Episode Transcript
Jon Prial: [00:00:00] The material and information presented in this podcast is for discussion and general informational purposes only, and is not intended to be and should not be construed as legal, business, tax, investment, or other professional advice. The material and information does not constitute a recommendation, offer solicitation or invitation for the sale of any securities, financial instruments, investments, or other services, including any securities of any investment fund or other entity managed or advised directly or indirectly by Georgian or any of its affiliates. The views and opinions expressed by any guests are their own views and do not reflect the opinions of Georgian.
When I think about legal tech software, I think about value add around discovery or document management. But with the explosion of AI, new opportunities are emerging.
Today, we're gonna share a story about how technology can help lawyers help more people, and you'll hear a word that might surprise you too: [00:01:00] justice. We'll be talking with the founders of one of Georgian's investments, a fascinating company with an absolutely wonderful name for a company in this space, Darrow. But it's not the name that matters today. It's about an idea and the coming together of a vision.
I'm Jon Prail and welcome to Georgian's Impact Podcast.
With me today are the co-founders of Darrow. CEO, Evyatar Ben Artzi and CTO Gila Hayat. Evya and Gila, so glad to have you here today. Welcome!
Gila, tell me a bit about Darrow and what it does.
Gila Hayat: So, Darrow identifies and scans the web in order to identify where in companies maybe, break the law. So basically we scan through tremendous amounts of information to identify whether there maybe have been a violation against large groups of people.
And, the way we do that is we built a machine that is managed by [00:02:00] humans, driven by AI that allows us to identify stories and bring them to light using big data and while looking at previous cases, looking at all sorts of open information to really build the story brick by brick.
Jon Prial: I like the way you described this, but I want to add just a bit more detail in terms of what you call the stories or what I might call legal issues. What you find these potential violations against large groups of people, may turn into class action lawsuits,
Identifying stories and bringing them to light, which means it all comes down to litigation and ensuring that legal teams are in a position to help
Evya, I'd like to ask you to comment on your vision for Darrow.
Evyatar Ben Artzi: Yeah, so I think we started the podcast with kind of a premier on legal, and I guess
The phrase that we use a lot is, "You could do the future justice," Right? So that goes both ways, both by saying that the future needs to be [00:03:00] just and there are things that we can do in the present to change that. And, that the current time right now, if you do the future justice, you're already there.
So, it changes As we go along and I think we are seeing that at Darrow.
Jon Prial: Well, that's a big vision, Evya .
Gila, your opening helped us understand how we can get there. To support a class action lawsuit, lots of data is required to both find and then clearly lay out these potential injustices. How does it work? Where do you look as you scour the internet, and how do you know when you found something?
Gila Hayat: There's a lot of power in open data, we view things that are available online as a commodity. While we often ignore their potential in showing and identifying things with public value. So the way we, we look at things is we're looking at the balance between what we call legal data – what the courts have to say, what regulators have to say about the norms or about cases that have social value that [00:04:00] should be discussed – and while looking at those, identifying those norms, those cases or stories that we think should be told and should see the light. So we're trying to find those clues. Trying to find those clues that while compiling them, there's a compelling story behind them and the facts that can support the fact that there has been a claim here or that has been the wrongdoing.
So, we're looking at many different data sets that includes both tabular data; it could be geographic, could be any sort of sensor that we're looking into that is publicly available for us, and seeing how those points correlates together.
Jon Prial: Now I see that as an interesting combination: legal data, regulations, and social cues.
Evyatar Ben Artzi: I just wanna add that what you kind of describe is that network, right?
That we're always talking. Like, people create a lot of data, and some people create open data that anyone can access. And, most of the time, that data is [00:05:00] used by actors that want to sell something to people.
There's a mechanism that was created in the common law countries where you identify a class of individuals that were harmed by one actor's actions, and it could be a few actors, it could be one actor. When you identify that, then you basically find out that there's a wrong that happened. And that wrong has value. Now redistributing that value back to the individuals that were harmed is a class action. It's a procedure, a legal procedure. It starts by identification, and then you file your identification to a court of law. And, the next stage is that a court of law reviews the filing and decides whether to really redistribute the value between the defendant and the claimant. And if that happens, then you have redistributive justice.
Jon Prial: Wow.
Building a class can't be easy, especially when it might be a small sample around something As a potential [00:06:00] environmental impact. Gila, how are you addressing finding the people? Is this your new offering? PlaintiffLink?
Gila Hayat: PlaintiffLink is our recent, uh, advancement where we understood that aside from finding the case, we're also excelling at finding who are the people that are most likely to be harmed.
