Yeah, yeah. They've gone kind of very deep down that angle. I don't know if they're still running their kind of like legal arm anymore. Yeah, yeah. So.

A (45:23)

And is there, is, is there like a couple others or, or there's like maybe like a longer tail?

B (45:28)

There is a very, very long tail, I would say other small, smaller companies that have launched. Sandstone is one that does kind of in house enterprise legal that they've launched pretty recently. And then there's like a really long tail of other startups doing similar things that I think a lot. Like this vertical has matured, this AI vertical has matured so, so fast that it's been, I think, really, really hard for this long tail of companies to catch up to the point where it's almost like every three weeks now, one of these small companies comes to us looking for maybe an acquisition or something like that. And they've built decent customer bases. But it's shocking how fast this vertical has moved.

A (46:20)

Oh, so have you guys done any acquisitions or exploring? Some.

B (46:24)

We are actually looking at two now. And yeah, part of our strategy this year is definitely to, you know, roll up some of these smaller companies that couldn't, couldn't quite get a foothold. The market moved a little bit too fast. They've built, you know, maybe something similar or more lightweight. They have a little bit of a customer base and like, we look at the math and it's like, you know, we could spend money on Google Ads or we could just, you know, acquire a bunch of these small companies. So I think like there has, you know, there is consolidation happening for sure. Like, legal AI has been very hyped

A (46:56)

and that's fascinating because you think there would probably not be as much consolidation this sort of early into a hypergrowth market. But it's probably that there's. It's just like these massive fluctuations in product capabilities, adoption.

B (47:14)

Yeah, it's so fast. I mean, the speed you have to move to keep up with the market is really, really fast.

A (47:22)

Have you guys found, were there certain times where the models would maybe like OpenAI or Anthropic would release new model and just suddenly Spellbook worked so much better. I know a lot of people have kind of had those.

B (47:33)

Yeah, I mean, that definitely has happened. We started building spellbook on GPT2, so that was tough. GPT3 was a little bit better. And yeah, it was funny. When ChatGPT came out, everyone was like, oh, my God, what's going to happen to spellbook? This was 2022. Everyone was worried about it. And when ChatGPT came out, our growth just kind of exploded because the model started getting better that we were using and lawyers were getting their feet wet in these kind of generalized AI experiences and then searching on Google. I want ChatGPT for lawyers. That's literally what they search. And then they would find Spellbooks. So every time the generalized models and platforms have launched new things or gotten better, it's generally been very good for us both from like a capabilities perspective and, you know, in terms of, yeah, just getting lawyers interested in AI enough to look a step deeper. Yeah, one, one mantra we have at Spellbook is like, that I think a lot of other companies have maybe gotten wrong is like. And I think is important for everyone to think about when they're adopting AI is we say it's time to cut trees with a blunt, chop down trees with a blunt axe. There's the Abe Lincoln quote, if I had six hours to chop down a tree, I'd spend five hours sharpening the ax. And I think a lot of engineers and a lot of knowledge workers, we're used to the idea of mastering a tool and then getting dividends from that mastery. But the reality in AI now is there's no time to master anything. Every six months, a new tool or a new model comes out. And we've had to teach our engineers, look, we can't sit around optimizing around GPT3 because GPT4 is going to come out in six months and we can't try to master this thing. No one is going to have time to master this thing. So a really important part of our culture is there's no point in sharpening the ax when the chainsaw is coming out tomorrow. And so what we teach our team to do is drop the axe, pick up the chainsaw, you know, stop, you know, and keep moving on, marching forward, implementing new models, new techniques, delete old code very quickly when it's not needed. And it's. As an engineer, it's like, it's a. I think it's like an unintuitive culture. You know, it takes. I think like one of our advantages is also just the culture that we've been building these products since, like, 2022. And, like, our team has kind of learned how to do this, which I think, like, the natural instinct of a lot of experienced engineers is kind of in the opposite direction. Like, I'm going to build a really complex, robust system around this model, but then the next model comes out in six months, and then, you know, you just have to delete all that code. So it's. Yeah, it's an interesting time to build software.

A (50:30)

Yeah. Because, I mean, I guess, isn't there this risk, though, that the models don't get better? Like, the chainsaw doesn't come out right, and then it's like your ax isn't sharp and you're screwed? Yeah, I mean, I guess it can kind of go both ways.

B (50:43)

Yeah, I mean, there is that risk, but, yeah, I mean, I don't think we're there yet. I don't think we're seeing this sort of plateau yet.

A (50:51)

That's fair. And is there, like, a reason that you have that specific viewpoint? What are you seeing to make you so confident? And then maybe how does that relate into how sort of, like, the AI software is going to change? Like, when you look five years in the future, is there still just, like, so much more room to use current capabilities to make the products and the feature so much better, or.

B (51:15)

Yeah, I think you just look at the trajectory. It's like, I'm Canadian. Like, you're skating where the puck is going. And the thing is the puck, if you just draw a trend line, the puck is moving way faster than it ever has before. We've never seen technology advance at this. This pace in our lifetimes. So a lot of people and just trying to do the math of skating where the puck is going, they're thinking about the puck speed that you might have had in 2015, but it's actually this really accelerated speed. And I think it's just the math of what angle do you want to go at? How ambitious do you want to be? If you are looking at the trend of where things are going and you. And you point your angle to meet the puck at the right place, you're going to be a lot more ambitious. And we've done that again and again. What we do is when we start building a feature or product, we aim to build things that are not doable today. And that's a really, really important feature. That is a hard thing to tell your engineers. It's like your goal is to start building something now that will not work today. It will work in six months when the models get better. That's how you actually time the building of these features. Because if you build something that's achievable today, it's not going to be that impressive in six months.

