Strongsuit Shows How Legal AI in Litigation Moves From Chat To Workflow

We sit down in this episode of the LTSF podcast with Justin McCallon, CEO and founder of StrongSuit, to get concrete about what modern litigation AI looks like when it’s built around real attorney workflows. Justin shares how his experience in legal transformation and early gen AI product work shaped StrongSuit’s approach: help litigators from intake through trial with research, drafting, doc review, timelines and statements of facts, deposition prep, and even oral argument practice. From that overview, Justin highlights just one of StrongSuit's standout features: an AI appellate judge that can interrupt, question your positions, and adapt in real time based on the case materials you upload. 

Your podcast host, Charlie Uniman, and Justin also dig into the engineering choices behind reliable legal AI: why StrongSuit emphasizes visual, multi-step workflows over an open-ended chat box, how “lawyer in the loop” review fits into quality control, and how a curated 11 million case law database plus retrieval augmented generation supports stronger results. 

We close with a wider lens on several salient aspects of today's AI-in-legal market;  namely, the looming competition in legal A between the foundation models, on the one hand, and vertical legal tech vendors, on the other; what may keep VC interest in the legal tech vertical hot; and advice to founders on focus and on building fast with AI-assisted engineering. 

If you like the episode, subscribe, share it with a litigator or legal ops leader, and leave a review with the one litigation-driven workflow you most want AI to improve.

Transcript

Legal AI Market Map Milestone

SPEAKER_01 0:00

Hello, podcast listeners. This is your podcast host, Charlie Uniman. Interest in AI applications for the legal market continues to grow. In fact, from what I see from my everyday reporting about legal tech, it's no exaggeration to say that that interest is growing exponentially. Therefore, there's no better time than right now for listeners to learn about Legal Tech Hub's Gen AI Legal Tech Market Map. I'll turn the microphone over now to Stephanie Wilkins, Legal Tech Hub's Director of Content. So Stephanie can tell you more about just some of what this Legal Tech Market Map reveals about AI in Legal.

SPEAKER_00 0:42

Hi, Legal Tech Startup Focus listeners. I'm Stephanie Wilkins, Director of Content at Legal Tech Hub, and if you follow us at all, you'll probably know that every quarter means a new update to the LTH Gen AI Legal Tech Map. You may have seen it going around LinkedIn. It's the big blue mosaic of Legal Tech logos broken down by category of AI solution. The first quarter of 2026 has flown by and we just issued our first map update of this year. This one's a milestone we've genuinely been looking forward to. As of March 2026, we've crossed the 1000 logo mark. That means that in the LTH directory, we have 1,014 Gen AI product placements from 806 vendors across 19 unique categories. To put those numbers in perspective, we launched the very first iteration of our Gen AI map in February 2025 with 400 logos, which bumped up to 505 just two weeks later for Legal Week 2025. Now, one year on, we've essentially seen the legal AI market double. What's interesting is not just that the growth has been steady for the last year, but also where the growth is coming from. Early on, it was an in-house categories like AI legal assistance. Now we're seeing the biggest gains in areas like law firm operations and compliance, which signals that AI is genuinely maturing in legal and making inroads where it can have real operational impact. The conversation is definitely shifting from if to how, and that's a good thing. You can find the latest map and our full analysis at legal technology hub.com.

SPEAKER_01 2:17

Welcome everyone to yet another episode of the Legal Tech Startup Focus Podcast. Uh here it is still in very chilly Northeast United States. Um we'll ask our guest, he'll remind me where he is, I think I recall, and what the weather's like there. But uh I'm very pleased, honored, and happy to bring to the podcast uh Justin McAllen, who is uh the CEO and founder of Strongsuit. I guess the word suit gives away the fact that uh StrongSuit is a legal tech startup in a field in which I never practiced, but a field in which I know something from having practiced, and that is litigation. So enough of me. Let's uh welcome uh Justin. Welcome to the podcast.

SPEAKER_03 3:08

Thanks for having me on. It's great to be here.

