Let’s talk about production and winning cases. It’s not only about acquiring new clients, but also acquiring new clients that can deliver results. A potential breakthrough using Artificial Intelligence, AI.
How do we frame, understand, and implement the best of AI’s capabilities? Lawyers need to tread lightly, even as AI is becoming increasingly important in the field of law. Data is often “dirty,” unstructured, not built to serve. A properly “trained” AI program organizes these fields into useful information.
Guests Evyatar Ben Artzi and Gila Hayat are co-founders of Darrow.AI. Darrow’s AI-powered Justice Intelligence Platform scans real world data to detect harmful events, determine the number of potential victims, predict the legislative outcome, and assess the financial value of a case to help firms locate and organize potentially important cases. No more chasing your tail or casting the widest net possible.
Across the law, from financial misconduct to environmental protection to pharmaceuticals, there is data out there that creates patterns if you know where and how to look. Properly managing raw data may allow firms to find new clients and tell better stories more efficiently. Producing results starts before a case even walks through the door.
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Male 1: Marketing, time management, attracting clients and all the things besides the cases that you need to do that aren’t billable. Welcome to this edition of The Un-Billable Hour. The law practice advisory podcast. This is where you’ll get the information you need from expert guests and host Christopher Anderson here on Legal Talk Network.
Christopher Anderson: Welcome to Un-Billable Hour. I am your host, Christopher Anderson and today’s episode of the three kinds of episodes that we do is about production. I’m really excited because this is an area that we haven’t really talked about, but it’s everybody else is talking about it so it’s about time and that is about artificial intelligence, about AI and about its application to law firms. So, while I said it’s about production, it also might end up being a bit about marketing, a little bit about acquiring clients so what we’ll see, we’ll see how this shakes out.
But as you know, the main triangle of what it is a law firm business must do includes two of those things, acquiring new clients, which we call acquisition, producing the results that you promised, production, and achieving the business and professional results for the owners, also known as why the hell we do this? And in the center of the triangle, of course, is you driving the results, driving the goals and today’s episode, we are going to discuss AI and what it means for law firms in production, in marketing and in their future and my guest today, I’m really honored to have with me Gila Hayat and Evyatar Ben Artzi, they are with Darrow.AI, both senior execs.
Evya is the founder, Gila is the CT co-founder and CTO, and we’re really honored to have them on the show and so we’re going to call today’s show, “Is it really artificial?” because there’s a lot of intelligence about this AI stuff and it could be really serve that intelligence function for your law firms. In other words helping you to discover, helping you to learn, helping you to find not just clients but subject matter that you can help resolve for the world so without further ado quick introductions.
Gila first of all, she is co-founder and CTO of Darrow. Prior to founding Darrow, let me just, this is really impressive stuff, that she spent seven years as a senior software engineer and team leader in the Israeli Defense Forces, which we will refer to as IDF so we sound cool. The intelligence unit and during her service she worked on a special highly classified, am I supposed to be talking about this, projects focused on ethical uses of AI, both for military and police with the goal of resolving core security issues.
She and her team were awarded presidential honors that recognized units displaying extraordinary performance and she was also chosen for the Forbes 30 under 30 list as one of the youngest minds in the industry to hold that kind of leadership position in a fast growing technology company so well, all right and Evya is a co-founder and CEO of Darrow also prior to founding Darrow himself he was at the high court of justice in the state attorney’s office, where his focus was on administrative law and public policy litigation and he served as a law clerk for Justice U. Vogelman at the Supreme Court of Israel and for Judges Goren and Cheston at the Center of Arbitration and Dispute Resolution.
He’s an LLB, BA in law cognitive science from Hebrew University of Jerusalem and he’s put together his studies of legal, psychology, linguistics, philosophy, computer science, AI, neuroscience and anthropology. You know what? I’m just going to let him lead the show because that’s amazing. I mean, he co-founded an educational non-profit called Yahav, which promotes social change in Israel. Evya is a captain on reserve in the IDF also where he currently serves as a company commander following service prior as a combat soldier and commander so holy-moly I’m usually bad at these intros but let’s face it, they wrote them, Gila and Evya, welcome to the show.
Evyatar Ben Artzi: Hey!
Gila Hayat: Hey! I mean, we were read them and — that is 99% of the impact.
