0:05

Welcome to another weekend bonus episode of the Tech

0:07

Meme Ride Home. I'm Brian McCullough,

0:09

as always. This is a portfolio profile

0:12

episode. And Chris

0:14

is joining us for this one, Chris. Hello,

0:17

hello. The reason being

0:19

because we're going to talk about an investment

0:22

that the Ride Home AI Fund made, which obviously

0:25

the Ride Home Fund made as well. But

0:27

this is one of our AI companies from

0:29

this year. We have with

0:32

us Scott Kvitin, the

0:35

founder of Case Mark AI, which you can

0:37

find out more at as you're listening at

0:40

casemark.ai. Scott, thanks for

0:42

coming on. Thanks for having

0:44

me, Brian. Good to see you both. Good to see

0:46

you, Chris. Yes. So

0:50

let's, let's right off the top. Case

0:53

Mark, as maybe the name implies, is AI in

0:57

the legal space. So give

0:59

us just sort of like the elevator pitch

1:01

for what Case Mark does, Scott. Yeah,

1:04

I think, I think the sort of

1:06

shortest version of this is we've created

1:08

kind of the easy button for attorneys

1:10

for specific discrete tasks that they'd like

1:12

to accomplish with AI. But we

1:15

do it with sort of security and privacy

1:17

in mind. In other words, we deal with

1:20

the implications around data leakage, training, making sure

1:22

that this is safe for

1:24

them to use. But then by boiling

1:26

that down to really simple tasks

1:29

that they try to accomplish, specifically what

1:31

we focused on early on were things

1:34

like deposition summaries, case summaries, trial

1:36

and hearing transcript summaries. And

1:38

that's kind of the early phase of this. But

1:41

what we're realizing is that over time, attorneys are

1:43

realizing, wow, there's actually more you can do with

1:45

AI. And once they see the simple use cases, they

1:47

realize there's a bunch of ways that they can essentially

1:50

take all this unstructured data that they

1:52

have and then leverage AI to transform

1:55

it. Into, you know, interesting structured analysis

1:57

and reporting for what they're trying to

1:59

do. to accomplish. So

2:02

you're talking about things like case

2:04

summaries, like depositions, as you say,

2:06

hearing summaries, contract

2:08

review, discovery response. One

2:12

of the sort of narratives for how

2:14

at the very beginning, in this first

2:17

inning of this era of the AI

2:19

revolution, it's about getting

2:22

rid of the busy work, or at least

2:24

obviating the busy work of

2:26

freeing folks up

2:28

to not have to

2:30

spend so many hours on stuff that allow

2:34

you to focus on other more important things. Is

2:37

that sort of the focus right now, what

2:40

I just described? Let's get rid of spending hours

2:42

going over depositions and things like that? Yeah.

2:45

Let's get rid of the TDM work. It's

2:48

easy to lose the details or miss

2:51

something because you're tired and you're on

2:53

the fifth hour of going through what

2:55

might be a boring deposition. Humans

2:59

are fallible in that sense. Of course, the flip

3:01

side response then we get from some attorneys is,

3:04

well, wait a minute, aren't you going to take

3:06

away my opportunity for a bunch of billables? Our

3:10

response is, well, no. One,

3:12

you still should review what we're producing. But

3:15

a lot of attorneys also have some

3:17

artificial limits in place, whether it's on

3:20

the defense side they can only bill so much

3:22

for what they get from these summaries. It might

3:24

take them six hours to do a summary, but

3:26

they can only bill two. They

3:28

lose that four hours. Or on the plaintiff side,

3:30

they just want to be able to do

3:32

it as inexpensively as possible because they're doing

3:35

it on a contingency basis. Any costs they

3:37

accrue then count against any potential future

3:39

settlements. The other thing that

3:41

I think a lot of folks forget about with respect

3:43

to AI right now is that

3:45

they talk about this doom and gloom around

3:48

how it's going to replace all these paralegals

3:50

and associates. If we were at 100% AI

3:52

usage today right now, I would agree with

3:54

that. However, what people forget

3:56

is that AI is actually going to enable,

3:58

it's going to make get really, really easy

4:00

for law firms to litigate. So we're about

4:03

to go into a hyper growth in terms

4:05

of litigation that's going to happen. So I

4:07

think we're going to see a 5 to

4:09

10x increase in litigation, which means there's

4:11

going to be a demand for not only

4:13

the AI solutions, but you're going to need

4:15

humans to sort of arbitrate and traffic control

4:17

on all this. So what we're doing is

4:20

removing the burdensome, time consuming, tedious

4:22

tasks and allowing attorneys to actually use their

4:24

critical thinking. And we actually think this is

4:26

going to increase their job satisfaction for

4:29

sure. Insert lawyer joke

4:31

here. I don't know if the phrase,

4:34

an explosion in litigation is what some people want

4:36

to hear. But I guess. I'm looking

4:38

forward to that. Yeah. Well, listen. That sounds like

4:40

a party. If you're a lawyer,

4:42

yes, I can see that as well. So

4:44

sue me. Says no one ever. Yeah. Yeah.

4:46

OK. Let's imagine that I'm

4:50

a law firm listening to

4:52

this right now. What does it

4:54

entail for me to start

4:56

using your tool? Does

4:59

it plug into my existing workflows easily? What

5:03

does it take to get running with K-SMARK? Yeah.

5:05

We tried to keep it as simple as possible.

5:08

And we also tried to solve the

5:10

problem in the simplest fashion as

5:13

we could. So you can essentially sign in,

5:16

log in with your email address, your

5:19

law firm's address, whatever it is. You

5:21

upload a file. You choose the workflow you want to run.

