Raymond Mieszaniec:
It's a bloodbath out there. I see AI as more of like an Iron Man suit to scale the firm's growth further.
Chris Dreyer:
Welcome to Personal Injury Mastermind. I'm your host, Chris Dreyer, founder and CEO of Rankings.io, the legal marketing company the best firms hire when they want the rankings, traffic, cases, other law firm marketing agencies can't deliver. Do me a favor and hit that follow button right now to subscribe. You'll be the first to get every new episode delivered straight to you the moment it drops, giving you the edge.
On these special Toolkit episodes, we dive deep into conversations with the leading vendors in the legal sphere, the masterminds behind the technologies, services, and strategies to help law firms not just survive but thrive in today's competitive landscape. This is Toolkit Thursday on PIM, your weekly guide to staying sharp in the legal world.
Let's get started. In the cutthroat world of personal injury law, the stakes are high and the competition is fierce. To come out on top, firms need every advantage they can get. That's where Ray Mieszaniec and his powerhouse startup EvenUp comes in. Ray is the visionary and co-founder of EvenUp, the AI-powered platform changing the demand letter game. It's the tool for securing lightning-fast resolutions and jaw-dropping settlements for clients. EvenUp is increasing transparency in an industry of closed-door settlements and has quickly become the secret weapon of choice for PI attorneys across the country. Ray shares his battle-tested strategies for ramping up case velocity, transforming your team into a squad of legal superheroes and running a lean, mean profit-generating machine. Here's Ray, COO and Co-founder at EvenUp.
Raymond Mieszaniec:
When I was younger, in 2004, my dad was in a catastrophic motor vehicle accident. So it was a runaway shooter, tried to kill his girlfriend, shot her, went on a high-speed police chase. And as he was going down the highway, this is at 5:00 AM in the morning, my dad's crossing the intersection and this gentleman rips my dad's car in half. That left my dad with a permanent disability for the rest of his life.
And then, we went through the trials and tribulations of the next three years trying to settle for a fair settlement. Ultimately, this is not something I knew back then, but something that I learned now is a lot of the data that is available to the public or available to attorneys to access is typically public jury verdicts, and only 3% of these cases actually go to trial. And so, 97% of these cases actually settle in favor of the plaintiff outside of court. But the question is, how much? Nobody knows. And so, that was something very interesting to learn about.
And then, learning further where my co-founder, Saam, he's a former defense counsel, but basically, he enlightened me with just this information in terms of how the dark side works, things like what's the claims process of the dark side? And so, talking about the different tools or softwares that the defense uses like Colossus and Claims IQ, all of these softwares are basically where insurance carriers would pull together their data into one place and collectively use that data to low ball the crap out of everybody on the plaintiff side.
But when you think about the plaintiff attorneys that we work with, they're all sitting on their own data and they don't necessarily share all of this data. There is no transparency there. And the reason for that is because a lot of folks are competitive with one another, right? Long story short, EvenUp, it's in our name, but we want to serve as that Switzerland where everybody can pull their data together into one place and we can use that to raise all ships to even up the playing field in terms of giving transparency to what cases are selling for behind closed doors.
Chris Dreyer:
I love how you mentioned that, that's immediately what I was thinking of as the rising tides type of scenario is it's just better for everyone with the more data it consumes as opposed to that real competition where there's a zero-sum games type of scenario. EvenUp ingests and analyzes millions of pages and medical documents every single week, and we got that system built out. So what comes out on the other end when medical records are put into the system, how much time are you actually saving clients from that perspective?
Raymond Mieszaniec:
First and foremost, in terms of reviewing these records and really using those records to understand your case, reading through all of these pages individually, it takes a lot of time. So some cases might have like 10,000 pages of records, some may have just a thousand, but even just a thousand is still a lot for somebody to read through. And it's also something that us as humans as we're reading through this, we can experience fatigue, we can miss some very key facts as we go through all this.
And so, what we've done here at EvenUp is essentially we've trained an AI to basically understand how to interpret these documents, so read and interpret these documents and extract all of those critical pieces of information from the medical records themselves, the medical bills, like the accident reports, any documents that you feed our way, we've trained the algorithm to basically understand what it needs to pull out and then use to transform into a demand package or a medical chronology.