SPEAKER_01 3:10

Great to have you. Well, a lot has been going on in Legal Tech. Uh Justin and I were uh talking a bit before I hit the record button about um the foundation models, and particularly uh Claude's plug-in for legal. We might touch on that. But uh let's begin, as we usually do on this podcast, asking about how Justin got into legal tech and uh more particularly strong suit, and then we're gonna talk about what strong suit is and does and how it distinguishes and differentiates itself. So give us a little background about your yourself, Justin.

SPEAKER_03 3:52

Sure. Uh I started practicing law in 2011. Uh I graduated from Emory Law School and uh spent a good bit of time at AT ⁇ T. Uh I helped run their legal transformation for their full legal department. We ended up saving over$100 million year over year. It was a combination of efforts from a lot of great attorneys. And uh I was on the transformation side, uh, Bruce Byrd, who's now a um general counsel, is uh what was on the legal side kind of running things. And then the next uh task I had was uh it was running an organization that launched the first Gen AI product uh for our subsidiary, Direct TV. And that combination of work uh for legal transformation plus gen AI made it just very clear to me what potential AI had for the legal industry. And I thought it was a very interesting time where we had spent maybe 10 or so years uh for with various AI-driven analytics revolutions and finance and marketing analytics and similar, but legal was fairly immune to the uh to different software that could really transform true work product and make a very substantive impact on the work that uh the lawyers were doing. And it was starting to become apparent that that would change because you have this tool that works so well with words, and then you have the this huge repository of contracts, this huge repository of case law that it would be able to draw from. This system of precedent was very much in line with what LLMs do well uh with. And I just saw that it was an amazing opportunity, and so started Strongsuit about three years ago now.

SPEAKER_01 5:34

Uh-huh. And it was always from the get-go, strong suit that is uh aimed at uh employing uh somehow, some way, generative AI and uh to the practice of law.

From Legal Transformation To Gen AI

SPEAKER_03 5:47

It has been. Yeah, we we always started with litigation, although at some at one point we did build a few uh transactional tools that are still very useful, but what we've uh decided to double down on the litigation side uh and focus there. And um yeah, we we started with the use case around legal research, uh, and that's where we're we've really built a lot uh around, but we've continued to build out the full kind of end-to-end litigation uh system.

Litigation Workflows Strongsuit Automates

SPEAKER_01 6:13

I see. Well, uh listeners of the podcast have heard me joke and may have even said it when uh Justin and I uh spoke a few uh a few weeks ago. Uh, whenever I told a young lawyer I thought of litigating rather than doing the corporate work I ended up doing, I would lie down and let the feeling pass. But uh uh not not to put a knock on litigators. Uh as long as as long as I don't touch any of the deals that I worked on, litigators are uh are just fine. And of course they'd point the finger at me and say, well, if you don't make any mistakes, we'll never touch your deals. But uh tell tell me then. Um you you were in-house at uh at ATT, you recognized how lawyers uh depend on words. That's their stock and trade. Generative AI is words. You can converse with generative AI by using natural language. It seems like a great fit. Tell me what that fit is for um Strongsuit. Uh I I can see the glimmerings of it with uh uh research, legal research, but from looking at your website and doing a little research of my own, you didn't stop with uh with legal research. So what what Strongsuit do for litigators? Uh do you work with uh big, small, medium-sized firms? Uh what is the feature set and what's your what's your secret sauce, if you will?