Christopher Anderson: With my own special flare but yeah, no, that is impressive so seriously though, I mean, that is quite a varied background, but it like both of you there’s your background of what you’ve done has you know really helped I think to frame up an ability to not only understand but implement the best of AI, you know not some of the silly uses of it that some people are doing. I know you guys are probably familiar with it. The lawyer who just had AI write it brief and it made up cases and submitted them like you know, these are the stories that people are hearing so let’s talk about the serious uses that we can put the stuff that too that could really help lawyers and law firms achieve more success for their further business but before we do that, let’s like what brought you to found Darrow, whichever one you want to talk about like why Darrow? What was the driving vision? What are you trying to accomplish?
Evyatar Ben Artzi: Well it was about four years ago and we were, I was a clerk at the Supreme Court with a law school friend and we recognized like the serious level of friction that this one has, right? You basically see a lot of cases that never really make it to court, just by doom scrolling on your phone at the end of the workday, right? You just go on Instagram or Twitter or whatever and you’re like, “holy”, there are so many legal violations in the world. Most of them don’t get to my desk every morning and as a clerk, that was frustrating because a lot of the work that I was doing was stuff that I didn’t find them taxable.
Most of the cases dismissed definitely in the Supreme Court level and it felt like well, what is my time used for? Am I being the best impact that I can for the world? Once we caught that and we learned that most legal violations never get detected people don’t even know that they’ve been harmed and this goes like across the board. Corporations don’t know about most of their legal assets so to speak and consumers don’t. Other people who haven’t done anything but got to get harmed are in the same position, they just don’t know and we shared like the problem with Gila and she kind of immediately recognized that what we were talking about was this fluffy macro-economic problem in the world about like an inefficient enforcement of the law or something like that. She was like, well, listen, this is an information gap.
Gila Hayat: Its intelligence problem I mean the fact that there is knowledge somewhere or intelligence that is scattered around is not something that is unique to the legal system just in general. There are a lot of opportunities and a lot of exposure in knowing what the other side or any side of that story holds so working in Intel understanding both in the macro level what is the story behind it, why the driving forces requires a lot of data and requires not only data but also a methodology to understand that, to tell the story at scale and gather that evidence so when we met and we started looking around this problem, we understood that it’s not about the legal profession, the ability to acquire knowledge and work around it and work around data in order to support those stories or those narratives. So, when we started working on this and then started working over this idea as a data professional for me what is required is the story behind it. Is the legal rationale that is the DNA of that story that we’re trying to convey because in the end it’s very difficult to understand what can you do with raw data because raw data doesn’t tell a story. It’s barely called information.
Christopher Anderson: Right and we’re talking about unstructured data as well? Like just like dated data from all sorts of sources that isn’t neatly in a database in a nicely categorized way, yeah?
Gila Hayat: Yeah, most of the data is dirty. It’s a problem that happens in every business domain, over every domain of our lives. The data is not built to service for a purpose, it just exists. Sometimes its side effect are a lot of actions so in order to make it coherent cohesive story there’s a lot of knowledge work to apply to that and to understand what you can derive from it in order to drive results
Christopher Anderson: Sure okay so let’s, help me connect the dots a little bit so you’ve described a problem maybe describe this problem of like that we’re spending a lot of time on less impactful things and yet we’re cognizant, were aware of more impactful things out in the world but they’re not making their way into legal system that’s sort of the problem that he’s framed up and now, Gila, you’ve talked about how somehow harnessing that the data that’s out there unstructured dirty you called it as it is, can be brought to discover these problems and or tell it straight. I think I love what you’ve said, you tell a story about it. So now let’s connect those two dots, how does that help lawyers? How does it help legal practices?
Gila Hayat: Well lawyers care about that deeply because they spend about 20% of their time like working hours on developing the business at least in litigation teams that means looking for cases and sometimes it could be creating the right marketing automation to get the right clients in the door but most of the time, it’s about hunting for those cases that make your practice.
Finding the business that makes your practice and that’s a lot of work that goes into something that isn’t legal work. It just isn’t it. It’s un-billable work that is basically creative. It’s about the developing your business in creative ways especially when it’s coming up with and understanding what kind of social phenomena are out there in the world, it really requires to harness your creative skills that is not perceive or quantified in a way, but lawyers are expected to do that. Also they expect that from themselves to bring the A-game both in creativity and building the right foundations toward the litigation that trying to create. The podcast is called Un-Billable Hour, right? We are trying to cut down on the un-billable hours that lawyers have because when you’re developing a new case, when you’re getting a new case, it doesn’t mean that you’re going to be able to bill that time and that’s the thing that we’re looking at as the business development work for law firms at least for litigation teams.