5:23

There's a couple options in there if you want to choose.

5:25

Otherwise, you can just do the defaults and click Go. And

5:27

then a couple of minutes later, you're going to have some

5:30

results that you can then download

5:32

and use. And I think what's interesting here,

5:34

too, is the work

5:36

product that we generate is a Word document

5:38

or a PDF. And while that might be

5:41

boring to us as technologists for

5:43

attorneys and law firms, their programming

5:46

language or software of choice happens to be

5:48

a Word document or a PDF. And

5:51

so when we can actually download that

5:53

PDF of a summary of, say, a

5:55

deposition, and then we can actually, what

5:57

we do is we append the transcript.

6:00

the source transcript is appended there. That's

6:02

actually a really powerful encapsulated litigation tool

6:04

that they can then drop into their

6:07

case management solution or

6:09

they can forward it onto their insurance adjuster. And

6:11

it doesn't, we're not completely replacing how they do

6:14

their work. In other words, they can

6:16

jump in, use our tool and then drop it into the

6:18

way that they do things. And

6:20

attorneys really like that because it's a way

6:22

for them to kind of try these things.

6:24

So often I see these really beautiful products

6:26

that are AI powered, but they require that

6:29

the attorneys completely change how they do business.

6:31

And if you think about a law firm that has a hundred

6:34

people, it maybe has 25 attorneys

6:36

and then 75 supporting staff. If

6:39

they all have to then learn some piece of

6:41

software and then that software is continuously

6:43

changing and that will

6:46

completely screw up their daily workflow such

6:48

that if a law firm, if

6:50

all the paralegals lose an hour because

6:53

of some new software, guess what, that's

6:55

75 hours of billable time that you've

6:57

lost and that's a big, big deal.

7:00

And so we try to make our tools as

7:02

simple and as easy as possible and deliver a

7:05

solution that basically mimics what they do by hand

7:07

today. And then in the future, we'll

7:09

evolve these tools to be sort of more advanced and

7:12

sort of ingrain them in their, more of

7:14

their daily process. But again, try to be

7:16

as lightweight as possible today. That

7:19

seems to have the biggest impact for us. I

7:21

think the other piece too is by

7:23

being a lean and mean startup and

7:26

because you're looking at the entire sales and marketing team,

7:29

we don't have time to be able to like follow

7:31

up and reach out to a lot of these folks.

7:33

So we do have a self-service model which

7:36

actually really helps these folks because maybe

7:38

it's the paralegal, the law

7:40

firm administrator, the overworked office

7:42

manager who's in charge of this

7:44

and they can jump into our solution, they can

7:47

try it, they can break it without having to

7:49

talk to a salesperson because they're always worried about

7:51

asking a question that they think might be considered

7:53

stupid which obviously we would never say that and

7:56

we don't think that. But the

7:59

legal industry is full. of people who

8:01

sort of shout down at the people

8:03

who are the paralegals and associates by

8:06

design. It's a weird industry. I can say

8:08

this because my wife's an attorney, so I

8:10

consider myself attorney adjacent. Just

8:13

to hear some of the stories of her when

8:15

she was a younger attorney, she's now been doing

8:17

this for almost 15 years, but

8:20

just to hear how they just get sort of brow beat

8:22

all the time. It's just astounding.

8:25

Anyways. Last one, and then

8:27

I'll let Chris give you some questions too.

8:29

But obviously here, one

8:31

of the concerns would be, I

8:34

can't have these sensitive documents fall

8:37

into the wrong hands, leak, be trained

8:40

on. So how

8:42

are you thinking of and dealing with

8:45

things like privacy, security, stuff

8:47

like that for everyone

8:49

that their business

8:51

is business critical, but you're dealing with legal

8:53

stuff and you could blow up cases if

8:55

you do it wrong. So what's

8:58

your process there? Yeah, for sure. So

9:01

we have relationships with

9:03

Amazon, Microsoft, and Google

9:06

to actually have sort of private cloud infrastructure

9:08

for all of the data that we ingest.

9:11

And so the best way to describe

9:13

it is when we ingest private data from our

9:15

customers, we basically drop it into

9:17

a container along with the LLM of choice that

9:19

we're using. We then do our transformations

9:21

on it and then output some summary, and then we

9:24

tear that whole thing down and it goes away. In

9:26

other words, we don't actually use models that

9:29

are, like we don't connect to OpenAI's

9:32

APIs. We do

9:34

everything inside of Azure with respect to OpenAI,

9:36

but we also are using Gemini and Claude,

9:39

a little bit of llama, some Mistral in

9:41

there. But again, that sort of same theme runs

9:44

true that we're not going to train with our

9:46

data. And we lean really hard into that. And

9:50

we've gone through quite a few, not only sort of

9:52

those third party risk assessments that you have

9:54

to do for some of the larger firms,

9:57

but also having the CIO offices or their

9:59

security teams coming. in and really kicking the

10:01

tires, getting into our source code even to see

10:04

and verify, trust and then verify on

10:06

these things. And so I think that's

10:08

a really critical piece there. And

10:10

I don't know if it's verboten or not, but

10:13

if I can share my screen, I can kind

10:15

of show something really quick if that's OK.

10:17

Sure. Sure. That's sweet. Knock yourself out.