A (52:33)

How do you know what's an okay degree of not quite yet possible but will be possible soon?

B (52:40)

Yeah, I mean it's an intuition. It's like shooting a basketball or something. You get a feel for just watching the technology. I think being plugged into into X is really good for just sensing the velocity. I think x is generally two years ahead of LinkedIn on this stuff. So if you're plugged in there, you're going to be seeing what researchers are talking about, you're going to be seeing what engineers are hacking on. And if you understand how the tech works, you'll see that there's a sequence of advancements that will inevitably be made that are going to make things easier. A lot of the advancements now are not even at the model level necessarily, but it's just in terms of figuring out the right techniques for like how do you schedule an agent to operate in the background rather than needing to be prompted. What techniques do you use for planning and how do you implement planning for long range tasks? These are things that are rapidly being iterated on and you can pull those into your software very easily.

A (53:44)

Are those things you guys have thought about at Spellbook?

B (53:47)

Yeah, yeah, a lot. Yeah, quite a lot.

A (53:49)

Is it there yet? Like, is it in the product right now?

B (53:52)

Like planning for long range tasks or like the scheduling of.

A (53:55)

Yeah, like agents doing stuff.

B (53:57)

Agents doing stuff? Yeah, yeah. I mean so we definitely have agents that can do very long running like drafting tasks today.

A (54:04)

So what's an example of that?

B (54:06)

Yeah, I mean one example would be like the financing transaction, like VC financing transaction example I gave. So you have a term sheet and you need to draft like a full set of NVCA docs that could be like a thousand edits. Like it's, it's actually quite, quite detailed. I don't know if you've seen like the full template set but like before Allure has edited it, but it's like there's tons of optional language, there's math you have to calculate. Like it's, it's a very deep problem. And so like that's, that's something that we can do quite accurately and as like kind of a long range asset. It's not just like filling in the blanks, it's like you're cross referencing these documents Making sure they're consistent, doing math, making sure that the math as of like you're calculating share prices and things like that. So like that's, that's kind of, I think where the state of the art is but the, that product. So Spelberg Associate is our agent product. We started working on that like I said before it was function possible. We, we launched the first version of that product in like alpha like almost two years ago now. So like it was the first long running, you know, agent for multi document legal work ever launched and it didn't really work when we launched it, you know, but then the models got better and better and we got feedback and then you know, today it works really, really well. And then the next thing that we're really excited about is like agents working in the background. I think, you know, when people think about like, you know, is AI overhyped or not? The biggest thing on my mind is the way most people use AI today is you put in a prompt and it works for five minutes and then you get an answer back.

A (55:43)

It's just kind of like better Google.

B (55:45)

Yeah, better Google. Yeah, better Google. But the thing I think about is imagine having an employee like that. You go to the employee, you ask them a question, they work for five minutes and they give you an answer and then they do nothing. That's like the worst employee ever. That's what we have today. It's like the worst employee ever who if you go over their shoulder and ask them a question, they'll work hard and give you something but after that they do nothing. They just kind of sit there and there's such an easy gap for us to jump to. Say well, how do we make these agents work in the background all the time like an actual employee pushing the boulder forward without us? I think that is going to be like a 10x for AI and agents. So I don't think people understand how impactful this technology is going to be is when you have like a AI coworker in your Slack who's like, I know, fishing through your emails, finding work to do, looking at what your clients are asking for. Say if you're a lawyer, you know that's going to be just this massive, you know, leap forward in productivity. So that we're working on, on that now for like basically getting to the point where you know, your spellbook agent can be in Slack as like this artificial legally competent coworker that you can delegate stuff to.

A (56:59)

Yeah, because I can think of from the VC perspective, it's like you almost Create this thing where you figure out the first engineer at Spellbook leaves and their LinkedIn says they're working on a stealth startup. And my tool automatically messages them and gets on a call. And even it's like an AI agent that's doing the call. And then I get all this information and I'm like. And I usually get an email. It's like, would you like to invest or not or whatever? That'd be pretty incredible if it. If it does that.

B (57:30)

I don't think we're fair. I don't think we're that far from that. Yeah, I don't know how we're going to deal with the noise of, like, AI agents calling and emailing everyone all the time.

A (57:37)

I get that a lot. Like, you get all these, like, spam.

B (57:40)

I do. Yeah.

A (57:41)

Yeah.

B (57:41)

You're not very good yet.

A (57:42)

Yeah, yeah, yeah. And I. And I don't think, like, if I'm. If I'm that founder, am I going to, like, do a call with a VCAI associate? I probably wouldn't, though, but I might do a call with, like, the guy who is making the investment decision or whatever. But in a sense, you do maybe skip through some pieces of the process and just more efficiently get to. It's like humans making decisions ultimately based on information from the AI. So maybe you do save some time. I'm not sure. I do go back and forth of personally trying to wait out. What are the ways that I just completely lean into AI and what are the ways that I just completely, like, just choose to not do it at all and just lean more into, like, podcasts is, like, interesting. Like, meeting in person, hanging out for two hours. There's, like, no technology really in this, aside from, like, cameras and mics. And we're just talking and communicating about this thing. And, like, that's probably, like, a good way to just, like, get to know you better and, like, build a relationship versus, like, I don't know, we could have, like, had our AIs exchanging information or something, but, like, that's not interesting at all.