Christopher Anderson: Yeah so let’s do that so obviously business development, a lot of lawyers believe and wait for their clients to realize they have a problem or an opportunity and then bring it to their door and we spend a lot of energy, time and money basically putting up a giant neon sign that says, “you got a problem, I can help,” but what you guys are talking about is maybe saying, “hey you’ve got a problem and you might not know about it,” which you know in marketing we would refer to as being higher up the funnel and which means there’s a lot larger market to address with a lot of opportunity that might otherwise never even be recognized. Am I catching it right?
Gila Hayat: Perfectly, yeah.
Christopher Anderson: Okay so let’s talk about then who are you helping right now? Like what kind of law firms, what kind of lawyers, what kind of practices are amenable to this with you right now?
Evyatar Ben Artzi: So we mainly work with the plaintiff side litigators. Those are partners and associates that do litigation but in general, we work with the rain makers of every firm, those like top litigators that bring in the business. Once we find someone like that and then we can help them achieve better results in the production.
Christopher Anderson: Cool. Let’s take a break here for a second and talk about how, right? So like I want to talk about a couple of practice areas that are like how, what kind of discovery are you doing for them? What kind of business are you being able to bring to the door that they might not otherwise have? But before we get to that, we got to listen to little bit to the folks who make this show possible and we’ll do that right now.
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All right, we are back with Gila Hayat and Evyatar Ben Artzi. Gila and Evya of Darrow.AI and we’ve been talking just a little bit framing up kind of what it is that they do because it’s you know not for nothing it’s new and it’s a new way to approach business development. I think it’s pretty exciting. Who’s using this? Like what talk to me like, you don’t have to give names or specific cases but like what practice areas have been successful using these methodologies that you framed up as a way to get new business in the door?
Gila Hayat: So we’re talking mostly about those consumer areas that the plaintiff lawyers like, right? So anything from consumer protection to product liability, environmental issues, securities fraud like general financial misconduct and you know, like in all sorts of areas and of course anti-trust and pharmaceuticals all the way to employment and privacy. We basically wanted to touch everything.
The great thing about the quality that we built that allows us to understand those cases because we’re looking at patterns. So we’re looking at various types of cases that can be done that go through the same place. So we’re applying law on raw data that’s what we’re doing. This allows us to expand very, very quickly to new types of domains or legal domains and new types of cases.
Christopher Anderson: Sure. As long as there’s data out there like you could ask a question and then find out what I think you said the with the patterns are what does the story that can be told but based on the available data. Are these data sources tend to they tend to be publicly available data sources or do companies bring you their own data and you can sift through that like how what do you feed into the algorithms to give you an idea of what the story is starting to look like?
Gila Hayat: Well, I cannot disclose all types of data sources that we’re dealing with but because all sorts of it, it’s a part of our secret sauce but I can share that we’re standing on a lot of openly available databases, data sources, anything you could imagine. A lot of text as well so we’re looking at different publications, we’re looking at everything that is publicly available can be source code and a lot of dumps that you will never be able to put your hands on just using a browser but they are publicly available.
Evyatar Ben Artzi: Just sometimes it may not be publicly noticeable, but it’s available.
Gila Hayat: I mean there’s a funny anecdote that you can hide a body in Page 3 of Google search, well we’re going to the Deep Web and beyond in order to find those data sources and also the interesting part is about making combinations. So for example, maybe one publication one day the source might not be enough to hold the full story because if the story is told in one place then I mean the story is out there but the ability to craft and understand the narrative where it’s spread across different facts or different evidence that happen find their way somewhere then we’re finding them. So, we’re basically connecting both the legal dots and evidential dots in order to create a full cohesive story.
Christopher Anderson: Let’s take a minute to step back because I feel like there’s a lot of conversation around AI. People are using the words, people are familiar that you know with the big story of course, is your Chat GPT is out there, people are familiar with that. People I think at the same time have kind of skipped over understanding what is it. What is AI, what you know because Chat GPT is a language system, tt doesn’t really encompass everything that really is about what AI is. So, from your perspective, when we’re talking about AI your company is Darrow.AI, what is, how would you describe so the listeners can get a better handle on it what artificial intelligence is in this context?