And so, it's been a pretty crazy journey building that. So we were the ones that pioneered the development of this technology and still, I would say the best in the game at it because of that head start.
What's very novel or unique about the AI that we built is it's not just a simple OCR or a keyword search, it's not like a Control + F, it's actually interpreting language. And so, even if you were to produce a summary or anything like that for the attorney to kind of read through and basically understand their case, the next thing that they're trying to achieve, like what is their objective here when reading through this is they're trying to surface insights like any bad facts that could cause negotiations to go sideways, so like pre-existing injuries, like gaps in treatment, if there's like a history of substance abuse.
We surface all of that using our AI. So our AI is able to identify all of these bad facts or problematic facts, surface it for the attorney so that they can better understand what could go sideways in their case and better prepare for it in advance before they go into any conversations with adjusters or defense counsel.
Beyond that, it's flagging areas of opportunity. So if you are tired reading through all these records, flagging things like symptoms of potential traumatic brain injury that you might've glossed over, we flag that for you so that if you missed it, you can now go send the client to go see a specialist. And then, aside from that, just flagging anything that's missing that could be money on the table, so like missing bills, missing records, missing providers. So all of this generally saves anywhere from five to 10 hours per case where they would've had to summarize and interpret all of this themselves.
Chris Dreyer:
When I think of leverage, I think there's labor-based leverage, there's software-based leverage, there's media distribution and how that impacts profit. So the first thing is you're maximizing the settlement values, so profit. The second thing is reducing labor, human-based expenses, you're getting more output, so you're getting more profit there. And then, just the speed of research on its own is profit as well.
The personal injury attorneys listening, it's like some of you are old school, you've done this manually in-house and you're very experienced and you have your own process. What's the challenge of adoption? How intuitive it is for someone that maybe isn't super tech-savvy? Like for example, I'm in the digital space and ChatGPT comes out and you've got Surfer, you've got NeuronWriter, you've got Jasper, you've got all these, some of them are quite complicated versus others. Tell me about just the ease of getting a personal injury attorney into the system and utilizing your technology.
Raymond Mieszaniec:
Yeah. Yeah, no, it is super simple. So we've built the interface to just be like a portal where people log into it. And then, if they need to request a demand or if they need to request a medical chronology, you just input some key fields of what's your plaintiff's first name, last name, and you can drag and drop all the documents. And that's already one way that we can already get going.
But aside from that, we do have our integrations with the big CRMs out there. And so, for any firms that want that minimization of clicks that it takes to actually get documents our way and get, of course, a document back that they can use, that's where these integrations aim to facilitate that. So we have integrations with Clio, Litify, SmartAdvocate, you name it. We've essentially got this button that you can click and it says generate AI demand. It allows you to select all the files that you would want to send our way or deselect any files that you wouldn't want to send our way. We get that on our end, it imports into our system and then we already get going.
So very, very simple, easy to use. We have to make it simple for our users to use. And one of the fun things that we've done is, my co-founder that I mentioned, the former defense counsel, he's probably the least tech-savvy person in the world, and over time, we've always tested our products on him. So if he could figure it out, then anybody could figure it out.
Chris Dreyer:
That's awesome.
Raymond Mieszaniec:
So it's always been a fun development process there.
Chris Dreyer:
EvenUp helps level the playing field with data, but what does this look like in practice? High volume of boutique firms can reduce the back and forth with adjusters. Teams of all sizes are less likely to burn out. And cases are resolved quicker for more money. Ray shares case studies where EvenUp has had a real impact.
Raymond Mieszaniec:
These higher volume firms, the things that they've said is like we've helped them resolve claims faster and for higher amounts, really kind of reducing the back and forth that goes on between the adjuster and their team, mainly because the approach that we take is, basically, we send everything upfront to the adjuster, so a full, complete set of information within the demand where it outlines like, "Here are all the facts, the verdicts, the computations we're using to justify the amounts that we're asking for. All of the evidence is here, photos, everything. It's up to you to just settle this for a fair claim at this point because otherwise this is also the information that we're going to use to see you in trial."