10:20

I mean, if you're listening on the podcast, this

10:22

isn't going to knock your

10:24

doom and spin. Oh, yeah. Yeah. I will

10:27

try to narrate, yes. Yeah. And

10:29

so what I've got here is basically kind

10:31

of a high level architecture diagram of how

10:33

our system works. And kind of at

10:35

the top of this are a series of workflows. And

10:39

the things of that would be like a

10:42

deposition summary, a trial and hearing transfer summary,

10:44

a case summary. And that sits on top

10:46

of what we call our sort of workflow

10:48

engine. And what the workflow engine does is

10:51

it securely ingests this data from our customers

10:53

and then puts it in a variety of different

10:55

places, whether it's

10:57

an elastic search or a Bragg database

11:00

or a vector database, such

11:02

that when we ask the questions that

11:04

are done by these workflows, which are

11:06

essentially sophisticated prompts, prompting chains that we

11:08

do, the workflow engine knows where to retrieve

11:10

that data, how to do it, how to verify that it's

11:12

the right data, all those kinds of things. And then the

11:15

second piece is that sits on top of what

11:17

we call our LLM routing engine. And

11:19

early on, we knew that we kind of wanted

11:21

to be the Switzerland of providers in this space.

11:23

In other words, we want to be cloud

11:26

LLM and then in the very near

11:28

future region agnostic. And

11:30

the reason that's important is our customers don't

11:33

know or care what models we're using. What

11:36

they're doing is signing a deal with us that

11:38

we're going to deliver what they need at a

11:40

decent price performance, that it's going to be accurate,

11:43

secure, private, all those things. And then what

11:45

we do is our LLM routing engine allows

11:47

us to essentially do that across a range

11:49

of different providers. So we'll never

11:51

be holding to any one provider. And like

11:54

we said earlier, we're in the first inning of this

11:56

thing, and we don't

11:58

know who the winner is going to be. And there might

12:00

not be a winner. So this ability to be able

12:02

to pick and choose the different models for specific things

12:04

that they're really good at is absolutely critical. The

12:07

last thing I'll mention, and then I'll sort of stop

12:09

here, but is that all of

12:11

these LLM models have something called

12:13

content filtering built into them. And that

12:16

content filtering is designed as a CYA

12:18

mechanism to make sure that the

12:20

big players are protected against or

12:22

protecting sort of the general public from doing

12:24

untoward things with their models, like making meth

12:26

or building bombs, like that kind of stuff.

12:29

But when it comes to law firms, they

12:31

actually have to deal with some really sensitive

12:33

topics, things like hate

12:36

speech or law-making mesh. Or

12:39

making meth. And we

12:41

actually even had one of our, we just

12:43

posted this story this week, but Lisa Peck

12:45

is a civil rights and employment attorney in

12:48

Northern California. And they just won a $20

12:50

million verdict. And it was an

12:53

employment law that had a bunch

12:55

of racial undertones in it against

12:57

Stanford Health. And it

12:59

was actually really, really powerful the way that

13:01

it worked. But in

13:03

any case, it's exciting

13:06

to see how these solutions can solve

13:08

these problems. And so we've

13:10

been able to figure out how to deal with that content

13:12

filtering in such a way that we deliver the right result

13:14

to folks, even if it is

13:16

sensitive content. And again, those are the kinds of

13:18

things that I think a lot of folks aren't really thinking about quite

13:21

yet. So yeah. So just to jump in

13:23

there, I just want to understand a little bit more

13:25

about the blog post that you previewed there and what

13:27

K-SPARK's participation or involvement was. But I think to make

13:29

the point, I think what you're

13:31

raising is super interesting because there is

13:33

a great deal of conversation about alignment

13:35

and making sure that these things, whether

13:38

it's less so about concerns

13:40

about hallucinations and more about saying, like you

13:43

said, reasonable things and not insulting someone or

13:45

saying stuff that they're not supposed to. Any

13:47

number of times you might have asked ChatTPT for

13:49

instructions to do something that is borderline

13:53

or could result in borderline content, and then it shuts you down.

13:56

So given that part

13:58

and parcel to lawsuits. and

18:00

how this is gonna change litigation,

18:02

especially in real time

18:04

trials are gonna change, I think

18:06

significantly here, so. This

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scales. that

24:00

happen annually and it's a huge, huge business.

24:03

It's a huge market, but these attorneys

24:05

to take a deposition as an attorney is

24:07

actually a process. It takes you several years

24:10

to get good at it, especially a contentious

24:12

deposition. And so one of the things

24:14

that we've seen is folks who can take real

24:16

time depositions and then transcribe those. And

24:18

let's say before you go into the deposition, you

24:20

know there's 15 questions you wanna have asked and

24:22

answered and the way the depositions work is you

24:24

get one bite at the apple. You get to

24:26

ask those questions and then that's it. You don't

24:29

get to go ask later. So if

24:31

you're an attorney that's a junior attorney and it's

24:33

really contentious and you sort of lose your sense

24:37

and you didn't get those 15 questions asked

24:39

and answered, guess what, you're kind of hosed.

24:41

So imagine if you start at 9 a.m.

24:43

it goes till noon and you have

24:45

a lunch break and you could feed in that transcript

24:47

and then you could compare it to the 15 questions

24:49

and the AI could come back and say, oh great,

24:51

you're nine of the 15. So you got

24:53

six more that you need to get answered after

24:55

lunch and so now all of a sudden

24:58

we can up level attorneys that wouldn't normally

25:00

have the skills to be able to do these things.

