Making Rain with AI

[MUSIC] >> This is John Quinn, and this is Law Disrupted. Today, we're speaking with Muhammad Arashik, the founder and CEO of Rain Intelligence. Rain, as in Rainmaking Rain, Rainmaking for lawyers. Rashik is a, he's a lawyer, I guess, a recovering lawyer as people sometimes say. But he found himself in a law firm after he graduated from law school. And there was an expectation that you would generate business and make rain. And he had an idea for an AI company that would help lawyers do exactly that. And if you take a look at their website, it's pretty interesting. It tells you that Rain Intelligence will help discover high potential cases before they're filed. Gain early insights into potential class actions through our AI-powered analysis of social media, e-commerce government, and other data sources. Streamline your business development process. The platform consolidates critical information allowing you to focus on strategy rather than time consuming research. Make data-driven decisions with confidence, stay ahead of industry trends and competitors help you identify a new series of liability and potential areas of growth. I have to say, Muhammad, that sounds really exciting. If Rain Intelligence does those things, those are things that a lot of lawyers are looking for. Yeah, thanks, John, and happy to be here. Yeah, I mean, the idea for Rain Intelligence really came out of my own personal need as an attorney. When I went to law school, and I went to law school really looking to start my own practice, a little bit different from most other law students at the time who kind of had this expectation to go work at a big firm. And so I started thinking, okay, what's the first thing I need to start this firm? And I realized that the first thing you need is clients. If you don't have clients, you kind of don't have a firm. And this wasn't a skill that was taught in law school at all in terms of business generation. So I started looking around for sort of legal tech solutions to help me solve this problem. And to my surprise, there was actually no legal tech solutions to help me solve this problem either. And so I thought, okay, shouldn't there be a data-driven systematic method, proactive method to discover potential legal needs and help me get ahead of those legal needs to develop new business for my practice? That thought kind of lingered in my head, but because I didn't have a firm, didn't have any kind of business generation skills, I went to go work at a firm, great firm, great people. And while I was there, I started kind of acting on this instinct I had about proactive business generation. And I actually started bringing in business sort of manually through proactively identifying legal needs at this firm I was at. And that went through analog ways, like taking people to dinner, giving speeches, writing papers, the old fashioned way. A little bit of that, John, but mostly proactively identifying legal theories or legal assets that that client had, that they didn't know about. For example, one kind of example of this that stands out is I was working at this firm in Baker's Field, California where I grew up. And I'm driving through the city one day on my way to work. And middle of summer, a warehouse is on fire. Big fire happened. And I was just looking at this and I happened to know I happened to have a relationship with the warehouse owners. And while I'm looking at the fire, I noticed that the way that the fire looks like it started, it looks like it started from the railroad property next door and it jumped over to the warehouse. And so I got really curious about this. And I went to the city and I thought, okay, what are the chances that that railroad parcel has kind of building code violations for overgrown shrubs because I've seen that parcel all the time. It's always overgrown the shrubs. Turns out that they've been cited for kind of overgrown shrubs constituting a fire hazard multiple times in the year preceding the fire. And so I took that insight, went to the warehouse owners, presented this information to them and said, hey, I think you actually have a pretty strong claim for liability against this neighboring property owner. And what stuck out to me and that is, you know, the first thing they said is, why thank you, this is really valuable information. And two, happened to retain you to help us pursue this case. And that was kind of the first type of impetus I had. This is a good idea. Proactively identifying legal needs is a better way to generate new business because it didn't feel fuzzy. It didn't feel like I was trying to sell the client anything at all. In fact, they were thanking me and me bringing in business to my law firm was simply a byproduct of me trying to do what I do best as an attorney, help people. And then we thought, okay, what if we could take those sorts of proactive legal insights and scale them so that every attorney has those types of insights that are relevant to their practice, their clients, their geography such that they can act on these proactive insights all the time going forward. Yeah, that observation you made, I mean, that's that's kind of a traditional way of identifying potential claims. There's been a fire, what caused the fire. Well, there's, you know, foliage brushes, whatever on the other side, look and see whether it was compliant with a code or not. I mean, that's a very traditional way. I mean, you've got you've taken this much further as I understand it into some type of data mining capability for identifying claims. Yeah, exactly. I think from that experience, what I realized is that every legal need sort of follows a similar pattern, meaning some event happens in the world that events could give rise to certain legal needs. And so if you can track the events all around the world and extrapolate what legal needs arise from those events, you can basically systematically, proactively predict legal needs on a pretty massive scale. And so there right now we look at data sources all over the internet, we look at social media data, we look at government complaints, we look at, you know, things like NHTSA recalls, consumer product safety commission, plaintiff firm advertising, press releases, where firms are soliciting plaintiffs for class action securities issues, things like that. So it's a pretty wide range of data sources that we analyze now to predict potential