B (58:54)

Yeah, yeah.

A (58:55)

This is like, you almost, like, transcend and go, like, above the technology in a way. I don't know.

B (58:59)

That's true. That's true. Yeah. I think about that so much with, like, writing and, like, tweeting, like, to me, like, AI writing, like, creative or informational writing is, like, so obvious still today. Like, the GPT isms that everyone makes fun of. Like, you know, it's not this. It's that thing. Like, you know what I'm Talking about, like, kind of.

A (59:18)

Yeah. I honestly don't even catch it because I don't do enough AI writing. Like, I don't use it enough for writing.

B (59:24)

Yeah, there's like all these tropes that you just pick up on and like, you know, the minute. The minute I see them, you know, I feel like, oh, this is. If it wasn't worth the time for this person to write this, then it's not worth the time for me to read it. Because in a way I think, like, you know, the fact that, you know, we're taking the time to sit here and have this conversation, or the fact that someone's willing to actually sit down and write something themselves indicates that they thought it was important enough to invest that time. And that means for the reader or for the viewer, wow, that's an indication that this might be worth my time as well. And it's kind of like a proof of work with Bitcoin and stuff like that. I think of it as writing as sort of this proof of work. And the minute someone at Spellbook sends me a recommendation of something we can, we should do, and like, the whole proposal is obviously written with AI. I'm like, I can't. Like, this doesn't. I can't trust that you thought. Actually thought about this enough, you know. So to your point where to not use AI, you know, I think writing is an area. I'm like, creative writing, writing recommendations, something I'm careful with. Luckily, contracts are very like, they're not meant to be creative whatsoever. They're very formulaic. Lawyers are not trying to be original. And so that's one reason, I think, also why legal AI has taken off so much, especially in transactional work, is there is no desire for really originality in contracts. People want to use standard language.

A (60:56)

Yeah. Because if you think about it, a business contract is almost like the programming language of business. I guess, if you want to really get philosophical about this stuff.

B (61:08)

Exactly, yeah. Code. And so AI for code and AI for legal. I think in both of these areas, you're not trying to write creative original code or creative original contracts. You're trying to make functional documents. And that's why I think AI works really well in those areas.

A (61:26)

Yeah. And I want to talk a little bit about maybe early Spellbook stuff because you have some interesting history of the company, but going back even further than that, you. Your first company that you started, you made an instrument, invented an instrument. So what was that and how did it go?

B (61:43)

Yeah, so that was my first kind of ill advised Startup. I was super into electronic music. Grew up. I studied computer engineering, but I grew up making electronic music, DJing, things like that. And I met this composer who was composing these awesome pieces of classical music, but incorporating these electronic elements. And he was like, it's really frustrating that there's no electronic instrument that fits into that atmosphere that the audience will really appreciate. If you go with a DJ turntable to a classical concert, people are like, I don't really understand. Is this person just hitting buttons or whatever?

A (62:25)

They press play, but then they're like,

B (62:27)

are they actually doing anything? Are they not? And so we conspired to build this instrument that would allow an electronic performer to, like, really show the cause and effect of what they're doing for these sorts of electronic performances. And, you know, shape, kind of like guitar or something. It has this, like, beautiful wooden frame, kind of like an acoustic instrument.

A (62:45)

And you almost like, hold it like you would an accordion, like, sort of in front of you.

B (62:50)

Hold it on your lap or whatever. Yeah, yeah. So it can face towards the audience. You can use it in, like a desktop mode as well, but it has all these lights and things, so it kind of makes it obvious. And.

A (62:58)

Yeah, there's like buttons and sliders kind of or something.

B (63:01)

Yeah, Button Sliders has a synthesis engine, it has a drum machine. You could use it in all these sorts of ways. And that was my first very naive startup when I was fresh out of college. Very little money.

A (63:14)

So you created this thing, made it. And we're kind of mass producing them, but not.

B (63:19)

We started producing them, selling some of them. We ran a Kickstarter. A couple things happened that kind of, you know, kind of three things happened that, you know, kind of set it off course. One was I got a really big legal bill. So, you know, one of my first bosses, this guy, Wally Haas, who ran this company, Avalon Microelectronics, that I worked for. It was one of my first internships. He invested 20k in the company, and that was a lot of money for me as a broke student. And he was like, this will get you to your next milestone. And then one day, we got a 10k legal bill by surprise that took half that cash out of the bank account. And for me, that was an enormous amount of money. It was half the angel check we had, and the amount of value we had gotten from that seemed very, very little. So at that point, I started thinking about, okay, I think there's a way bigger problem to solve than electronic music instruments. So that is where the idea for Spellbook came from was that frustration, but some other things that happened through that experience. A hero of mine is this guy, Roger Lynn. He built one of the first digital drum machines in the world. So if you listen to 80s music and you hear that snare drum with the big echo, that might be a linn drum. And I got to go to namm, which is this big trade show for musical instruments. And I got to meet Roger Lynn, this hero of mine, and awesome guy, super friendly, but his company was still only two people and he had dedicated his life to building his instruments. And I was like, I'm really glad he did that and I really appreciate it. But I don't know if electronic instruments are what I'm going to dedicate my life to. I don't know if it's a great market. And the TAM for niche electronic instruments is pretty small.