And so, there is this element of also making sure that we're providing all of the right information to help maximize the value of the claim. And so, by having this comprehensive approach, we are actually helping the adjuster put in the right pieces of data into their valuation tools to set the reserves as high as possible in the event it goes to trial. And so, if those reserves are actually set at a very high level, that's where it's also triggering any review from the defense counsel to actually look into this and be like, "Hey, wait, we should probably stop messing around here in terms of these low ball offers and stuff."
And this actually comes from a lot of the understanding of our team members who are former defense counsel, ex-claim adjusters, again, unveiling that process of the other side, where it's like they have lots of stories where if they knew what was going on within the negotiations with the adjuster and how they were actually low balling some of these firms or some of these clients, they would've settled it far sooner rather than see this go to trial, and then now, see their name on it that this is something that they have to try.
Because a lot of the times, and I had one colleague who told me a story, looked at one of his cases and he was just like, "This is an immediate L that I'm going to take. I'm going to take a loss here. What the heck is going on?" And in instances like that, he would pick up the phone and just try to smooth things over with the attorney that he's going up against, and he was just like, "Hey, can we settle this? This was a big oversight on our end. How can we do right by you guys here?"
And so, I wouldn't always say it, like I know there are some folks on the other side who maybe take their job very, very seriously, but I would say that there are some good humans out there on the other side too just trying to make sure they do the right thing for plaintiffs as well. So kind of taking that approach, helping to maximize the value of the claim. So settle for higher amounts faster is one element.
The other piece as it relates to some of these growing firms is one of the biggest challenges law firms face today is the fact that they can't find great talent. It's a bloodbath out there. Everybody is trying to poach each other's case manager or poach each other's paralegal or attorney. And people are going left and right to different firms. And at the end of the day, every firm is kind of spread really, really thin. They have a few really, really good people trying to do many, many different things.
And what's crazy is when people think about AI, they think about, "Oh, I'm going to slash a bunch of head count and this is going to make my firm much, much leaner in that nature and we're going to basically cut a bunch of costs in that sense." I do understand how you can see it that way, but I see that the situation that most firms are in is that they don't actually have enough people to carry out the good work that needs to be done.
And so, I see AI and our services as more of like an Iron Man suit to scale your best people, one to many, to help them do more work at your firm or essentially allow them to do work that will help drive the firm's growth further without necessarily adding a ton of head count or struggling to find those unicorns who are really good EQ and really good writers and all that stuff.
If we can take the writing off of their plate and allow them to focus on finding talent that is really good at building great relationships and organizing information, that's where we can ultimately do what matters most. And I believe that it's, number one, getting the requisite information that will help work up the value of the case. And number two, delivering a 10 out of 10 experience to your clients. Right? It's actually speaking to them and not putting your phone on Do Not Disturb and not answering calls. So actually connecting with them, helping to coach them and be vulnerable about their pain so that you can take that information and tell their story through the demand, through conversations with the other side, the jury even.
Chris Dreyer:
One of the KPIs that I think many firms should monitor is a revenue per employee, and what this allows for is to maximize revenue per employee. So you're just enhancing their capabilities, kind of like a tool or a suit, so to speak, I've heard you say on other interviews, and I think that's true.
And I also think too, and I sound like a salesperson for you, and I have no financial incentive here, I'm just trying to educate the audience is I feel like, with the data, it helps to eliminate errors, which eliminates waste and redundancies when having to redo work and things like that.
So let's talk about a couple of things here to round it out though. So walk me through first what the onboarding process looks like. Just 80/20 of that. And your pricing model maybe for how a pricing model's impacted by maybe a solo practitioner versus maybe a firm that's getting a hundred cases a month.
Raymond Mieszaniec:
In terms of just the onboarding process, it's quite simple. So we do have dedicated team members that are assigned to every firm and assigned to making that firm successful. So we do take customer success very, very seriously.
That said, how it works is there's some onboarding checklist that we have a client do before a complete. It's just like a survey that they complete prior to an actual onboarding call. When we hop onto that onboarding call, we discuss some of the inputs in that checklist. So a lot of the inputs relate to the preferences that they have in terms of how they want the final work product to actually look like. So there's different ways that we can format things. There's different ways that we can prepare exhibits or organize the exhibits. So all sorts of stuff there where we kind of fine tune, align on the expectations here of what that work product looks like. And then, we're off to the races in terms of doing first set of work for you.