25:02

And to me that's very exciting. Not only is it

25:04

a job satisfaction thing but it's also helps them kind

25:06

of up level to a certain extent. And

25:09

more importantly, yeah, sorry, go ahead. Well,

25:12

I was gonna say just like one more on that, I guess that topic,

25:15

which is around, I guess like talent and how

25:17

the field itself is changing. One thing that I

25:19

think Brian and I have been witnessing and observing

25:22

as we've been talking to other founders going into

25:24

other spaces, of course, where AI is starting to

25:26

be deployed is just hiring is

25:29

like sort of, I mean, I suppose

25:31

this is like the meme and the reality going

25:33

around, but that people can't hire enough staff,

25:35

talents, they can't keep them, they can't retain them,

25:38

whether it's because now there's a whole new world of influencers

25:40

or whatever it is, it seems

25:42

as though like you said about the court reporters that

25:44

there's going to be deficits in

25:47

certain roles. And so the need for

25:49

AI to actually perform those duties is

25:51

becoming more and more significant. And

25:54

I guess the other thing that I would add to that is that you

25:56

also have in some cases, older

25:59

or more, I

30:00

mean, we probably have one of the original

30:02

relationships. Yes. Yes,

30:04

exactly. Back when you were at the Oregon

30:06

State University running the computer lab, the open

30:09

source lab. Thank you. Back in 2004.

30:13

So, you know, we, we go back 20 years and, you know, we've seen each

30:17

other through a number of different eras and

30:19

waves of technological development

30:23

from open source into, you know, I was a advisor

30:25

to urban airship, which is now just airship, I believe.

30:29

And essentially is focused on the

30:31

productization of push notifications once, you know,

30:33

the iPhone came out and provided a

30:35

solution to replace SMS. The

30:38

reason why that I think is relevant is

30:40

because, you know, Scott and I have been

30:42

in these moments of transformation

30:44

of, you know, basic set

30:47

of, you know, behaviors

30:49

and norms where, you know, to, to sort of

30:51

invoke McLuhan, the idea is to take an existing

30:53

set of, you know, media content and move it

30:55

into a new one.

30:57

And it's just more efficient. And so in a similar

30:59

way, you've got these depositions and it's going to be

31:01

more efficient to process them. And yet the transformation doesn't

31:04

actually happen until you're a few years in. And because

31:07

you've built on the existing

31:09

substrate or set of behaviors that exist, you

31:12

can then start to make subtle tweaks and

31:14

changes to process. So

31:16

my question to you is, you know, as

31:18

you're seeing this, you have this interesting challenge

31:21

to, on the one hand, deliver a product and

31:23

a service and a tool that meets the

31:26

legal profession where it is currently, you know, like

31:28

you said, you're not going to replace the whole case management system, you

31:31

know, one and done. But

31:34

over time you get to redefine

31:36

the way that that work actually is executed.

31:39

So when you think about, can you play out case

31:41

mark over the next several years, you know, one,

31:44

how do you see this moment, you know,

31:46

with AI being different from past technological

31:49

revolutions in terms of you building product?

31:51

And then two, how do you

31:53

see the industry, the

31:55

legal profession sort of

31:58

changing, assuming you're successful? Yeah,

32:01

I mean,

32:03

history doesn't repeat itself, but boy

32:05

does it rhyme. I'll

32:08

tell you what, I just feel like we're going through

32:10

what we saw with the original dot

32:14

com bubble, web 2.0 that you and

32:16

I lived through, Chris, mobile 2.0. To

32:19

me, what's happening is a lot like what happened with

32:21

the iPhone with respect to AI. What

32:24

I mean there is, AI is having what I would

32:26

call an iPhone moment. We

32:28

all had mobile phones in our pockets when the iPhone

32:30

came out, but when we saw the iPhone for the

32:32

first time, we said, oh, this is

32:35

what a mobile device is supposed to be. While

32:38

there were people who had 20 years of experience

32:40

with mobile when the iPhone came out, that was

32:42

all out the window. AI has

32:44

been around for 40 years, but when we all

32:47

saw chat GPT, we said, oh, this is what

32:49

AI is supposed to be. What

32:51

that does, just like with the iPhone, is it creates

32:53

a moment in time where people are saying, oh, my

32:55

gosh, I have to have that. I have to have

32:58

that. That creates an opening for a

33:00

company like ours to create that as a wedge. We

33:02

have an answer for you for your AI solutions because

33:05

that's what we've said we can do.

33:08

Do we have a specific product or feature or point

33:10

solution right now? Yes, we do,

33:12

but really, the way to win in this

33:15

to me is to build a

33:17

partnership with customers over time because you're navigating

33:19

what is a disruptive cycle that will take

33:21

a decade to accomplish. This is exactly what

33:23

we saw with Urban Airship. It's

33:27

been very, very interesting to see

33:29

that as it relates to

33:31

AI and this moment in

33:33

time with legal and, anyways,

33:36

to watch how this is playing out. Again,

33:38

the other thing that's also very interesting is there's

33:41

a lot of companies raising a lot of money

33:43

at valuations that are ridiculous and they don't have

33:45

product market fit and they probably won't get product

33:48

market fit. Me

33:50

having gone through this a gazillion times, I'm

33:52

shouting at the top of my lungs like,

33:54

oh my God, we're really doing this again?

33:56

But again, that's how these cycles go and

33:58

that's okay. But when

34:00

we think about where we wanna be or

34:02

where we think this is gonna go is, I

34:05

see a lot of people saying like, we're gonna be

34:07

this AI assistant for law firms and

34:09

you can ask it questions and it's just gonna do things. And

34:12

what you end up usually with is this sort

34:14

of mile wide and inch deep solution that doesn't

34:16

really solve specific problems. And so what we said

34:18

is, wait a minute, what if we can solve

34:20

from the bottom up? In other words, we can

34:22

solve these specific discrete tasks really, really well and

34:24

we'll get them bulletproof because we're gonna throw thousands

34:26

and thousands of tries at it and we're gonna

34:28

use it and all these people are gonna use

34:30

it. Such that when we do layer

34:32

on that assistant down the road, we

34:34

can then look at a case folder and say, okay,

34:36

well we see some pleadings in there, some transcripts, some

34:39

medical records. Here's our suggestion. We think you should do

34:41

some deposition summaries and medical chronology. And then by the

34:43

way, we'll sum the whole thing up with a

34:45

case summary report that you could forward on to somebody.