A (65:17)

Maybe a million dollars, maybe a little more.

B (65:19)

These are like, they're expensive to build. And yeah, I learned a ton about producing hardware and it was really fun. But maybe we'll get back to it as a hobby someday. But the legal market, having that pain myself and just seeing the size of the TAM of how many people touch contracts every day, it's gigantic. You know, like companies like Harvey and Ligur are really focused on the lawyers and the lawyer market. And I've talked a lot about lawyers, maybe 20 million lawyers in the world. But if you think about how many people you know, kind of touch contracts, it's way, way, way beyond that 20 million number. So I was really inspired through that experience to start Spellbook because like every

A (66:04)

salesperson, when you close a deal, there's a contract related to it that you probably touch.

B (66:09)

Exactly.

A (66:10)

I mean it's really any kind of business transaction that happens. There's like, you know, there's some handshake deals, maybe you don't sign a contract, but like most people do, even the handshake deals that I do will do like a one page contract, which is just like we won't screw each other, but like we're just making sure that we can't screw each other basically with our very rough contract.

B (66:32)

Yeah. And even like an email can be considered, you know, a contract, you know, legally as well. Yeah.

A (66:38)

So, and, and, and so you like, you were like, holy cow, I paid half of my bank account for this legal bill. I'm going to try to fix the legal market. Like how did, what, what happened from there?

B (66:48)

Yeah, so I actually I, you know, I stewed on the idea for a while. I kind of, I worked at a Network monitoring company and was the director of engineering there, kind of building out that product for a while. And then I was kind of working on this in the background, trying to figure it out. And we went through a ton of different iterations. First in my mind, smart contracts were big and I was like, oh, maybe Ethereum smart contracts will be this automated type of contract we can all use. That really obviously wasn't going to work for a bunch of reasons. We showed blockchain smart contracts to a lawyer and they're like, I will never use this. We threw that away pretty quickly. But where we kind of landed was we had this product called Rally. It was a template based product. So there was no AI at the time. We actually launched in 2018 originally and we sold that to about 100 law firms. And basically what it let them do is build these really advanced legal templates. So if you're doing a bunch of NDAs or sales agreements, you can build a template on our platform which goes does a lot of things that you wouldn't be able to do in a normal templating engine. It's built on word, it can ingest legal data and then you could kind of spit out contracts much more efficiently. But for a bunch of reasons, there wasn't real PMF there for a long time. We were able to do 100 sales. We had raised some money. Our board was kind of like, let's get on with it and scale this thing. And we're like, no, we don't think we have PMF Product market fit. Our view of product market fit is basically the customer is pulling the product out of your hands faster than you can keep up with. Until we hit that, we're not scaling the company. So we kept the company super lean for a really long time as we built that out. And we actually launched like over a hundred landing pages.

A (68:37)

This was like a three year period. Yeah, I think you see posted this one chart where it was like, it was just like the revenue, I think of the company. And there's a point it was maybe 2020 where you're like, we lost half our revenue or something. What happened there?

B (68:53)

Yeah, so we built out the platform. We did sell to kind of like the big law innovation committees at first and like, you know, we had some like very lucrative kind of early customers and you know, one of those customers churned and was half our revenue and like, like we literally lost half our revenue overnight. But we kind of felt that, you know, that wasn't bringing our product in the right direction kind of again What I talked about earlier of, like, working with these innovation committees, they're not the actual users. And so we're like, you know what? We're going to start selling to these small firms, solo lawyers to start and kind of snowball our way from there.

A (69:29)

So this is around when you started to test, like, a landing page. So you launched a hundred landing pages in three years. What does that mean? On practical, you were basically doing one every two weeks, roughly.

B (69:40)

Yeah, yeah, exactly. So we launched one every two weeks. Sometimes we would actually launch a product variation with the landing page. So at one point, our view was like, if we roll the dice enough times, eventually we'll figure this out and find product market fit. And we're optimizing for the number of app ads we could do. At one point, we launched Shopify for law firms. So we took our templating engine and we put a store on top of it. So, like, a law firm could stand up like a Shopify store. Like, need an employment agreement. Like, click here and then it'll use the template to, like, spit one out the back end. Like, we tried an absurd number. We launched the client portal, and we built landing pages for a ton of these different angles and things that we were trying.

A (70:17)

So what's the importance of launching a landing page for someone who's like, what is this even? Like, what is a landing page?