Visual Workflow Design And Guardrails

SPEAKER_03 7:32

Yeah, yeah, absolutely. Um as far as solutions go on the litigation side, we'll we'll go all the way through that initial client intake, all the way to trial, and try to help litigators along the way. So what we do legal research and drafting, we help with doc review. If you have just a massive doc review repository where you need to review and find uh the relevant documents for responsiveness and so forth. Um, we can help with timelines and statements of facts. So if you take the time to upload a bunch of uh maybe the record in your case or just a bunch of documents that you want us to analyze, we'll be able to build a timeline out of that and then move, use that timeline to build a statement of facts that can then be used in legal research. Um, we can help with depositions and help with um answering or coming up with questions you might ask an expert as you're deposing her. Um we can help with oral arguments. So if you want to actually speak to our AI, uh and if you're an appellate lawyer, um you could practice in front of an appellate judge as an example where the appellate judge is going to be the AI asking you questions in real time. Um so we we can help with all of that end-to-end. Uh, we do have a word add-in as well. And so you can work on our platform on the web, or if you want to make more real-time edits and changes and kind of interact with AI real time in Word, we can help with that as well. Um, we do have a chat-based feature, but where we, and I think it's quite good, but where we stand out is we really believe in workflows being highly visual. And so what what that means is we took the time to think through what are the big areas that lawyers spend a lot of time in uh time I'm working in. And so if, for example, legal research or or DockerView or similar, and then we try to understand what they do generally today as far as high-level milestone steps. And we try to kind of recreate that same approach with AI in a highly visual, multi-step kind of way. Uh, and we keep the lawyer in the loop. So everything feels like uh like older software where it's traditional and you can point and click, it's easy to use, it's sort of just like Windows and just really smooth and um and just accessible um for lawyers. Um, at the same time, it's very powerful. And so what we've done is because we we have these workflows where we know on each step what the output is going to be like. So, for example, we're going to produce a list of legal authorities. Um we know the different algorithms we need to run on the back end to do it very well. And so instead of a generalist AI that just says, okay, uh, I don't know which one of a million directions uh I'm gonna be sent. And so I'm just gonna be ready to do anything in a kind of simplistic way. We have a very specific answer that we need to give for every step that we're going through in our workflows. And because of that, we can run highly curated um agent swarms where we have many agents running in parallel to give an answer. We can run algorithms to say, here's the way that we can eliminate hallucinations by um removing um any anything that would be a hallucination before the user is able to see it. Uh, here's how we can reference the data that we have. Um, for example, we have a full 11 million uh case database that covers all precedential US cases. We we leverage that very heavily whenever we do research or similar. Um and so by having that that view on what each step needs to end with as an output, we can get a lot more uh out of the uh AI capabilities when we engineer on top of them as well.

The AI Appellate Judge Simulator

SPEAKER_01 11:06

Yeah, having an appreciation for the workflows in any uh subject matter domain, I think, is crucial because you're not expecting the lawyer to sit there in front of a chat bot box and just uh uh wheel away at uh asking questions and expecting uh replies. I think if you have a structured user interface that r respects uh tried and true workflows that litigators or corporate lawyers or tax lawyers, estate planning lawyers are are expecting, you're well ahead of the game. And and you're gonna help them focus and appreciate the software a whole lot better than if they were just uh uh looking at a a chatbot interface. So uh, you know, I applaud that. That that's that's a big help. Um I want to double-click on that uh uh appellate judge thing. That that must have taken a bit of uh fine-tuning and and uh uh post-training to be able to take what must have been the backbone financial uh foundation labs uh uh offering and and enable you to to build that uh appellate judge interrogator. Uh how long did that take to build? And uh it must be quite a uh quite well received.

SPEAKER_03 12:22

We really enjoyed building it. It did take quite a while. Um it's impressive what it's able to do. Um sometimes it's uh fairly hard to demo because if if you don't take the time to really read up on the case that you're working on, you get stumped very quickly by the judge. And so uh it becomes uh something that we have to be always trained on on the background of our make-believe cases that we use. Um but but yeah, it's uh the the way it works is what we start by saying go ahead and upload any kind of case materials from you or the other side that you have available. And then we spend some time doing our having basically the judge do its own research uh and learn about the case. And then it comes prepared with a bunch of different kind of background information it can draw on. Um and then as you're going through the case, the judge will stop you and kind of say, uh, please please expand upon an XYZ. And it has the case law research um in front of in front of it to where it knows the types of questions it's going to ask and it can react in real time and then then keep working with your answers and so forth. It's um it's a surprisingly impressive um end product. Uh, and we we were just shocked by how capable it was.

SPEAKER_01 13:34

And and are you selling uh to uh all sorts of firms? Are you focusing on big law or medium, small, solo, uh all of the above, any one of the above in particular?

SPEAKER_03 13:48

We're happy to serve all litigators. Uh we've focused on small law um to start, and we will continue always serving small law that uh we think there is an unmet need in that market, and we want to help them out. Um we want to be able to let them compete uh compete with the big players and be well armed. At the same time, uh we we have been going up market more and more, um, and you you'll see more of that in um on our page soon. Um but we we do believe we have a great offering for larger law firms, and we're happy to partner with them as well. But um definitely small law is gonna continue to be our foundation.