34:48

And oh, by the way, we have a couple of

34:50

next best actions you should take because there's a couple

34:52

of filings that are due in here and you should

34:54

check those out. Right, so that's where I think this

34:56

is gonna evolve. I mean, that's what I'm talking about

34:59

there is five years of work, right? And

35:01

it's not gonna happen overnight and people aren't gonna adopt

35:03

or trust it overnight, but that's where we're gonna kind

35:05

of get, I think. And just

35:07

like when the first push notifications got sent,

35:09

I immediately saw, I was like, oh wow.

35:12

And it was companies like Starbucks that said, okay,

35:15

yeah, cool, we have a mobile app and in our

35:17

mobile app, we have our menu. But

35:19

what they saw was, oh wow, this is

35:22

gonna change how we do ordering, how we

35:24

do tipping, how we do stored value. Like

35:26

all those things that have a fundamental change

35:29

to your business that manifest themselves in an

35:31

app, but really mean you have to change

35:34

how your store works and how, like all

35:36

those things. So that's the transformation that Legal's

35:38

about to go through right now and it's gonna be

35:40

painful and it's gonna be hard, but

35:42

those who navigate it and find the right partners to

35:44

help drive that are gonna come out the other side

35:46

way, way stronger and

35:49

way, way more profitable in my opinion, so. You

35:54

mentioned that you're attorney adjacent and you've

35:56

gone into a little bit of your

35:58

background, but can you give me. Any

36:00

sort of the inception of the

36:02

idea of this company, maybe touching

36:04

on where you were when

36:08

you started working on this company and

36:10

where the light bulb moment came from? Yeah,

36:13

I mean, I think so just

36:15

on me, I mean, I'm a serial entrepreneur.

36:17

I cut my teeth at Amazon turn of

36:20

the century. I have the dubious honor of

36:22

being on their Y2K team for

36:24

what was basically the biggest nothing burger

36:26

ever. Was really active in

36:28

a bunch of open source, open technology stuff. Just

36:31

like Chris said, he and I were there when Mozilla

36:33

spun out of AOL and we

36:35

both helped kind of get Firefox 1.0 out

36:37

the door. Then again,

36:39

we partnered up on making sure that

36:41

OpenID and OAuth and pulled Facebook, Microsoft

36:44

and Google together to say, this is it.

36:46

And then we created the OpenID Foundation, which

36:49

I think I was the chairman for a little while and then we

36:51

let that off into the world and that's created a really awesome thing

36:53

there. I started a company called Urban Airship to

36:56

do push notifications. I really moved over to the

36:58

business side to sort of build, scale and sell

37:00

B2B enterprise SaaS companies. After

37:02

that, my co-founder and CTO, who I still

37:04

work with this to this day, Steven Osborne, we

37:07

started a point of sale system for the cannabis industry that we sold

37:09

in 2017. Most

37:12

recently, we sold a transparent ledger for

37:14

physical assets company. So it

37:16

was a blockchain company, except that instead of saying, if

37:18

you build it, they will come. We

37:20

actually had a customer who had sports memorabilia that

37:22

we were doing anyways. We sold that last year,

37:24

ended up being an exit out to Fanatics because

37:27

our customer got acquired. So we got kind of

37:29

swept up into that. And

37:32

so I had a team and this

37:34

is literally June 1st of 2023. So

37:37

just over a year ago, and we

37:39

were kind of looking at what we're going to do. And

37:41

my wife, obviously being an attorney, she runs a firm

37:43

here in Portland. They do Oregon, Washington, Idaho,

37:46

insurance defense. So

37:48

they have big retailers and

37:52

let's just say, driving or vehicle

37:54

related businesses, they support all

37:56

those. And so she kind of

37:58

jokingly said, hey, why don't you do something? something that helps me for

38:00

once. Like a lot of attorneys, she

38:03

had the first experience which was, like I do when

38:05

I'm launching a new company, the first thing I want

38:07

to do is get product into market so we can

38:09

increase our pace of learning, so we can figure out

38:11

what the heck's here. We immediately

38:13

said, let's launch a Word and Chrome extension,

38:16

a Word add-in and a Chrome extension. So

38:18

we found the open source solutions out there,

38:20

we hired the devs, and then

38:22

we launched something within a month, basically about four

38:24

weeks. We learned really quickly that

38:26

attorneys don't want those. They forget

38:29

about the add-in, they hate paying for software

38:31

that they might not use that month. The idea of

38:33

SaaS to them is just, they just

38:35

don't understand it because they bill for the time that they

38:37

work, so they just don't understand it. In

38:40

the fall, we tried some fine-tuning of models

38:42

for firms, but then we

38:45

were left with this whole concept of,

38:47

okay, cool, we fine-tuned your model. Here's

38:49

a prompt window, go ahead and start

38:51

use AI, and they're like,

38:53

what the hell do I do with this? That

38:55

let us down launching what we call legalpromptguide.com,

38:57

which is a really simple solution. It's a

38:59

freebie site there, it helps attorneys figure out

39:02

how they're going to do prompting, and

39:04

really understand, just like I took a generation for

39:06

folks to figure out how to get those Google

39:08

searches right to extract what you want out of

39:10

Google, the same thing is true like 10x for

39:12

chat, GPT or

39:15

just chatting with any generative model.