B (70:21)

Yeah, landing page is a single webpage that usually has no links to anything else, that has a single message, an image that is trying to get you to, you know, sign up for a product or sign up for a wait list or something like that. So it's a. It's a. You know, and often, you know, you drive traffic to these through advertising or through social media, through campaigns. So you're not. It's not like people are landing on your homepage and finding it. You're finding a way to drive traffic to it. So what we would do is launch these, like, little ad campaigns with maybe a thousand bucks or something like that. And then we would drive traffic to the landing page and we would see what's the cost per conversion. Like, how many dollars do we have to spend in ads to get a lawyer to sign up for the product from this landing page. And we literally tracked that over 100 landing pages where we could see, okay, we ran this experiment with client portal, and that cost US$100 per lawyer. And then we ran this experiment, and that cost us $500 per lawyer. And you really get a sense of, like, what's resonating and what's not? And you really learn, like, how do you get a message, you know, straight into someone's past their, like, blood brain barrier into their brain, you know, really, really fast. And yeah, then eventually, you know, we launched. No, the AI product was just another landing page. You know, we were like, okay, GPT2 was around. I had used GitHub Copilot for coding and. And we were like, oh, this, this is cool. Like, we're going to try launching like GitHub copilot for lawyers. We're going to basically just launch another landing page with this.

A (71:51)

And what did you call it? Like, what was like the buzzword, like. Because ChatGPT had not launched yet, right?

B (71:56)

No, it had not. Yeah.

A (71:57)

So how did you describe it in like, couple words?

B (72:01)

Well, we called it, we did call it Spellbook and we, you know, the big thing we had was an image. The thing that we would do in a landing page is there's a headline and there's an image and we would design the image or gif or video. Our thesis is like, your image plus headline has to deliver this visceral sense of value in five seconds flat. And I think our headline was just like, Draft and review contracts 10 times faster. Spellbook uses GPT3. People kind of knew what GPT3 was on LinkedIn and stuff. It was a little bit of a buzzword. Even before ChatGPT, people were talking, people were curious about what is this GPT3 thing. And you know, spellbook uses, you know, GPT3 to, you know, surgically redline your documents or something, you know, something like that. And then we had the. But the important part was the image. You know, we had an image of like the word word window and someone just like, you know, clicking, you know, draft and it just like drafts a clause instantly, you know, and that was like the sort of magic moment that once someone saw. Once a lawyer just saw hitting a button and drafting a clause from a

A (73:14)

headline versus having to hand type or copy paste from somewhere.

B (73:18)

It was just cut through. And there's this. Have you ever read this blog post? It's Find the Fast Moving Water on nfx. Have you ever seen it?

A (73:26)

I don't think so. What is it?

B (73:28)

It's a really good blog post. I forget who wrote it at nfx, but you know, the. The author talks about their first experience of seeing Tabulous, which was like a precursor to Uber. And, you know, seeing this app for the first time, I think someone else had it, like his friend had it or something and he was like looking over their shoulder and he was like seeing this app for the first time was like a, I had a neurochemical response to it. Like my pupils dilated. Like blood rushed in my head. And I just saw that the future ahead of me was going to look very different than, you know, what it has been when it comes to like transportation. Like, and I think that was like the magic moment. We were trying to, trying to hit with Spellbook and we hit it and within three months we had 30,000 waitlist signups. Within three months we had more revenue from that product than the other three years we had selling everything else. So it was when you hit that kind of like resonance, you feel it, you see it in the numbers. It's also unmistakable. I don't know if you need to actually measure it as much as we did because when you really hit it, it's like PMF resonance. It's, it's, it's. Yeah, it's unmistakable. You will know it.

A (74:40)

Yeah, it's like, it's like something that you can't quantify because it's just vastly like order of magnitude or more of just like way more resonance and usage of the product.

B (74:52)

Yeah, signups. I mean, yeah, I mean, I mean we couldn't measure it too. Like, you know, the other things we were selling. The landing page might have cost us you know, a hundred or two hundred dollars in, in ads just for a sign up. Now when we first launched this, it was like $5, $10 signups, you know, like just an, almost like an order of magnitude cheaper in advertising to get someone to sign up.

A (75:11)

So you basically pay $5 to get someone to sign up. And then if they convert and use it, they may pay like 10 bucks a month or something and you might get like a 20% conversion rate from like free signups to paid. And then you could do the math of saying like, okay, we need to acquire a fully converted user costs us about $25 and they pay us 10 bucks a month. So within three months we are making money off of that customer.

B (75:35)

Yeah, yeah, that would be like your CAC payback. Yeah. And I think our original price was like 49 bucks a month. Today it's more like 500 bucks a month. So no. VCs also talk about like, oh or like price is going to go down. Actually our price has only gone up as we've added more and more value, you know, to, to our customers. So. But yeah, that's, that's, that's how you can do the math and you know it is measurable. And like our, when we hit that moment, like our salespeople's calendars were just completely blocked. Like every day of the week they would have eight sales meetings, you know, or onboardings. Actually, we didn't do sales meetings. It's just onboardings, you know.

A (76:08)

So what were some of the other biggest things you think then you learned over that launch or landing page period? And then just this thing worked and you just had to start going. Any lessons?

B (76:20)

Yeah, I think the biggest lesson for us was, or what I would tell other founders is yeah, if you keep beating, resilience is incredibly important. Obviously if you believe in something enough and keep beating your head against the wall long enough, you will probably figure out something eventually. And if you keep your burn rate low, like you can make money last a very long time if you're scrappy. But the secret is you have to really believe in the problem if you don't believe in the problem. If we didn't believe in the problem that we were solving, we never would have gone through a hundred landing pages for years of grinding with the super small team with very low salaries. The reason we were able to do that is because we believe that legal efficiency was unsolved, that software still had not really changed how law was practiced and that a lot of people needed legal services less expensive. A lot of in house teams needed more efficiency on their contracting. And we just really believe both. One, this problem is huge when you think about the scale of it. And two, that it is unsolved. And if you believe those things, you can be very resilient.