legal needs mainly in the complex litigation space right now. So you have a database and I mean, you query that database. There are prompts where you're saying, we'll give us an example of the kind of prompts that a lawyer might use to search the database to find potential claims. The platform right now, John, is less structured as a database where lawyers query prompts. What we do is we actually serve up the information in daily reports to the lawyers of proactive insights relevant to their firm and their clients. And the reason we do it that way is because, you know, we think that one of the big problems with legal tech solutions today is that they require a huge amount of behavior change from the attorneys to kind of learn these new legal tech tools and solutions to help them help their practice. And we sort of approach this problem with a high degree of empathy to say, okay, you're an attorney. You have a million things going on with your practice, the stresses of just everyday lawyer life. You don't have the time to go learn a different tool. What you need is a report that comes to your email just like Block 360 comes to your email or any other daily report. And it just gives you the insights that you care about. And so to your example, an example could be, hey, this substack, and this is a live example from about a year ago now, hey, this substack article just got published that indicates that there's price fixing in the PVC pipe industry, for example. And there's a lot of noise around that substack article such that it's really hard to identify that substack throughout all other noise and that to find out, okay, here's this substack article that indicates that there's going to be a price fixing class action against the PVC pipe industry. But with an AI algorithm, you can actually spot this sort of information source in a really direct way and be able to send that insight to the right attorneys who may be representing the PVC pipe company or practice antitrust more generally. Okay, so basically is your service a subscription service? Yeah, it's a subscription service. Right now, we work with about half of the AMLOB talk 10 firms and about 20% of the AMLOB 200. And the outputs that the different law firms get sort of contoured to those firms' particular interests, their practice areas, or is everybody seeing the same thing or what? It's highly, highly personalized to each attorneys practice their clients and their litigation history for them specifically. And I'll kind of take a step back and talk about how business development is done for these attorneys a little bit more generally before rain and kind of what we do to help them and what it looks like afterwards. Right now in most AMLO firms, there's a team of BD professionals and usually there's one or two per practice area or kind of industry practice group. And their job is, for example, I'm the BD person for antitrust and I'm here to support the entire antitrust practice group. And they're sort of given an impossible job which is you have to look around the entire internet for information to proactively help spot information relevant to all of my attorney clients, all of my attorney's clients. And it's impossible to do. There's just too much information and you're one BD person is supporting too many attorneys. But imagine if every single attorney had their own personal business development person who said to them, "Hey, over the last 24 hours, I looked at every single new complaint filed, every single social media complaint relevant to your practice, every single government announcement, AG announcement relevant to your practice, every single plaintiff firm website, ad campaign and press release. And based on all this information that I analyzed today, here are the five bullet points that are most relevant and most important to your business development efforts as an attorney." And so an example might be, "Hey, you represent X company. There's a plaintiff advertising campaign soliciting plaintiffs against X company in your practice area. This is something that you should reach out to them about." So basically, we take all this information and we distill it down and personalize it to each attorney based on their litigation history so that they are able to act with a high degree of precision and know they're acting on the best information that's relevant to them as an attorney. That's really interesting. So how did you go about building this database? How really did you do it? And how do you keep it current? And how long does it take you to it? It's a great question, John. It's a constant work in progress, meaning what we do is we constantly build out data pipelines of information where we think we can extrapolate potential legal needs from that information. So an example that is, you know, we could build out a data pipeline for product reviews and that would be an indication for any sort of defective product, product liability attorney to get ahead of defective product cases. We might build out a database of, let's say, sub-stack articles and academic studies regarding kind of pricing patterns that might be an indication for antitrust attorneys of what's to come in antitrust space. So there's data pipelines that we consistently build out and we're consistently sort of adding more data and more pipelines into our system so that we're able to identify more and more proactive legal insights and legal needs for our customers. How large is your team? Right now we're about 10 people total between three attorneys, about three engineers, and some salespeople and some support staff. So I assume you identify sources of information and websites in the like and you're just scraping and collecting information from all these different sources. Exactly, exactly. And I think, you know, the scraping and collecting of information is of some value but we think kind of the greater value is being able to piece together different data sources where you're able to identify legal needs that would be non-obvious if you're just looking at one of those data sources. Can you give us an example? Yeah, definitely. So I'll give you a couple examples and maybe starting with an area like food, food labeling class actions, for example. If you were looking at food labels, just generally, you scraped a bunch of food label data and you saw a product that was advertised as preservative free and the ingredients had