SPEAKER_01 14:24

Very good. And and um let's see. Um how about the uh the citation checking? And uh you already mentioned that you have uh quite a large repository of case law. So you're you're you're you're you well let's let's not go in the order in which I raise the the points. Let's start with the repository. That's your own uh homegrown or home-owned or licensed uh repository of case law. So you you're you're you're offering that up to the user uh rather than just uh uh you know touching base with LexisNexis or or TR or Vlex.

SPEAKER_03 15:04

That's right. Yeah, yeah. So uh as far as we know, there are now four serious providers of case law um for US cases. So the V Lex, now CLIO, um the West Law and Lexus, and then we're the fourth. Um we've spent uh a large deal of time ever since inception curating a case database, and other groups will have access to the raw opinions. And so we partnered with different groups to get access to cover all US cases that are uh presidential for the raw opinions. But then there's a lot more that you need to do to be able to get good use out of your AI. So for every case we have, what was the summary of the case? What were the holdings? Uh who were the parties involved, what were the material facts, about 20 or so pieces of metadata for each of those cases. We actually publish a lot of that online. And so what we want law to be accessible to everybody. And so you if you search for a certain case with a blue book citation and then had strong suit at the end of it, you should be able to see, um, assuming our SEO is doing its job, you should be able to find uh find that in our um free accessible online case database. But then the key is that we bring that into um whenever we do research, what we bring it into the AI. And so because the AI has the metadata, it's much, much easier for it to kind of find the right cases uh semantically. And so what we use what's called retrieval augmented generation, sure, where uh the the AI is able to kind of build this like index of all the cases in a in a way that it can actually search through well and then find the right case for everything that we're trying to prove. And then we have a few other algorithms that uh supplant that as well.

SPEAKER_01 16:44

And citation, is that uh you know, double checking sites and and the like? Is that part of your package?

SPEAKER_03 16:50

Or yes, yes. What we we do uh similar to Lexus has shepherdizing, it's trademarked. What we're we're calling ours strong site. Um we're able to do good law analysis, uh, just like like how Lexus does it, um, and to where you can ensure that the cases you're citing um are on good authority, they haven't been overruled or similar. Um and and and and so that is a core part of our offering as well.

Foundation Models Versus Legal Tech Vendors

SPEAKER_01 17:13

Right. Very good. Very good. Sounds like a soups and nuts solution or set of solutions. Uh that doesn't make me want to go back to practice law and become a litigator. But I I I damn well know that if I were practicing litigation, I'd want to have a tool like this. Um we'll get back to Strongsuit and how people can get in touch with you and learn more at the end of the podcast. But I I want to turn to some bigger issues that I I think I've laid out for for Jason to consider, Justin, sorry, to consider. Um one of them is in a lot of talk recently, as I said at the outside of the podcast, about what's going on with the foundation models. Um where do you think, as far as our beloved Legal Tech Vertical uh goes, where do you think we're gonna see the most impactful uh improvements and feature building from the uh the legal tech solutions providers who specialize in in our vertical? Or as uh as uh Claude uh took a toe dip in with its uh you know its plug-in uh for legal as part of co-work, we're gonna see the foundation providers, or they're gonna work in harmony with what I expect are general improvements from the foundation labs and more tailored, uh precise improvements from the uh the legal tech solutions providers such as Strongser.