A (77:32)

So then when this thing really started to work, you did not have a product yet and were you just like, oh, we gotta like actually build this?

B (77:39)

Oh no, we did. I built the product. A really crappy version of it that like our ENG team tore apart. But like, okay, I built first we thought this was gonna be like a lead magnet. Like we didn't even think people would, this would be like a product. We were like, we thought it'd be like a cool marketing splash. Like, oh, first company to bring GPT3 to lawyers. Wouldn't that be like a cool press headline and then people will convert to our other product. That's what we thought would happen.

A (78:03)

Really?

B (78:04)

Okay.

A (78:05)

And then eventually this became the product, right?

B (78:08)

It is the product. Yeah, it is. 99.9% of our revenue is from Spellbook today. A very small amount from our previous products. But yeah, we actually built the prototype on repl dot. This is before repl dot had AI Features. I built it in a couple weeks of evenings and weekends. It wasn't a board goal. Like, no one really knew it was being worked on. It was just like a fun little side project. And I built a really crappy version of it on Replit. And like, Replit ended up using us as like a case study after, like. Because the thing I said is like, you know, it was such an amazing platform because I had this like, bolt of inspiration and it would have perished, like, if it weren't for a platform like Replit. I would have just not found time to work on it and it would have just died in the, in the shower, you know, where I had the idea and. But because, you know, ideas are perishable and you have to like, rapidly chase them down because I was able to deploy it really fast. Yeah, we actually did have a product, you know, on, on day one. It was. It wasn't great. The engineering team ended up having to like, basically rebuild it from the ground up. But.

A (79:16)

Well, and I think the other interesting thing that I've heard you say before is that you were never like, attaching these experiments to kind of a legacy product like you. This is called Spellbook. It was a different sort of product.

B (79:27)

Exactly. Yeah. I think that's. That's one of the things I would have done even more differently. Like, we learned this towards the later phase. Like, you know, I think there's this instinct for founders pre PMF to just keep stacking on features and like the list and the website gets longer and longer and more complex. That does not make it easier to pitch your product, especially in the earlier days. What you want is a pointed, easy to understand thing. So with Spellbook, our goal was like, we're not going to talk about any of the other things we built for the last three years. We're only going to talk about this in a really pointed way to make it super simple for people to comprehend. And that was part of the success. My advice to founders launching these landing pages would be like, just focus it on one thing. Don't list off everything you've built. Because chances are if you're pre PMF, 80% of what you've built doesn't matter and you can delete it and there will be one or two things that matter.

A (80:19)

Yeah, I think the other thing too is like, you. You think there's like all this, like, historical context and history of the product, but literally 99% of people just, they're seeing you for the first time and they don't give a shit about the

B (80:30)

history, you know, people are so busy, they are not paying attention. Yeah, yeah.

A (80:35)

Like, one thing I think I need to do more is just generally tweeting like, hey, by the way, like, I have a. I'm also invest in startups, like, you know, whatever, because I, like, don't do that enough. And there's like so many people that like, they've literally, they followed me for years and they're like, oh, I thought you were kind of like a meme account or something. I didn't know you actually were an investor. They're like, oh, man, I got to like. So sometimes it's like that of like just saying the message over and over again, like reminding people the thing that you do.

B (81:00)

Almost. Yeah. This is one. One of the things I've been learning from Keith since. Since KB's come on board. I think that's something we were going to chat about.

A (81:10)

Like, well, I wanted to ask you. So. So Keith, you guys really took off. You started growing really quickly. So what happened? Did you like, was it instantly you're like, okay, we have pmf, we're leaning into this. Was there a debate of, like, how do we need to figure out this legacy old thing? How did you manage that?

B (81:28)

It was funny. No one knew we were working on this thing. The board had no idea. By the time we got to our board meeting, it was almost like a mic drop moment. It was like we launched surprise. We launched a new product. Here's the growth chart. And it was just like immediate consensus. It was like, wow, like, everyone's like, you need to go chase this thing. Don't worry about the other stuff.

A (81:50)

And you'd been holding off, right? Like, weren't your investors kind of like, we have pmf, like, we need to start scaling?

B (81:55)

Yeah, yeah.

A (81:56)

But this point, it was like, we truly do.

B (81:58)

Yeah, yeah. I mean, yeah, it was a pretty amazing moment for the team because we were so, like, we went through like the 2020 era, like the, the ZIRP era, pure ZIRP era, where, like, people were scaling way too fast pre pmf and like, we really, really resisted it. And sometimes we felt, you know, insane for doing it.

A (82:20)

Are you missing out because you didn't do like a remote, like video calling tool or something?

B (82:25)

Yeah, yeah, yeah. And like, and, and like, for not scaling the company, like, everyone around, all the companies around us were just scaling, scaling, scaling, whether they had PMF or not. There was a lot of capital sloshing around and our investors were feeling like, the pressure also, I think to some extent to like, okay, let's get the show on the road. And it was a very validating moment for the whole team to be like, this is what we've been talking about. This is real product market fit. And the board was like, yeah, you're right. I'm glad we waited for this moment. And then it was just very fast consensus. It was like, we have to scale this now.