an ingredient called citric acid. If you were looking at that data in isolation, it's very unclear that that is going to lead to potential loss here. But if you then marry that data with the fact that the FDA has made an announcement that citric acid is considered a preservative, then there becomes a higher likelihood that that labeling data is going to lead to a potential loss here. And then you can get even kind of more granular and say, okay, there's actually another class action against a different food brand that is for this exact same fact pattern, meaning they advertise their product as preservative free and it contains citric acid, which the plaintiff's alleged is a preservative and therefore false advertising. And you marry all these sources together and now that labeling data becomes very, very indicative of a potential lawsuit in the near future. And so I think the large benefit of what we do is we have kind of the practice area expertise and that we've built up over time. So that we're able to ask ourselves, okay, what data sources can we marry together to produce insights that would be non-obvious to somebody just looking at any one of these data sources in isolation? That's very interesting. Well, let me give you a live example based on something I've been thinking about just within the last couple of days. I've talked to some people, I've done some reading and it concerns the emerging modular nuclear reactor industry. I mean, I'm sure you've heard about this so that these small nuclear reactors, some fission, some fusion, about the size of trailers or maybe even smaller than trailers, which people think there seem to be a lot of reason to be this is going to be a very significant power source in the very new future. And it's actually kind of a crowded field. There's a couple of dozen at least startups that are well-financed with the objective of creating power from nuclear fission or nuclear fusion. I mean, you can do some research on it. It's really kind of amazing to see these companies and they have very different strategies and approaches. One aims to one called deep fission, aims to put these reactors a mile underground in a 30-inch wide hole, a mile underground because it turns out if you can put something down there, there's a lot of things you don't have to worry about in terms of construction and safety and the like. But they all have different approaches. They have IP, many of them have patents. This is obviously a highly regulated area. You have this whole AI phenomenon with these enormous which demands for power in all these power centers. Billions of dollars are going into building these power centers around the world. Nuclear may be a solution for this. So I'm very much on the front end of this. I mean, I'm not aware of any litigation relating to this. But AI, I think it's got to be a highly regulated industry too. I think there will be a shake out. There's billions of dollars. Very, some of these are very high finance. Sam Aldman's involved in one of them. Bill Gates is involved in one of them. There are IP issues. And if I come to you and I say, "Well, this is something I'm thinking about. It hasn't arrived yet, but I think this is coming. Do you think you could help me?" Yeah, 100 percent. I mean, I think those are the types of kind of white gloves service issues that we do for a lot of our large customers, where they come to us with something just like that, where they say, "Hey, this is a trend or this is kind of an idea." Yeah, it's maybe not even a trend. It may not be a legal trend yet. I mean, it's definitely an industry trend that's going to be a thing. Billions of dollars is going into it. At some point, it becomes a legal thing. So we're totally on the front end of this together, and I'm sharing my idea now with the world. We're totally on the front end of this, Mohamed. You think you could help us identify friction points or emerging issues, emerging conflicts. So look at patents, things like that. Yeah, I think we could. I think this seems like a very quinimanu case, so I'm an exciting one. Well, what's your reaction? I mean, is it maybe too preliminary? Because these are all startups, or very, very early stage companies. Yeah, I think you hit the nail on the head there, which is when we try to assess potential legal issues in the future, we look at liability damages and collective collectibility as like the three main things. And seems to be over here, given that these are a lot of startup companies, even though they're well funded, I just wonder how much of a collectibility issue one may have pursuing these. But of course, I need to look further into this too. Yeah, being for sure. So if somebody wants to learn more about your service, I assume they can go right to Rain Intelligence and you've got to contact us button there that they can push and get in touch with you and learn more about this. Yeah, yeah. Rainintelligence.com, contact us or request a demo. And generally, what we try to do is we try to put ourselves in the customer shoes where whoever we work with, we provide them pretty personalized, helpful information just to start out with to say, "Hey, here's some insights relevant to you to go get some business." And we like to just prove it to our customers before we even have them sign up with us to say, "Hey, once you generated new business with our data during just this small trial period, then the whole thing becomes a no-brainer." And so we'd like to approach most engagements that way. Really interesting. Okay, before we sign off here, anything else that our listeners should know about Rain Intelligence and how it works and how it can help them make rain and grow their businesses or we've basically covered it for a kind of a general introduction. I think we've pretty much covered it. Thanks so much, John. This has been a super fascinating conversation and I'm glad to be talking to you. Thanks, Mohamed. We've enjoyed it also. This is John Quinn. This is Bob Disrupted. We've been speaking with Mohamed Rashique of Rain Intelligence. Thank you for listening to Law Disrupted with me, John Quinn. If you enjoyed the show, please subscribe and leave a rating and review on your chosen podcast app. To stay up to date with the latest episodes, you can sign up for email alerts at our website lawifedisrupted.fm. Or follow me on x@jbqlaw or @quinnamagnol. Thank you for tuning in. [BLANK_AUDIO]