39:18

That's when we realized, oh, we have to take what

39:21

we learned from all these prompting

39:23

and turn those into easy buttons,

39:26

leveraging these methodologies and the things that we've learned to

39:28

be able to get the most out of the LLMs.

39:31

That's when we really launched in

39:33

earnest in January, and then we had a bunch

39:35

of legal tech players approach us saying, hey, we

39:37

want to license your stuff, which

39:39

then led to, oh, we need an API and

39:41

now we've got folks connecting to the API. It's

39:44

literally just taken on a life of its own

39:46

now, and now we're stamping

39:48

out all kinds of workflows for

39:51

folks to be able to really increase our scope,

39:53

and then anybody who's plumbed up to our API,

39:55

guess what? They get to have access to any

39:57

of the workflows that we have. has

40:00

this multiplicative effect right now.

40:02

And we're kind of have landed on this, you know,

40:04

AI as infrastructure play. And

40:07

then, you know, from a pricing standpoint, we've tried to

40:09

be really aggressive. Instead of saying, here's

40:11

how much it costs for an attorney to do it,

40:15

we're going to charge just a little bit less, we actually

40:17

are going the other way, which is we know what our

40:19

cogs are, and we're going to tack on a margin that

40:21

leaves a lot of room for people to resell our stuff.

40:23

And that's working really, really well right now. And

40:26

so yeah, that's kind of the hook for

40:28

why we landed on it. And then my

40:30

wife's been really instrumental in helping

40:32

us kind of craft the some of the

40:34

initial workflows that we did. Because

40:37

the key with the LLMs is they have

40:39

the answers, you just have to

40:41

know how to ask the right question. And the

40:43

answer can't the prompt can't be pretend you're a

40:45

lawyer. What you have to do is

40:47

ask a question like a lawyer does. And then

40:50

it will respond with a response that a lawyer would

40:52

expect to see. And so those are the

40:54

tricky things that we don't have the experience in. And that's

40:56

why we lean in on my wife and a couple other

40:59

trusted advisors in that front. So

41:01

yeah. It's funny. I'll just comment

41:03

on a question. One

41:06

of the reasons why we invested was along the lines of

41:08

the AI varietals thesis that Brian

41:11

and I work from. And that essentially is

41:13

this concept of bringing together

41:15

some subject matter expert with

41:18

AI engineering or the

41:20

use of generative AI. And the fact that

41:22

obviously your wife is the subject matter expert

41:24

in this case sort of allows you to

41:28

ground in real truth the

41:30

way in which you're bringing generative AI

41:33

into a specific context where there are

41:35

specific requirements. And those requirements come from

41:37

language. They come from a set of

41:40

expectations that people in the field, you

41:43

lose so much credibility if you answer the question in any

41:45

other way that is not sort of legalese. And

41:47

so it's not enough to,

41:49

I think, like you said, like pretend that

41:52

you're a lawyer and then create a prompt.

41:54

There's a whole lot more that goes into

41:56

that, which is around culture and norms and

41:58

why communication happens to certain people. way. And

42:00

I think it's valuable just to

42:02

keep that in mind from a product design perspective is

42:04

that when you're designing something in the generative AI space,

42:06

you have to match the language of the person that

42:09

you're actually interacting with. So the

42:11

question that I have is about maybe the, if

42:16

not the metaphor, the conceptualization

42:19

of bringing the

42:23

output of an LLM into the legal context. And what

42:25

I mean by that, or what I'm getting at is

42:27

the increasing

42:30

interest in agents or having

42:32

kind of like AI employees

42:35

that work alongside someone else versus

42:37

let's say like a copilot, which you treat as

42:39

like a chat bot, which you know is an AI

42:42

and sort of sits alongside, let's say a document versus

42:44

a set of workflows that

42:47

are almost like macros, but lit up, you

42:49

know, for like the 21st century with generative

42:51

AI. And we seem to be at

42:53

this moment, you know, you mentioned AI as infrastructure, but

42:56

there's still a question of how someone

42:58

chooses to invoke these like

43:02

services. And we're, I

43:04

think in some ways struggling with the right interface for

43:06

this. So my

43:08

thought and question is kind of about that, whether

43:11

agents are relevant metaphor for you, whether

43:13

it's you'll have multiple different sort of

43:15

paralegals, but they're all powered by generative

43:17

AI. And that eventually, you

43:19

know, the junior like lawyer goes

43:21

to one that is a specific

43:23

subject matter expert, or where you

43:25

imagine these workflows are the right

43:28

concept and framework for delivering casemark. And

43:30

you're going to stick with that, because that's the one that people

43:32

seem to understand. And you're going to go forward with that. Yeah,

43:37

I think in the near term, what we're finding

43:39

is what's the easiest way that people can kind

43:41

of rock this, such that

43:44

they feel confidence, one that they can test

43:46

it, try it, get a result, and then verify

43:48

that result quickly and easily. And I think that

43:50

sort of manifests itself in the easy button today,

43:54

that idea of a changing paradigm

43:56

where you'd actually trust an assistant to

43:58

do those things or co pilot. even. I

44:01

think that's it's going to be how

44:03

it happens. It's just those things never

44:05

happen overnight. You don't adopt those paradigms

44:09

overnight. I mean, if you think about any new paradigm,

44:11

I mean, the only one that I can

44:13

think of that took hold overnight was

44:16

sort of like the pinch to zoom or like

44:18

any of the sort of touch interfaces, but you

44:20

weren't doing anything new there. You were just doing

44:22

something that was obvious that you would do. Well,

44:25

it was like a digital version of something that kind

44:27

of already existed to some degree. Yeah, exactly. And so

44:29

I think that I think that, you know, for

44:31

us, we just tried to do the lowest, easiest

44:34

way for people to get in and use it.