A (83:03)

I think when I first came across you, maybe I'd seen you, but it was an unconscious conscious, like, oh, that's like. You tweeted just. It was like a graph of your ARR growth or something. And you're just like. You were like, going to San Francisco to fundraise or something, and you just posted the growth. And I remember seeing it, I was like, oh, nice, that's pretty cool. And I just retweeted it because I was like, thank you. I hope that you have a great fundraising round or whatever. And so how did that process kind of go of raising this? I think that one specifically was a series B.

B (83:35)

That was our recent Series B. Yeah.

A (83:36)

Okay, so just take us kind of like beginning to end, just how that process went.

B (83:42)

I will say it was the easiest raise we've ever had because it's just numbers and metrics at this stage.

A (83:48)

Were you just kind of showing the

B (83:50)

spreadsheet, basically showing the numbers, showing the growth. And, you know, it's. It's been really good. So. And it's much easier than the pre PMF phase where you're like, you know, convincing of just pure. On pure vision. At least. At least for me, it's. It's easier when. When you kind of, you know, point to the numbers. But, yeah, we started the raise, I made a tweet, and I was like, hey. And I was like, my goal raising is usually like, compress it, you know, into as tight a time as possible because I want to be working on the product. I want to be working with our customers. No offense to. I like hanging out with VCs, but to move the company forward, I have to be constantly working with our team and our customers. And so you want to compress it, and you also compress it in order to create deal tension. If you dilute the deal tension across six months or whatever, no one moves. Everything is sluggish. And so I made this tweet. I tweeted our growth chart, and I was like, yeah, raising our series B. I'm going to be in New York this week and SF the week after. And that was it. And so it went viral, maybe because of the chart. And we got bombarded. My email, I still have emails I've not responded to from that moment from funds who reached out. And yeah, I set up in a hotel in New York for like a week. And yeah, most of the investors all came to us. So like, one of the strategies I learned from another founder is like, you know, try to, if you can, sequence your meetings. You know, we rented a boardroom in the hotel and we just said, hey, you know, come here. We're taking meetings this week, can dictate the schedule. So we had a lot of investors come by there, visit a couple offices, and then did the same thing in sf, basically. And then we got in touch with Keith and he was like, I only take in person pitches and he was in New York and I was in SF and I had to fly all the way back and cut the SF time short.

A (85:56)

And wasn't he the only one who didn't come to you?

B (85:58)

Basically, yeah, basically, yeah. I also went to the KV office as well. Sumit Kanu there as well.

A (86:09)

And what was kind of the difference? I know you said there was a pretty big mindset difference almost between east and west coast investors. What did you kind of experience there?

B (86:16)

Yeah, for me, it was very night and day for the most part. Besides Keith, who I think is a very. He's smart on the numbers, but he's unique in how qualitatively he assesses the world. He's very willing to bet on qualitative things. But the New York investors are extraordinarily quantitative, and it seems like they almost have all converged on the exact same spreadsheet that they use the exact same metrics, exact same benchmarks, exact same spreadsheets. And to the point where it's kind of a little bit absurd because if everyone's looking at the same spreadsheet, then where's the alpha? If everyone's looking at the same thing, they're going to pay the same price, everyone's going to bet on the same companies. There's very, I found, you know, there's very little emphasis on like the, the qualitative side with, with a lot of the New York investors we pitched. Whereas on in sf there was a much deeper focus on the qualitative vision of the company. And, you know, how. How is the future going to play out and why and kind of like, you know, the, the idea maze, you know, kind of exploring the idea maze and understanding where things are going to go. Very different. Yeah, very different.

A (87:26)

And then you end up Keith and KV Coastal Ventures Leather Round. What has it been like working with Keith just over the past couple months?

B (87:35)

Yeah, incredible. So yeah, we've had a few board meetings and yeah, Keith's an incredibly sharp investor. He's been in the weeds. He really understands how things work on like a, a deep, deep kind of like CEO level. And yeah, like we just learn a ton. Like he obsesses about performance and like what, what best in class people look like. And you just learn so much from someone like that. One of the like biggest things I've learned from Keith and that I'm learning is like how to communicate really well. Like this guy is like a nuclear grade communicator. Like how incredibly concise he is, how he's able to. I think he's really good at like counter positioning companies and opportunities to cut through the noise in a way that other people really struggle with. And he thinks about it deeply. It's not like by chance or, you know, he just happens to have this talent. It's like he's consciously very good at thinking about how to get the message to listeners and how to create a movement with a message in a way that I think it's one of the hardest skills for anyone to learn. It's a marketing skill. How do you get your message out into the world? Spreading. To do it, you have to have a really simple message and you have to repeat it a lot. Like you were saying, people not knowing you're an investor, you have to find a way to repeat that message or get that message in front of people because people are so busy, so distracted, they have no time. Like getting someone to read more than five words is an extremely hard challenge. For the most part. Getting someone to watch more than a five second video clip is pretty difficult a lot of the time. So I mean, he's just so aware of that and so good at dealing with it. Like an example I'll give you, we did like a series B announcement video and I think we booked like 45 minutes for Keith to come down and talk about his view of the company and the opportunity. And we're sitting down in front of cameras kind of like this and he sits down next to me and he just hits his lines. He's just like boom, boom, boom. He has five bullet points about why this opportunity is incredible, why the contract opportunity in particular is really special, and how the market data that we're collecting is going to change how contracting is done. I mean he communicated everything in like five minutes. And then he was, he was like, okay, I think we're done. Like you couldn't think of anything else to say? Like, that was it.