Now, once we actually complete that first set of work for you, that's where we would hop on a call and we also review it. If there's any changes you'd like to make or anything that you got that you looked at and you're like, "Hey, if I were to do this myself, I probably would've written it a little bit differently and so forth."
This is the cool part of our AI as well is we do have a feedback loop. There's two ways to communicate feedback to us. You could either just be like, "Hey, Ray," and send an email like, "These are the things that I want you to change in the future. These are things that I changed within my demand. And yeah, please implement these in future demands." We could do that.
The second way is if the firm actually just sends us the final version they ended up sending out, we can take that and we can actually track the changes there and then we can input what those changes were into the demand generator that we have. And now, every demand after, comes through your way is more and more like the ones that you would've done yourself if you fed us that information and if you had all the time in the world to actually do demands yourself. So that's the cool feedback loop here. It does get better and better over time because we do want this to be exactly the way you would've done it yourself if you had all the time in the world.
Chris Dreyer:
And like you said, the rising tides, the more data it gets, the better it's going to perform. What's your pricing model look like for our audience?
Raymond Mieszaniec:
There's definitely some flexibility beyond what I'm about to state here, but generally, our pricing is anywhere from $175 per case to $600 per case. And essentially, all of this subscription model, so it is a subscription model that you would be on here. And that subscription model is based on the number of demands that you do with us on a monthly basis. So if you do more with us, we give you better economics.
That said, there are other areas where we can customize templates. So for example, I did say 175 was kind of like the lower end there of pricing, but let's say if we wanted to customize a template where we can remove certain pieces or aspects of the demand in terms of content that maybe you're not really interested in or you don't need, we can actually customize that to a degree and kind of bring the price down as well.
I only specify that because it relates to some of these really low value claims that you might be working with. You might not need a crazy, overkill demand, by that I mean like a comprehensive demand that's going through the storytelling, all the damages, all of that. You might need something more quick and dirty to help tender like a 10K policy limit. And that's where we can customize things to give you what you need there.
Chris Dreyer:
So one of the things, you hear ChatGPT and the sourcing's incorrect and you hear intellectual property and things like that. Just how do you protect yourself from just the compliance and the data perspective?
Raymond Mieszaniec:
The folks who have cited fake cases or anything like that, they kind of made this big mistake where they relied on ChatGPT or trusted ChatGPT to do something that it was never really set up for success for. You know what I mean? So what I mean by that is if you think of ChatGPT like your regular household dog, like a household dog, household pet, it's able to do very basic things like sit, stay, come, down, all of that stuff. Right?
But if you want it to do specialized tasks like what a police dog would do, or a rescue dog, it needs specialized training. What I'm kind of pointing at is ChatGPT learns off of all of this information that exists on the web. You and I and any attorney to search verdicts would still need a subscription to VerdictSearch, Westlaw, Bloomberg. This isn't publicly available, so there's no way that ChatGPT would've had access to that kind of information to even cite in the first place. Otherwise, I think LexisNexis and Westlaw and stuff like that would be pretty upset with that. And so, you just can't expect it to produce that kind of output.
And so, one thing that's really cool with EvenUp is the reason why our AI can actually cite verdicts that are relevant to your case, so we actually have our own AI that is able to pick the most relevant verdicts based on injury, treatment, jurisdiction down to the county level as well as who you're going up against, and it's able to cite those verdicts in the demand itself for you, how it does that is we have our models that is actually living in these data sets of verdicts. So all of the data that you would expect from LexisNexis, Westlaw, VerdictSearch, et cetera, we've compiled all of that and then we have our AI scouring through with its own algorithm in terms of how to match up the best verdicts to your case at hand. And so, it actually has that contextual understanding that, say, ChatGPT doesn't have. So there's that in terms of how we're protecting things.
As it relates to compliance, we actually go beyond HIPAA compliance, we go into SOC 2 compliance as well. So SOC 2, not necessarily being something that is required in the space, but it is required by many of our largest customers. So our largest customers will not move forward without SOC 2 compliance. And so, we've gone through the painstaking work of getting that. And for context, I'm a former cybersecurity consultant from PricewaterhouseCoopers.