SPEAKER_03 18:41

Yeah, I think this is such an uh interesting area now. And in M Sharia, there's a lot of very interesting perspectives. I'll I'll give you mine on it. Um so one is I think these labs are just phenomenal, what what the the people there are doing, that they have just tremendous intelligence, uh, and and it's just very exciting um to see the progress they're making. They're gonna continue to make progress. That this is gonna be a very interesting year when a tremendous amount of um the data centers become uh come online and we have just a lot more compute that unlocks a lot of capabilities. Um, I think they're gonna start, we're gonna start seeing early versions of continuous learning, which is one of the big issues that uh that models have right now that stop. There's still a lot more. Humans are very intelligent in ways we didn't really even understand that well a few years ago. And there's a lot more that we're gonna have to unlock before we get to where um it's what what the labs consider human-level intelligence. I think it's gonna take a decade. But the uh the there's a lot on the um legal tech side where we can add value as well, and I think a lot of value. What I've seen um uh in the market is there's usually two types of providers. So the more common type is kind of your more traditional legal tech player where they're saying, we're gonna take what the lab has. That's kind of the intelligence of what we're getting, and then we're going to layer in integrations and we're gonna layer in some nicer UI for law. We're going to layer in single sign-on and and we're gonna have have just all the kind of uh corporate uh expectations met uh on top of these AI models. But the other type that we've aspired to be is a company that really pushes from an engineering perspective, a lot of value out on top of the models. And and there's risk to this. So what what uh one of the terms people use is you don't want to become um bitter lessened. You don't want it to be that your your very specialized solution becomes kind of taken over or kind of worthless because the general solution does it better. Um, but what we think that we we're building in a way that that doesn't uh that makes that very unlikely. Um, what we we think that there's a lot more to unpack versus what the models have natively, where I think if you use the models just by themselves and say, I want to do my job for the day and basically uh as the or with the model and I want it to be be able to have the same quality output that I have, I think it's still pretty far from being something that you're gonna be happy with. What we try to do is we say, we do need you in the loop. You're gonna need to stay in the loop and be part of the process and everything you're doing. Uh, but we think we can add a lot more domain-specific value than the models themselves by true unique domain-specific engineering that adds a lot more intelligence beyond what the models have. And so that that's what what we've worked on. Um, and I do think that that's going to separate the best companies um from those that are adding on the kind of more generic corporate tease type of stuff in the next couple of years.

VC Heat And The Market Outlook

SPEAKER_01 21:46

No, I I get it. I get it. And I can't help but believe that uh no matter how superbly intelligent a model may become, even achieving human level intelligence uh Or beyond. And maybe it's the beyond that will undercut my thinking here. But there are those even among the smartest people on the planet who there are those who specialize, who acquire domain knowledge, who learn the workflows, who pattern match because they've built into their brains or into their algorithms or into the post-training rag they bring a whole set of things made explicit, if it's rule-based, or even picked up pattern matching-wise somewhat implicitly, that they know about the domain because they've lived it. And I don't think the foundation models are going to train for that. And I don't think they're going to want to sacrifice research dollars to uh to build for that. So I think you're right. I think there's a long way to go for legal tech vendors to add uh domain-specific features that will uh continue to drive purchasers in legal to look to them for solutions. Um I know uh people like you are saying from my lips to God's ears, but I don't I don't think it's a big I don't think it's a big stretch. Um but I do agree with you. I think that the whether it's continuous learning or adding uh you know symbolic AI as some sort of structural base to the to the machine learning uh techniques that that LLMs use, I think you're gonna see the models continue to research and build things that perhaps today are even unimaginable and eventually end up doing so assisted by the models that they were they they're improving. Uh so yeah, it's gonna be fun. Um things have been uh hot in investing in legal tech for quite some time now. Uh everything happens so quickly, so a year seems like uh uh like a decade's worth of uh things that might have happened ten years ago, and it took a decade then. Do you see the uh hotness, the demand, VC demand for investing continuing? You know, at the beginning of the year, and I keep track of this stuff by virtue of the post that I do, it was running hot. It's quieted down a little bit, although I think there was a rush at the end of the year that made it appear as if things were even hotter than they otherwise would have been because we were at year end. Do you see things continuing to be hot?

SPEAKER_03 24:32

Yeah, great, great question. Uh I I've done some research on this. Uh I'm lucky my my cousin is an uh is a VC uh and uh VC attorney and emerging market attorney at one of the big uh law firms. And uh we've talked about uh about this question a bit. Uh so I got his perspective that it ahead of their write-up of what they um what they expect to uh as far as like like the Q4 uh total deal volume, his perspective, he's a partner. He said he's pretty sure this is going to be the hottest year on record anytime the last several years, um, thanks to a very strong Q4 in 2025. And generally he sees that 2026 so far has been off to a good start. Um when I read just his background in general about the VC community, um, we see it as a pretty good environment right now for 2026. And so then moving on to the legal specific area. I mean, the way I see it, you you have a market that's a trillion dollar total adjustable market. Uh, I don't know what um how much of that legal uh tech is going to add, but it's going to be significant. And I think that half the value or more is going to be derived from legal tech solutions. And I think it's going to just grow the market. I don't think it's going to be a splitting of the pie. I think it's going to be Jevon's paradox where because you have um a more accessible uh price point, because work is more affordable, you get more work done. And so I see that trillion dollar market growing in legal tech being a sizable portion of it. Um Harvey right now is I think somewhere in the range of a couple hundred million. Um and I would expect this to be in the tens or hundreds of billions of dollars of total market size uh in the next say 10 years or so. And so I would expect very rapid growth, and and I think the valuations will will support that. Um and so I would expect it to continue to be pretty hot.