Gila Hayat: Artificial intelligence in a nutshell is the ability or a system that has ingested huge amounts of human intelligence that has been curated for a long period of time. They might look like magic for some people and maybe like dark magic because it’s hard to explain how it works but in the core of things, it has been exposed to a lot of data that couldn’t be comprehended by a single human being just because it’s a lot and the ability to derive or predict any kind of question or but in the core of it, it’s statistics so the more data you put into it, the better results you will get.
Just like you would educate a child or teach a kid how to talk or how to speak a foreign language or to swim, the more good examples of good behavior they’re exposed to, then they will know what to do next and when they’re not sure or when they try to do something to your example over Chat GPT making up things, they’re very, very keen to give good results but sometimes don’t know how so they’re just working with the best they could.
What we feel about or maybe the initial reaction when interacting with language models that it’s dark magic, right? It says things that sound coherent but it’s only as good on the data that it’s worked on and from what we’ve seen with general AI models I believe you play with Chat GPT really talks about legal matters in a way that a civilian would but it doesn’t really get to the depth of the secrets of the trade or maybe the intuition around that to understand what the stories really is about or what kinds of facts might support it or what kind of narrative can tweak up in order to make it more understandable story.
And I think when we’re talking about AI in legal field sometimes when we look at this promise that is being made and there’s a lot of buzz around that in the legal texts fear or in general just in the legal world, when we’re looking at the missing component, the intuition, the ability to make real legal decision this is what we’ve been working on the past three years. Since we started there now in order to really craft the intuition that holds that and implement that and the intuition that has for a good litigator. That sense of I know how to argue that case, I know what make case.
Christopher Anderson: Right so let me, so this show also has dark magic. I can already hear the questions from the listeners from the future and so let me let me address a couple of them because what you said — talking about, and me sounds amazing and like, everybody should jump on it, right? There are going to be skeptical questions that which I think deserve to be asked and answered and so the first one is you know based on experience of things that we’ve heard about.
One, is I think you just mentioned it sort of like the need to answer the question even if there’s insufficient data, which leads BS and the problem of GIGO garbage-in-garbage-out, right? So what happens you know, you guys said you’re going deep in the web well one thing we all know is it’s like there’s probably I don’t know what the statistic would be, but I would get, I would venture to say substantially more misinformation or quasi misinformation than solid information out there. So, let’s deal with the first one first. How can if attorneys of law firms are going to use this to discover problems how do they make sure they’re not chasing a phantom problem when it’s just stoked by misinformation?
Evyatar Ben Artzi: I think, first of all, like the idea that there’s a lot of misinformation in the web is something that we all feel intuitively is right. Not because people have bad intentions just because sometimes there is misinformation. The capability of finding out and cross-referencing a source is not unique to humans. Humans do it the machines can also do it. First of all, I think anyone using generative AI should employ techniques for cross-referencing their sources making sure that the information they’re providing is the best available and just know like someone will be using the information online, someone will be using it.
There is true information out there. Anything that anyone des today, there’s a trace on life. It’s there and so deciding not to do anything about it because of the risk of doing something wrong that’s not played very well for the technological advancement of law. We have to do something so cross-referencing sources is important and if you get a case it seems odd. Out of Chat GPT for example, then you might want to cross-reference it and check whether that’s right and that doesn’t mean asking the model again, right?
It’s like that’s what we’ve seen it doesn’t work. So, I think first of all, the techniques of cross-referencing sources have to be advanced thoroughly and we’re doing that but there are a bunch of other companies that are doing this as well and like hats off to everyone who’s in this problem trying to figure out how to prevent hallucinations.
We see a lot of e-discovery companies that are doing this and another space where you really can’t get it wrong. The stakes are too high. Here in the type of knowledge work that we’re talking about, the legal work justice is at stake and sometimes people’s lives are at stake. You can’t in any way get this wrong, you can’t lose in it so using AI without the proper brace that doesn’t work. You have to have checks and balances in the model in your models and in the architecture.
Gila Hayat: Yeah, I think it’s also important to double-click on that note so it’s not a single model that is a completely black box and we don’t know how really how it works and I think that is the initial fear from commodity language models that it’s insane to call it commodities now because a year ago they were not even, wouldn’t be able to imagine it, but they do not exist by themselves, they’re not a self driving machines that are working there is —
Christopher Anderson: Not yet.
