He fired all his customers. Then built a $1B startup in 2 years. | Jay Madheswaran, Co-Founder of Eve

When do you decide to go all in on this product and shut down all your other business lines?

Pretty quickly. So we still had customers coming in for the old product and we were getting a new pipeline going for the new product. And keep in mind, the product is barely there. And as a result, you have to be very careful qualifying the early customers to make sure they're aligned with the larger market. But what we noticed during that process was 40% conversion rates from cold outreach into demo requests.

Wow.

When we sent the we're shutting down the service product is when we got probably like really strong signs of product market fit for that product. Because we saw other examples of cases where they were like, no, don't take it away from me. What do you need? That's, that's always an option for people in the future if they want to find product market fit, put in hands of people and then take it away and see who complains the most.

How fast do things go? Like, how fast do you hit a million? How fast do you hit 10 million.

Arrow the first quarter, I think we hit a million in ARR already for that product and the two months after that we had another million and a month after that we had another million.

You probably 10x in a year. Ish. From 1 to 10.

We've been growing a lot. I think the Official number is 800%, but I think it's, it's ever increasing. That's Product Market Fit.

Product Market Fit.

Product Market Fit.

I call that the product market fit question.

Product Market Fit.

Product Market Fit.

Product Market Fit.

Product Market Fit.

I mean the name of the show is Product Market Fit.

Do you think the Product market fit show has product market Fit? Because if you do, then there's something you just have to do. You have to take out your phone, you have to leave the show five stars. It lets us reach more founders and it lets us get better guests. Thank you.

Jay.

Welcome to the show, man.

Thank you, Pablo. Excited to be on. I loved watching your episodes.

Thanks, man. So you started, I mean, you started this business, I guess five years ago. There was a bit of a pivot going on. But the last few years seem to have been completely explosive. I mean, you raised a series a not long ago, earlier this year from a 16. You raised a hundred million dollars at a billion dollar valuation a couple of months ago. So things seem to really be going up and to the right. So we'll talk about all of that, including the pivot. Because I think what you start with and how you get to where you get to is just as interesting as what it is that you're doing today. So maybe as a first question, like tell me a little bit about, you know, yourself, your background and especially what you were doing in you know, 2018, 19, 2020, like right before you started Eve.

Yeah, absolutely. Thank you for asking me. So I mean I'm an engineer by trade largely so I joined Facebook before the ipo, worked on a lot of their consumer facing products and then joined Rubrik which I saw you had as co host. The Glean founder. AJ Was my manager over there.

Arvind from. From Glean was also. Was also here.

That's right, yes.

Yeah, the whole R Rubric mafia, man.

Rubric mafia is growing. It's pretty exciting actually. And that was a wild ride, right? Like from four people in a tiny room to 5,000. And I grew to at one point lead about a third of engineering. And 2017, 2018 is when I joined Lightspeed as a venture capitalist. I was leading their early stage AI investments back then 2018. It's crazy to think about back then. AI does not mean what it used to mean today.

That's right.

But weirdly, a lot of the same type of issues where every company was saying they were an AI company.

It was almost like the false start, like the first wave of AI pre gen AI was like the false start of AI where you talked about the things you wanted to do and most people couldn't actually do those things. And then all of a sudden now.

You can actually that's exactly what led me to start the company. So what I noticed was in 2018 one of the nice things of being in a platform like Lightspeed is you get to meet hundreds and hundreds of really exceptional talent all at the top of their field. Thinking about next gen ideas and what you start developing is a trend line of what's actually going on or at least what potential future could be. And I think what I realized in 2018 is like, you know, AI is probably there, but this wasn't it. You know, like a lot of the use case you were seeing were, you know, supply chain, some sort of if then else statement claiming to be AI and the actual models to be trained to do even classification tasks weren't really good enough. Right. But what was happening was literally year over year they were getting good rapidly, especially in nlp. And I started seeing signs of real use cases in NLP actually pop up largely from, you know, there's a lot of data labeling companies that ended up doing a lot of training in nlp. But Also, some products started actually relying on the fact that transfer architecture and transfer learning with BERT allowed you to go from needing a billion records to train a classifier model to literally like 100 the next year to then 10.