44:36

I think that will evolve over time,

44:38

but we always have to continuously check

44:40

in with our customers on this. The other

44:42

thing is that we've learned too, as we

44:44

sort of track the sessions and watch users

44:46

and their behavior of our solution. I'm

44:49

always astounded at how easy

44:52

we are, how simple we have to make the

44:54

interface so that they understand it because

44:57

these attorneys, you know, a lot of the times can

44:59

be sort of neophytes. And

45:01

that's a problem. That's our problem.

45:03

It's not their problem. And so

45:05

often I just keep seeing so

45:08

many companies that are building software that

45:10

I know attorneys will never use, or at least, you

45:12

know, even this current generation, the

45:15

sort of youngest generation, they'll probably be able

45:17

to make it work. But those aren't the

45:19

folks who are making the buying decisions or,

45:21

you know, have the budget to purchase, you

45:23

know, the line items that would require for these things.

45:25

So yeah, it's very,

45:27

very interesting to watch how this

45:29

whole thing is shaking out for

45:32

sure. Yeah. During

45:34

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45:36

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45:38

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45:45

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45:47

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48:01

a lot of the people listening to this

48:03

episode right now are listening for, if

48:06

I was in his shoes, what

48:08

would I do? Would I make

48:10

decisions this way, that way? As

48:15

I'm looking at the tech meme back end right now,

48:18

there literally seven hours ago was

48:20

another legal AI company

48:23

that announced arrays. This

48:25

is a hugely competitive space. In

48:27

fact, by the way, I got

48:29

out of that that legal tech

48:32

startups have pulled in $356 million so

48:35

far this year. Now that's down slightly

48:37

compared to last year. That's

48:40

from GeekWire, by the way. But so, OK, number

48:42

one. Oh, and it's twice that

48:44

now with Harvey closing that

48:46

round, right? OK, well, so number

48:48

one, hugely competitive space. Number

48:51

two, a lot of times VCs

48:54

will encourage startups to raise

48:57

big rounds because it sort of freezes

48:59

the market. Like if somebody

49:01

has hit a billion dollar valuation, then

49:04

that stops other people from being able to

49:07

raise. So in

49:10

this broadway or

49:12

as narrow a way as I can

49:14

ask this, how are you

49:16

thinking about being in a super competitive space

49:18

where, A, you've got a competitor

49:20

that people would look at the market

49:22

and say, further ahead, bigger valuation? You've

49:25

made the argument also that this

49:28

is early innings. But number two, everybody,

49:31

again, I'm looking at the tech name back end, the

49:33

amount of legal AI startups.

49:37

How many lawyers are there in the United States? That's

49:40

a good question. I want to say it's somewhere in the

49:42

neighborhood of like 800,000 attorneys,

49:45

and that's going to grow by like 18% in

49:47

the next 10 years or something like that. So

49:49

a big market, but also that's

49:51

why everyone's going after it. So as

49:54

a founder

49:56

of a company in a super

49:58

competitive space, How

50:01

are you thinking about that? Is it

50:03

just, we got to

50:05

focus on the product and everything

50:07

will work out? Or is

50:09

there strategically as you're building

50:12

this company, how big is the team right now? We're

50:15

10 people right now. Yeah. As you're

50:17

building the company, do you make decisions

50:19

based on that that you wouldn't make

50:21

if you had a space to yourself?

50:23

You know what I mean? Well,

50:27

I think if you have a

50:29

space to yourself, you don't have a business. Right?

50:32

Or you're doing something so crazy that no one

50:34

knows it's a business yet. That's true. That's true

50:36

too. But that's so rare because even

50:38

the idea of sort of monkeys, infinite

50:42

number of monkeys in front of an infinite number

50:44

of typewriters, the

50:46

odds are there's going to be a lot that are really, really close to

50:48

each other. And so I feel like that's

50:50

kind of where we're at right now. And the answer

50:53

to this is, you have to

50:55

kind of play the market a little bit, but also make

50:58

sure that you're focusing on the fundamentals. I mean, we did

51:00

this at Urban Airship, which was let's make sure we get

51:02

a sales motion in place such that we

51:05

know how and who we're selling to. And we

51:07

have a strong relationship with those folks. And let's

51:09

lock that up. And we

51:11

did raise, I don't know, all of us there, we

51:14

raised about 50 million, which is like chump change now.

51:16

Our Series A was $1.1 million, which

51:19

is like a lack of investment. That's

51:21

not even a pre-seed now. And

51:23

so, but for us at

51:26

Urban Airship, what we did is we said,

51:28

let's get that sales motion right. Let's make

51:30

sure we have a really solid product market

51:32

fit. And then we're going to

51:34

watch this market. And what we're going to do is

51:36

we're going to identify those players that either overraised or

51:39

couldn't find product market fit or ran out of gas. And

51:41

then what we did is we acquired them for pennies on

51:43

the dollar. And we got great teams in tech, and

51:46

we were able to fold that into our sales motion.