What it takes to actually become SOC 2 compliant is essentially a full audit into your systems, the security of those systems and where that data is living and making sure that all of that information is secure and not something that someone can just easily hack and get into. All of this has to be audited by a third party auditor where you get a final report of all of any deficiencies or anything and how you've actually gone and fixed it. And then, you get that stamp of approval from the audit firm. And then, you can be on your way and promote that you're like SOC 2 level 1, SOC 2 level 2 compliant. So we've achieved the fullest or the highest levels of compliance to make sure that we are offering the absolute highest level of assurance to our clients.
Chris Dreyer:
I think that's incredible. Ray, this has been great. Where can our audience connect with you and learn more about EvenUp?
Raymond Mieszaniec:
Feel free to reach out to us. If you want to schedule a demo or anything like that, our website allows for that. But if you do want to reach out to me, I'm totally accessible as well. So my email is raymond@evenuplaw.com, so Raymond, R-A-Y-M-O-N-D. And if anybody wants to reach out to me by phone too, you can text me at 415-490-6842. That is my personal cell.
And as busy as the days have been, I really do care about connecting with our customers and making sure that we're solving different problems for them. This is value that we want to offer beyond our services. It's just being a partner that you can connect with, talk about your problems with and see what recommendations we might have to help you solve those problems because we're all in this together, this journey of growth.
Chris Dreyer:
Thanks to Ray for all's insights today. Let's hit the recap. Increase velocity. Leverage technology to increase case throughput and boost your bottom line. By automating time-consuming tasks like demand letter drafting, firms can streamline their workflows, take on more cases and ultimately drive more revenue. Firms that embrace cutting edge tools can resolve cases faster and more efficiently, giving them a crucial edge.
Raymond Mieszaniec:
We send everything upfront to the adjuster, so a full, complete set of information within the demand where it outlines like, "Here are all the facts, the verdicts, the computations we're using to justify the amounts that we're asking for. All of the evidence is here, photos, everything." By having this comprehensive approach, we are actually helping the adjuster put in the right pieces of data into their valuation tools to set the reserves as high as possible in the event it goes to trial.
Chris Dreyer:
Create superheroes. Firms need to give their teams every advantage. Using the right tool is like giving each member of the team and Iron Man suit that pumps up the revenue per employee. Attorneys can focus on the highest value work like building client relationships and honing case strategy while still managing caseloads. Partnering with a service like EvenUp allows teams to punch above their weight class and take on more cases without getting overwhelmed.
Raymond Mieszaniec:
When people think about AI, they think about this is going to make my firm much, much leaner in that nature and we're going to basically cut a bunch of costs in that sense. The situation that most firms are in is that they don't actually have enough people to carry out the good work that needs to be done. I see our services as more of like an Iron Man suit to scale your best people, one to many.
Chris Dreyer:
Supercharged settlement value. Embrace a data-driven approach to case valuation and negotiation. By leveraging vast data sets of past settlements and verdicts, firms can make more informed decisions and set realistic expectations. Tools like EvenUp can help level the playing field against insurers and defense teams that have long used data to their advantage.
Raymond Mieszaniec:
Only 3% of these cases actually go to trial, and so 97% of these cases actually settle in favor of the plaintiff outside of court. But the question is, how much? Nobody knows. But when you think about the plaintiff attorneys that we work with, they're all sitting on their own data and they don't necessarily share all of this data. There is no transparency there. And the reason for that is because a lot of folks are competitive with one another, right?
Long story short, EvenUp, it's in our name, but we want to serve as that Switzerland where everybody can pull their data together into one place and we can use that to raise all ships to even up the playing field in terms of giving transparency to what cases are settling for behind closed doors.
Chris Dreyer:
All right everybody, that's it for today's episode. I hope we added a few more tools in your kit. For more information about Ray and EvenUp, head on over to the show notes. Before you go, do me a solid and smash that follow button to subscribe if you haven't already, I'd sincerely appreciate it and I know you don't want to miss out on another episode. Thanks for listening to Personal Injury Mastermind with me, Chris Dreyer, founder and CEO of Rankings.io. Catch you next time. I'm out.