Gila Hayat: Not yet. There is a tremendous amount and of investment in the infrastructure where it works. The ability to call for humans or to cross-reference, it’s not necessarily just one statistical model even though or even a family of models or a group of models that are working together but it’s also the ability to apply criticism and really see and check and see if really checks out even if this source is available real and truthful.
So when we’re talking about AI it’s not about referring also to the garbage-in-garbage-out, it’s not just a coin where you ask a question you get full legal case. Obviously, that was so easy then we wouldn’t be here talking about this but the fact that there’s a lot to be done and a lot of different steps in order to find the legal case from A to Z, it requires a lot of human intelligence and also a lot of different checks that you need to apply and they all need to come together.
So it’s not just this one model working, there’s a lot of software involved into understanding and to scouring every step of the way and see are we getting close to the story or not? It’s not certain whether it’s truthful, where it’s feasible.
Evyatar Ben Artzi: And there a lot of humans in this loop, right? We are like, we’re 90 people at Darrow. Israel, right? And I think the understanding is that the AI companies, the ones developing models and ML architectures and building AI, those companies are kind of trying to be the beating human heart of this machine, right? They’re trying to educate the machine in order to make it sufficient for our needs and that can’t be just this idea that we’re building a machine and that’s it’ll do everything for us. There is a whole operation around this. That’s what AI companies are about. The software is part of it. It’s not everything.
Christopher Anderson: Sure, that makes sense. We are going to take another break here and we are talking with Gila Hayat and Evyatar Ben Artzi of Darrow.AI and we’re going to come back out after the break and talk a little bit about how it’s implemented in firms and also a little bit of vision casting because like a year ago we couldn’t have even imagined that this existed and now we’re in a situation where we’re talking about some of it being commoditized already but so let’s talk about you know what the future holds as well but first a word from our sponsors.
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Christopher Anderson: And we’re back, we are talking with Darrow.AI and co-founders Evyatar Ben Artzi and Gila Hayat. So, you guys gave some examples about the uses but so in the beginning you talked about the using the or eliminating the Un-billable hour, which I take personally, let’s call it optimizing the un-billable hour and really allowing attorneys to do that in such a way so we talked about the reliability, so how do they come to you? How does a law firm come to you like what’s the question they should be asking?
Evyatar Ben Artzi: It’s the question of do I want to have discipline in my business development and marketing practice? I’m developing new cases. I want new cases for my litigation. What is going on? Am I doing this in a disciplined way? Am I systematic? Am I methodological and if I feel like there’s a problem there and it could be either the partner, the managing partner of the firm saying like I feel that we’re not disciplined enough or I see some of my litigation teams having trouble with a lot of downtime and I want this to be fixed or it’s just a litigation team that wants to insert some discipline into their practice.
Gila Hayat: And bring their A-game.
Evyatar Ben Artzi: Yeah, it got to the point where it’s not about like I think that’s probably the major thing in legal tech, like most legal tech companies are focused today on making firms more efficient, right? We focus on helping firms grow, so any law firm that wants to grow and has a litigation team, they will usually come up to us and say like, “hey how can you help us?” And we start out with saying we’re not going to help reduce your time spent on certain tasks, it’s not going to be helping you perform discovery tasks in a simple manner.
There are companies that do this but we don’t. We will help you generate new business for the firm, which means more cases that basically is it and these solutions that of course, powered by AI have the ability to mitigate the risk of spending a lot of time on these business development tasks and I think that’s the major value that firms that use Darrow are looking for. They’re looking to reduce the downtime and get more business into the firm to grow.
Gila Hayat: And also a lot of risk of going into both developing a domain that you’re already in or creating more cases of what you’re an expert in or in finding new domains that you want to practice or new types of cases of interests or trend and there’s a lot of risk into going into new domains or expanding the practice and there’s a lot of risk in that because you don’t know necessarily if you can find more cases, find the plaintiff and all of that work without both without discipline and without data is very, very dangerous so this is kind, this is what we’re offering to our partners, is the ability to assess and be exposed to new types of cases and to cases that they want to pursue more being backed with data.
Remove the guesswork around validating those new fields and just gaining an understanding what is the next course or what is the best strategy to move on forward in growing the firm.
Christopher Anderson: Yeah that makes a lot of sense so let’s take that last step now and talk about Gila mentioned that this stuff didn’t even exist a year ago, some of this commoditized today, it’s improving at an exponential rate. The models are becoming smarter and faster. So, what’s the future? How does this look? I don’t even know how far I could ask you to look down the road. There’s a fair to say a year from now? What do you see coming?