As Evya said before, we talked about the concept of class actions. So, in order to find the person that has been subjected to some sort of corporate violation, we've created a system that allows us to find them and move along with a case.
The way we do it, it's heavily tech-infused in the way we identify them, and that allows us to be able to do it at scale
Jon Prial: Look, we know that some data is immutable, whether it's actual sensor data or prior legal art. However, some data could just be people commenting on stuff and saying, within this case, you know, their environment. How do you think about potential [00:07:00] bias in that data?
Gila Hayat: So, bias is a part of building trust in data. When we're looking at about addressing biases is saying, "This data set or collection or this insight is based off of partial information." This core issue around working with intelligence, understanding that you're telling a story, that there must be more facts out there that can tell a completely different story.
And I want to take it from the ability to build trust in biased systems or reducing it, or even being acquainted with the risks and the ability to mitigate those. The way we tackle that issue while building trust in identifying those stories is by supporting it with vast amount of alternative data sets. If we're looking at the classic data sources that are available for law firms as we speak, is mostly legal data and maybe some sort of business directories or information around those.
So, our sensors and our data [00:08:00] network is much more vast than that, and by knowing that, that allows us to cross-reference, identify what other facts could be supportive or contrary to the story and the ability to obtain those. That is the core advantage that we have an intel when working on a legal case, the ability to obtain more data and reducing that risk of bias or even mistrusting is a larger category of, uh, that that bias, uh, indicates of, and being able to understand how we got to here to tell the story backed by facts.
Jon Prial: Have large language models helped and both in terms of you understanding the data and then in terms of what you might offer to your clients, the law offices you work with?
Gila Hayat: Oh, absolutely. I think it did, uh, a lot of good things, both in the technological advancements inside the company, but also I think the most, uh, interesting thing that it has done to our relationships with our clients is now every lawyer knows what GenAI is. Which is a completely [00:09:00] different starting point for us as we started Darrow, where technology is perceived as, uh, something mysterious while everybody's, at least once they've tried to use ChatGPT and their experience varied of course.
I think it's a great talking point because it tells beautifully the story of how early or later adopters of technology or tech-averse people, so use Google search at first well, where you would write a well put request rather than the query. So, now professionals are moving from writing queries to talking with natural language, again, which lawyers excel at.
And that is something that we really see coming back where using language, using rich language to express the. Your objective and how you want things to be done is something that lawyers are actually better than other people, which is a joy to watch and interact with because we've seen good questions in terms of working with GenAI, actually, we've been using generative models before ChatGPT exploded, but [00:10:00] this vast adoption allowed us to collaborate more and extract a lot more value and a lot more insight while interacting generative models.
Jon Prial: No doubt that it is all about the value. So, Evya, talk to me about what metrics are important to your customers – lawyers – when using GenAI solutions like Darrow.
Evyatar Ben Artzi: GenAI has helped with helping a lot of people become authors, right? As Gila said, like now you can basically do technology and create amazing things just with natural language so people become authors of their own stories.
And, the same goes for lawyers and law firms. Law firms have been measured in the past on revenue, but not in the value they really create, which is litigation value. Right? So we measure ourselves at Darrow with an impact metric, a core impact metric, which is called 'GLV' – gross litigation value.
Gila Hayat: It's more optimizing.
Evyatar Ben Artzi: Exactly. So we're helping law firms optimize their balance sheet basically [00:11:00] and grow their gross litigation value. And how do they do that? By finding the right data to support the cases They already understand how the world works and they can see things and patterns that are patterns of wrong if you're a law firm on the plaintiff side, or a pattern of defense if you're on the other side.
But once you see that pattern, adding data from the real world that supports it, builds a case and if that case is strong enough, it will, uh, succeed in court. And that's what we help lawyers do. We help them in court with the cases that they can bring, and that requires our network of open data sets and humans that create the trust.
Jon Prial: At the end of the day, it all does come together from the data to the plaintiffs, to the lawyers, all with a straightforward metric. Am I missing other players in this ecosystem?
Evyatar Ben Artzi: Yeah. So, we talked about the move from victim to author. Now let's talk about the move from tax or risk planning to GLV planning. Right? Firms all over the world have kind of [00:12:00] uncovered that. Litigation is also an asset or a liability depending on where you stand in the balance sheet. There's a whole industry called Litigation Finance. What Litigation Finance does is once they see someone uncovered a litigation asset, they want in and they wanna fund that asset. So, the asset takes time to mature and come to fruition, and Litigation Finance does that.