Startup Advice Focus And Faster Engineering

SPEAKER_01 26:30

Yeah, and there are things that uh uh again, driven by AI, uh that I I think are are hardly touched. I mean, training for for young lawyers, uh the ability to use voice uh-driven applications, the ability to uh to deal with uh uh you know with the proper permissions, to allow the AI, and we will get the permissions and the security uh uh battened down, to allow the AI to read and listen to the goings-on in negotiations and in courtrooms, uh, and then be able to detect patterns that humans probably couldn't detect, even with uh 40, 50 years worth of experience, reveal those patterns and improve the way people practice law, be it negotiating corporate deals or or litigating, arguing before courts, uh conducting depositions and and the like. Uh I I think uh virtual reality, as silly as it may sound, is going to play an even uh bigger role in in our hoary old, old-fashioned, backwards-looking profession that uh we can't even imagine now. So I think the the the uh as people grow more comfortable with and experiment more with at the practice level, uh they're gonna see problems that they never saw uh or thought of as being addressable by some form of automation or software, uh be it good old-fashioned AI, be it rules-driven engineering uh uh uh driven question and answer uh AI or the gen AI that that uh you know in different modalities that we've come to know and begin to love. So I I I think we're we're still uh at early innings, very early innings here.

SPEAKER_03 28:26

I completely agree with you. And uh it's always tough, but as the uh CEO of a legal tech provider, there is so much we want to build, and you can only focus on on so much while doing it well. Um but I think this is going to be just a very, very exciting time for the industry. Um and it I think that lawyers uh some are probably a little bit kind of feel leery about AI. I think once you get the hang of it, this stuff is a lot easier to use than than the machine learning type of stuff that analytics folks had to learn before. Once you get the hang of this stuff, it's really fun. Um, and I think you'll find just really exciting ways to use uh AI that you everyone can be creative uh in their area and just have a lot of fun with reinventing their work uh that I think they'll really get enjoyment out of. The the piece that uh that we always give advice on is find ways for AI to synthesize large amounts of data and and then for you to drive the strategy and drive um drive the thinking in direction. Um what I think that combination works very well. And it aligns to what you mentioned about the AI might pick up signals from the data that because it's crunching so much, would be very hard for a human to see. Um, but if you can leverage that and then use that to inform your um your decision on how to proceed with the case, I think that can be very powerful.

SPEAKER_01 29:49

Yeah, I want to be able, if I were practicing, I I in 10 years would want to be able to walk in to the AI and say, all right, this is the sort of deal I'm doing. These are the regulatory concerns, these are the size of the parties. This is the industry in which the parties are involved. I want you to go back and want you to review all the uh oral and video that you've seen that has been captured in my firm of hundreds and maybe thousands of lawyers on matters of this kind. Tell me what, after you've viewed all that and listened to all that, tell me what worked in in the negotiation, tell me where the buyer side or the sell side tripped up, tell me what got people to agreement more quickly, tell me how not to waste my time on matters that others have, it turned out, wasted their time on, and then have the AI report to me and improve my negotiating skills. And I know my my opposing number is gonna be doing that too, so we'll come we'll come to yes more quickly, hopefully.

SPEAKER_03 30:45

That's absolutely the goal, and and I I think that's a great way to defend it.

SPEAKER_01 30:49

And and tell me, be and we're gonna be wrapping up soon, jealous of my listeners' time. We keep it down to about a half an hour or so. And I I think we uh we've covered a lot of ground. Thank you so much for your insights. But if you you've been doing this for three years, you've you've been an in-house counsel, you know a little bit about business. If you could look back on what you've done and what you've learned, and things you may have learned the hard way, successes that you've had and that you're delighted, of course, to see repeated. What sort of business advice uh could would you care to impart to your fellow legal tech startup leaders?