A (90:13)

Yeah.

B (90:13)

And like, that's. That's the pattern that you notice in the board meetings too. It's like, you know, you'll ask for feedback and he'll say it in like one sentence, you know, and then. And then he'll be quiet. And it's like he's so good at getting to the heart of the matter and then letting the core message breathe and be received.

A (90:34)

Are there things you've changed about marketing or messaging over the past couple months then?

B (90:41)

Yeah, definitely. I think focusing more on delivering our core message, what we're all about, and repeating it to the point where it can become kind of boring for the speaker, I think just doing that more and doing it better.

A (91:00)

One last thing I wanted to ask you about. I feel like you're maybe bleeding edge of AI. Personally. What does your personal AI stack look like? What are you using? What kind of products and things are you taking advantage of?

B (91:15)

I've tried a ton of stuff. Obviously, I use cursor and Claude code on the engineering side for mainly building prototypes and things like that. The product I'm loving right now is Twin. Have you tried Twin? So have you ever seen it?

A (91:31)

No, I've never.

B (91:32)

I mean, it's. It's on the surface, very simple, but I think they just got like this generalized agent formula, really. Right. So how it works is you can go in there, you can say, I want to build an agent. You build an agent by prompting. You don't have to like, write code or connect together boxes or anything like that.

A (91:49)

That's always the most frustrating was there's one called like, N8N, I think.

B (91:53)

Yeah. N8N. Yeah.

A (91:54)

Like, I never ended up getting it to work, so I was like, just. I don't. I don't care enough to like, figure this out.

B (91:58)

Yeah, the way this works is you're almost like vibe coding these agents with like a couple prompts. And it's really good at like, scheduling to work in the background. Like what? Like what I was talking about earlier. It's like you don't want an agent that you have to prompt to do work. You want it to work on its own. And so I built probably about five agents with Twin now that I use daily. Yeah. One of them is my Canada recruiting scanner. And what it does is every morning at 8am it scans Twitter. We hire in the U.S. but most of our team is in Canada. We basically scan all of tech Twitter and find Canadians who are tweeting about AI and looking for engineers, designers and interesting people saying interesting things. And that fills our queue for like recruiting. And that's, that's been a huge help.

A (92:45)

So do you reach out to them or is like the team, like, what's the process then for that?

B (92:49)

Yeah, I mean, we, we manually kind of evaluate like myself and like our hiring managers will then look at the candidates and then we will do the reach outs ourselves. We're not at the point where the agent goes and reaches out yet, and we're kind of like tuning the, the quality and the filtering and stuff like that. So, yeah, that's been really useful. We have, we have another agent that does a similar thing where it's just like digests all the feedback from every channel from Slack, from email, from HubSpot, and we'll basically summarize all of our product feedback every day in Slack. Why are people churning? Why are people expanding things like that?

A (93:21)

So that's probably the biggest one, Twin.

B (93:23)

So for me, that's the thing I'm loving the most right now. It's just so easy. It's so fast. I think you want something that's so easy that if you have, you want to make it so that if you're dealing with a problem, oh, I need to find my next podcast guest. That it's so fast to set up an agent to do that that it almost takes you no extra time. So it's like, oh, I'm going to go look for podcast guests or search Twitter or something.

A (93:50)

I was going to say you almost want. The process of creating the agent is actually faster than just going on and

B (93:56)

doing the thing, actually. Yeah, that's right. Yeah, it's actually faster. And that's like, twin is the first thing that's actually hit that level for me where it's like, I might as well create an agent. And it actually works. Yeah. And I love the other thing I love. It has so many integrations, so it can suck in from so many things and then it can pump into Slack. Like, I think the thing I always think about with products, like, the hardest thing is getting people in the habit of actually using them and getting the products in people's faces and like getting our team to like go to a new agent product and changing their habits is really tough. But if we can pump the agent output into Slack, you know, into channels that people are in, you know, the usage is much better.

A (94:35)

Yeah, that's pretty cool. Well, I'll throw a link in the show notes, people can check it out. I'm going to try it. I'll see. I'll let you know what I do with it. Yeah, but this is a lot of fun. Thanks for coming on the show.

B (94:43)

Thanks for having me, Turner.

C (94:45)

And thank you for listening. A quick thanks again to Flex for supporting this episode. Upgrade your spending to Flex Elite to get $1,000 on your first card using code Turner the Waitlist link in the description. If you enjoy this conversation, please like comment. Subscribe and share this episode with a lawyer friend who is still manually reviewing

A (95:03)

all their contracts by hand without AI.

C (95:05)

Make sure to check out the back catalog of over 100 episodes with the founders of companies like Robinhood and Mercury. Tune in over the next few weeks for guests like Mike and Nikhil at Footwork, Chris Hodgkic at Hanover park, and Sophia Amoruso, founder of Nasty Gal and Trust Fund. If you want to miss any of these, subscribe to my newsletter. The split linked in the description. Get each episode plus a transcript emailed directly to your inbox every week. Thanks again for listening. See you next time.