So, I think the idea is that litigation funding is open to everyone. What corporations can take litigation finance to reduce liabilities from their balance sheet because they think they have a good case and law firms do it as well in law firm loans, and this has been around from like hundreds of years, right?
Benham talked about it in a letter to Adam Smith. Right. Litigation Finance is kind of embedded in our culture, but it's not talked about a lot. So, what we wanna do in Darrow as part of bringing justice to light is kind of show this to the world, see cases are being funded because they are a venture for making the world better.[00:13:00]
And, that is kind of the idea – correcting a wrong sometimes needs resources. Lawyers need resources to work, and plaintiffs need resources to take care of themselves until they get a paycheck mandated by a court, and defendants need working capital.
Jon Prial: People always make risk-reward trade-offs. So do lawyers and all the parts of this interesting ecosystem we've been discussing. Do you think this metric captures that?
Gila Hayat: So when we're talking about GLV, we are establishing a metric that indicates a value that could be talked about. So when we speak about optimizing, we're looking at also companies as major contributors in that metric.
Because litigation is only about slapping the biggest case in court and whatever the story is, just try to, uh, to be predatory about it. There's a lot of litigation that has zero value and it is just happening and it harms businesses in a way that shouldn't be even initiated. So the concept around GLV [00:14:00] has much deeper level of adjusting the value of the claims into something that can be optimized, planned for and negotiated in a data-driven way,
Well, litigation is not just like a piece of a creative text that has some allegation in it. It indicates of value that has been taken or deprived, or needs to be negotiated over, and the ability to inject predictability or data into that process allows it to become a much more neutral transaction.
And once it becomes a transaction, a lot more people can understand it and talk about it. So people are being suspicious about lawyers and that space. And for me, as a techie, being able to rationalize it or even understand it, that is the core issue or even the core value around GLV.
Evyatar Ben Artzi: And that moves. I think the person from victim to author, right? It's not just about the people who were harmed from the legal violation, who move from victim to author once. They can say, "Hey, it's cloudy today [00:15:00] more than than usual. Maybe this is something to do with climate change." Right? And that helps us with data.
It's also about moving the law firms from being reactive to cases that come to them, to proactive and finding the cases that they want to work on, the impact that they want to create in the world. And, it's also moving the corporations, the defendants usually, from being a passive defendant that gets a case to being proactive about changing their gross litigation value balance sheet, really moving more of their liabilities into assets.
I think it's the class action part is, is the part we do today. So, we're always thinking like a few steps ahead and thinking about every legal case, right? Darrow is a network of open data sets that enables legal professionals to identify legal cases that were hardto identify before because that network of data sets is curated by people who are working for people that company. What it really does is [00:16:00] help legal professional identify cases, and those cases can become class actions that benefit the public at the end, lawyers make money from them as well. And this doesn't only have to be class actions. Darrow has a lot of different types of data that it could enrich and nurture different types of litigation that provide value to people.
Jon Prial: No doubt that you too have your eyes on the prize and you've clearly built your company around that. Tell me, how does what you do at Darrow, your purpose-led vision translate to your corporate culture?
Evyatar Ben Artzi: We wanted to build a human-centric culture where humans are kind of the beating heart of a machine that works for them, with them. And, that concept made it so that it is supposed to be around the people, centered at the people at Darrow.
So, what it is it that they author is the company's culture, is what really became the culture at the end. All we did was prompt with humans are always at the center of this. And, now, [00:17:00] Darrowers create this culture and if you join Darrow, either as an employee or a client, or a supplier or a vendor– or doing this podcast with you, now, Jon – we're in sort of a story partnership and you decide what that culture is.
Gila Hayat: I think Darrow's culture is to put technology aside, and I think it's the best thing that we've built at Darrow so far. But, the core thing about being socially impactful and make it work as tech and the ability to attract hyper-talented people that want to make a dent in reality and really make it come to fruition.
So, we talk about technology a lot but we also talk about humans a lot because things are changing. We talked about trust before around technologies moving in a way that we barely can catch up. And for me, personally, the culture that we're nurturing here is looking at the core human things that we want to change or the core things that we want to change around society and make it our [00:18:00] daily mission.
Jon Prial: I want to thank the two of you for spending time with me today. Pulling together the vision that you have, translating that into your company's products and culture has really helped me see how your company name, Darrow, is so much more than a name. I think Clarence would be proud. For Georgian's Impact Podcast, I'm Jon Prial.