Gila Hayat: Well I think a year ago, I was overwhelmed just by the by the adoption just like any other person because it is mind-blowing the ability that has been introduced to the world so it’s very hard to guess how where we’re going to be in a year so don’t take my word for it but I think during that in the past three years that we’ve been learning, that we as humans or as professionals are going to distill the human cognitive things that cannot be replaced.
A lot of the tasks work is going to be redundant it’s going to be replaced. What is not going to be replaced is the ability to ask questions, define problems and I think one of the most interesting things to see when people interact with language models is the quick feedback around the ability to ask good or bad questions because sometimes we’re just being confronted with the ability to with our own ability to ask those questions again and gain answers. So, what we’ve learned that asking good questions is better than thousands of good answers when you don’t know what you’re trying when you’re trying to pursue.
So I think that what language models bring us or in general just the commoditizing AI it will turn everyone into product managers because technical skill will be something that is you can quickly overcome. However, the human component around problem-ing rather than solution-ing is going to be key and we’ve seen that working with lawyers is to carefully draft that question, carefully identify what sources do I trust or not, what kind of methodology, what kind of train of thought is aligned with my mission is something that is very hard to reproduce just by prompting simply.
It is really a process and the ability to learn that process for us as humans interacting with a lot more machines now and consuming a lot more content that by the end of next year 90% of the continent is going to be machine-generated so the ability to ask the good questions and formulate what are the problems that we’re trying to solve is going to be key, and this will completely re-define the way we interact with technology.
I can’t promise anything about how the next user interface is going to look like but in terms of cognitive skills we as human species are going to hyper growth of learning how to ask questions and when it’s quick enough to get that answer then the loops are going to close much faster.
Christopher Anderson: Is that part of what you do for law firms is teach them how to ask the questions?
Gila Hayat: Absolutely so we are asking a lot of questions as well to understand their practice and see how to apply those capabilities on the over those machine skills. There’s a lot of machine learning but also a lot of machine teaching and drafting, interacting with our clients is the core component of teaching that machine so by asking good questions, both the machines and the lawyers that we’re working with is what makes it happen and it’s what we take pride of the ability to translate that and introduce more context. The more content and more data into that but in the end it’s about the question that matters.
Evyatar Ben Artzi: And I think you don’t have to be a technologist per se to ride this wave, right? That’s the cool thing about it. Like if you’re just a lawyer and looking to understand how you can implement these capabilities into your practice you don’t have to go and study computer science for ten years.
Christopher Anderson: Yeah in a sense to me does lawyers and law firms I think are an excellent market for you because the one thing we’ve been trained for many, many years is to ask questions, that’s what we do, right? That and so that I think that’s going to have to be a great place to leave this. We are right up against time and that wraps up this edition of the Un-Billable Hour so we thank the audience and we thank the listeners for paying attention here but you know what, Gila and Evya we have, I don’t want to use the old cliche of just scratched the surface so I think we only dipped our toes in this water and the pool is getting deeper. If folks are interested in it and they want to learn more how can they get in touch with you to ask some more questions?
Evyatar Ben Artzi: So visit our website at Darrow.AI. Darrow.AI. We’ll have the link somewhere in the show notes maybe and just fill out the contact form. We’ll reach out to you as fast as possible.
Christopher Anderson: Will a human reach out or will it be AI?
Gila Hayat: It will be a human and if you have questions or just you’re keen to learn about AI or application of that, we are opening our emails and we love to hear. We love to have really tough problems to solve because this is what we started this.
Evyatar Ben Artzi: Yeah our names are not easy to pronounce but they’re short and easy to write. Bold letters each and you just write that name at Darrow.AI and you get our inbox and we’re ready.
Christopher Anderson: Fantastic so that is my guests today have been Gila Hayat and that’s G-I-L-A and Evyatar Ben Artzi but it is just E-V-Y-A for you at? Darrow.AI and that’s how you can get in touch with them and of course, this is Christopher Anderson and yeah, I’m just a human but I do look forward to seeing you next month with another great guests as we learn more about topics that help us build the law firm business that works for you and don’t forget that you have an opportunity to ask us questions also every third Thursday at 3:00 PM Eastern time at the Un-Billable Hour community table so I look forward to seeing you there as well and of course, you can subscribe to all the editions of this podcast at legaltalknetwork.com or on iTunes. Thank you so much for joining us. We’ll speak again soon.
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