51:48

And the uplift was anywhere from 15 to 30% in

51:52

net new growth from a

51:54

business perspective. And I think the same thing

51:56

is going to happen here. And that's how we're architecting our

51:58

business. It's one of the reasons we're- we only

52:00

raised the 1.7 million is we're in

52:03

this, we wanted to do a small amount to prove

52:05

out some things that we think we're proving out right

52:07

now. And then we'll

52:09

likely raise again, but

52:11

we don't have to because we're now throwing

52:13

off a bunch of cash and building an

52:15

interesting compelling business. But as we

52:17

hit 18, 24 months, my gut says that

52:19

this is gonna go faster than the original

52:21

sort of SaaS cycle because AI

52:23

is moving so fast that we'll be able

52:25

to look out at the landscape and say,

52:27

well, where do we have gaps? What could

52:30

we fold into our sales motion such that

52:32

it'll, we'll have some

52:34

uplift there that allows us to continue

52:36

to grow and scale and actually turn us into

52:38

not just an interesting

52:40

company, but a brand that people

52:42

will depend on for legal in

52:44

general. And that to me is where

52:47

it gets really interesting because AI is gonna become a

52:49

feature. Right. Without

52:51

putting words in your mouth, what it sounds

52:53

like you're saying to me is let other

52:55

people get headlines with big raises. Let

52:59

20 other people raise rounds.

53:01

We'll make the headlines down the road

53:03

if we've executed on the sales, because

53:06

then we'll make the headlines because our raise

53:09

will be based on the revenue that because

53:11

we've executed on the plan. Yeah,

53:14

I think that's about right. But I think there's

53:16

also some element of, you have to play the

53:18

market a little bit too. Obviously,

53:21

after we close the round and now

53:23

it's, there's so much interest in the

53:25

space that we get investors constantly pinging

53:27

us. And I take those

53:29

calls. I have those conversations with those

53:31

folks and there's always interest, which is

53:33

great. That's great. But again, we have

53:35

to focus on execution. And I know

53:37

it sounds boring, but that's the critical

53:39

pieces right now that I think is

53:41

really important. Now, I think the

53:44

unfair advantage that we have is as a team, we've

53:46

all worked together. It's a bunch of airship folks that

53:48

have come together, urban airship

53:50

folks that have are putting this together.

53:52

And we've all scaled companies like this

53:55

before. So a lot of

53:57

these startups have the challenge of market

53:59

headwinds. and implementation and all those

54:01

things, as well as learning as they go

54:03

on how to scale a company. Team dynamics

54:05

too. The team dynamics are

54:08

really helpful. We can shout at each other or get

54:10

angry and have a disagreement, and guess what? The next

54:12

day we wake up and go, okay, cool, we solved

54:14

that problem. Because

54:16

we have that relationship, I think that sets us

54:18

apart as well. I

54:21

think it's one of the reasons we've been able to accomplish so much in just

54:23

a year. Especially

54:26

when I look at some of the

54:28

folks that are in our same space or

54:30

even in the same portfolio companies like a

54:32

Gradient and others, the

54:35

amount we've accomplished and the challenges they have

54:37

around just what I consider simple stuff

54:39

around scaling, they're struggling with. But

54:41

I always give feedback on those things and say, hey,

54:43

maybe you want to think about this. That

54:46

to me is also really fun to help

54:48

those other companies. Cool.

54:51

You mentioned the pre-seed round,

54:54

which was led by Gradient, which is Google's

54:56

seed fund. We are

54:58

honored to be a part of it as well

55:00

with the Ride Home AI Fund and the Ride

55:02

Home. We're excited. So happy you all are involved

55:05

without a doubt. Very excited. Again, if

55:07

you want to learn more as you're listening

55:10

right now, it's casemark.ai.

55:13

But also, if people are listening and

55:17

want to learn more or want to get

55:19

involved, are you hiring?

55:23

Do you have an ask for this audience

55:25

that you never know who's listening that might

55:27

have something that they can deliver for you? I

55:30

mean, we're definitely hiring. We're

55:32

looking for folks on

55:34

the engineering side of things without

55:37

a doubt, especially DevOps, Site

55:39

Reliability. We're having some

55:41

what I call wonderful problems around scaling

55:43

in the sense that this

55:45

is going really fast and so we have

55:47

to figure out how we're going to scale

55:49

those things. So always looking for folks who

55:52

are interested in a serious going

55:54

concern that has the fun problems of a

55:56

startup which are scaling and those

55:59

kinds of things. If you

56:01

have folks that are running, whether it's an

56:03

insurance, friends that are running insurance defense firm

56:05

or doing transactional work like

56:07

personal injury stuff, then hey, they should point them

56:09

out in our direction, and have them check out

56:11

case mark.ai. We offer

56:13

up a little free plan where you can test it

56:16

out with a couple of different free summaries.

56:19

We find that when people see the free

56:21

version and they try it against it, especially

56:23

a deposition they've taken themselves, and they see

56:25

the summary, they're actually really blown away. That

56:28

would be my ask, was just check it out and spread the word.

56:32

By the way, according to Claude, there's

56:34

1.3 million lawyers in the US. Oh,

56:36

so I was wrong. I was going

56:38

off of what I'd seen in my

56:40

last Gartner report, which I probably mis-voted.

56:42

Great. Claude must

56:44

have it right. I was going to say AI to the rescue.

56:48

They have references, so I believe

56:50

it. Yeah. That's true. That's fair.

56:52

Fair enough. Again, case mark,

56:54

case mark.ai. Scott,

56:56

thanks for coming on and telling

56:59

us all about that. Chris,

57:01

thank you for joining me as well. Yeah.

57:04

Thank you, Brian. Thank you, Chris. Thanks so

57:06

much for having us. Really appreciate it. The

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