SPEAKER_03 31:24

Well, one piece for for us has been focus really helps, although it's very hard and legal. And so what we we've been on both sides of that, but I I think I would still start with focus. So uh early on, we we said we don't know which way the market is going to go, whether they're gonna want transactional or litigation or a specific uh practice area of being particularly good, particularly strong with AI or similar. And so we said we're gonna build a few different pieces and that kind of make sense together and and try all of it and then double down where it makes sense. That that was probably not the right answer. We we probably should have picked something that was was uh a good thesis and and really gone after that um in a focused way. It just it's was very stressful and very time consuming and kind of used a lot of money to build a lot at once. Um for us now looking back, what we're in actually a good position that we did, um, law, law is very connected. And and I think uh Yudia put out a great article on this about why it's so helpful to have a full platform. Um, because when you do um when you're doing research, it depends so much on the facts. And when when you draft a document uh for research or for litigation, the facts matter so much. So does the research. And so trying to segregate is very hard. At the same time, um, as a startup, it probably would have made a lot more sense to really start with a very focused uh customer first and and build up from a very specific set of solutions for that specific practice area. Uh if I had to do it again, I probably would. Um at the same time, I uh we're in um not a bad place now, um, having not done that. Um so that that part's been interesting. And and then um I think the other uh piece of business advice, uh something that um we I think we did get right is uh I think really, really taking the time to uh have high expectations and and push the engineering team to use AI and to um build in a very fast way with very limited bureaucracy in a way that kind of reinvents the way that engineers build is just very possible right now. And um we we track our stats as far as um how fast we're our engineering team is outputting quality code. And um by by just going working with how can we make uh the process more efficient and better every day, that that has worked very well for us um through that kind of continuous improvement lens.

SPEAKER_01 33:57

Yeah, what time does not permit us? We could do a whole podcast and more about how engineers that working in a startup such as yours are leveraging uh, you know, Claude Code, uh Codex uh uh and and uh and the like to change the way in which they uh they build the things they build. That that's for another time. Um but I think the way engineers building software are going to learn to re-architect their thinking. I wouldn't be surprised if senior lawyers and mid-level lawyers are going to learn to re-architect their thinking, uh, and I think this has been talked about quite a bit, certainly not original to me, to bring almost a um an architect's view of how to get the AI to think for you and and do for you uh a lot of the work that you might otherwise have thought you had to do yourself. But again, that's that's for another day. We'll have a panel, you'll be on it, and we'll talk about it maybe at some conference in a lovely part of the state where, or country I should say, when it's very cold elsewhere. Uh you're where? Where where are you located?

SPEAKER_03 35:01

Uh I'm in Dallas, and then we have offices in Dallas, Public City, and San Francisco, and so I'm between those three a lot.

Where To Find Strongsuit And Closing

SPEAKER_01 35:08

Yeah, I don't know what the weather's been like uh for you guys in in Dallas recently. Here it's been kind of awful by our by our standards, but I saw people not too cold at the Super Bowl yesterday, so I guess I wish I were in uh in uh Santa Clara, Silicon Valley. Um, Justin, thank you so much. If people want to learn more about Strongsuit and you and reach out, get in touch, what's the best way to do so?

SPEAKER_03 35:32

Yeah, and thanks a lot for the time. Uh it's strongsuit.com. And you can reach out to me on LinkedIn, Justin McCallan, or by email Justin at strongsuit.com.

SPEAKER_01 35:42

Very good. Uh as I've said to so many, and I enjoy saying it, and I mean it in your case, certainly we've seen one another on videos back and forth. Uh let's get together face to face uh soon. And um uh I can't wait to hoist a beer with you.

SPEAKER_03 35:58

Well love that. Uh thanks again.

SPEAKER_01 36:01

Okay, thank you. Thank you for listening to the Legal Tech Startup Focus Podcast. If you're interested in legal tech startups and enjoyed this podcast, please consider joining the free legal tech startup focused community by going to www.legaltech startup focused.com and signing up. Again, thanks.