Right.

I mean, obviously with ChatGPT these days, you don't even need a training data set in large amount of cases if you want to, you know, qualify sentiment or something.

Do you remember any like mini ChatGPT moments? Like, obviously ChatGPT is where everybody saw the possibilities. It sounds like you're describing well before ChatGPT, you seeing that things were on the come. I'm wondering if there were demos or products where you're like, oh wow, I can't. Interesting that this is possible. Do you know what I mean? That were kind of showing you a certain trend.

To me it was something very, very simple that blew my mind. Funnily enough, it wasn't even chatgpt like. But early on, one of the issues with computer vision machine learning is you would have to deal with, let's say, large volume of legal contracts or bank statements. And the typical way of extracting information from that used to be computer vision. Right? So you train models, you get them to try to recognize pixels and boxes basically and try to extract that out. And with bert, entity extraction is becoming a thing. So a model could know if that's a name or not a name, is it a number or not a number, is it what have you. Right, Like a dog or not a dog. And that alone made a dramatic improvement to extraction rates. So. Right. So you went from needing to train thousands upon thousands of documents to 10 documents, maybe to extract things like dollar amounts from invoice totals, simple things like that. But it's happening in really, really large volume. And the amount of effort required to train a model was just so cost prohibitive before it got reduced that tens of samples rate, that it just never made sense even for really high volume, repetitive grunt work. And that's actually what got me to take a big leap of faith and kind of bet on that trend line and basically make it possible to help anyone make machine learning models easier. That's kind of what the initial talk track was for NLP specifically.

Did you want to be a founder? You want one of those VCs that knew you wanted to operate or like, because that transition, as much as VC and startups are close by, obviously working hand in hand, it's not a simple transition. The life and the workday is very different. The risk reward is very different. Like, how did you think through Actually making that leap.

I think I'm still a builder and I always known that about myself. Even as an investor, I was actually building tools to help me prospect and identify resources and doing a bit more data sciencey work to help me do my job better. And I just can't help it, right? Like, forget building, I go make, I make my own coffee table. You know, it's stupid, things like that. I still build, build on. But part of my appeal to even joining Rubrik back in the time Pablo was I tried starting a startup within Facebook. Not that I had nothing else going on, but I tried doing that and I felt there was real gaps that I had in my own capabilities and that was starting to get fleshed out at Rubrik. I just love technology and building in general so much that being a VC investor allowed you to take multiple bets, right. And you get to kind of watch it from afar and still participate in the upside. And that to me was really intellectually stimulating. But what I can't get behind is like the level of conviction. Like I was really, really, really high conviction towards AI, probably more so than it was rationally smart to do. And given that conviction, I think that's when I knew this is not something I could just do by investing and focusing on a vertical to invest in. I wanted to do it, especially while I still had the operational chops to go do it. And that's ultimately what led to me kind of making that jump. Plus team was a really, really strong team of people. I used to work together at Rubrik was coming together and I was excited to work with them again.

What was like the first idea in 2020?

First idea. It's a good question. So the whole idea was around task automation at the time. So the idea was think like the early day RPA bots were very error prone.

Okay. I was going to say, yeah, yeah, rpa, yeah.

And the next level that we were kind of innovating on was how do you make it kind of do an rpa, like bot automation on NLP tasks? Right. That's how we got pulled into the legal industry, finance industry. We got pulled into multiple different industries. We were actually in multiple Fortune 500 companies deployed for those use cases during our time.

How do you get things off the ground? Like I assume being a VC was relatively easy to. I mean you had the team from Rubrik, but then raising you probably raised like a pre seed round or how did you kind of set things off?

That's right. So I think one of the nice things you do learn about being a VC is how to actually fundraise in general. Because VCs are fundraising all the time themselves with LPs and you're hearing a lot of pitches every day. And I think that really helped me put together a very clear perspective on the market and where we were going to participate, along with a really robust set of companies in that phase. Right. And that is our tactical advantage. At least when VCs go into it, you know, they want some level of greed to be clicked on. It's like, oh, if they do this, I can see why they would make a lot of money and make me a lot of money. And you have to then once you do that, reduce risk. Right. Basically show a through line between where you are now to how you're going to get there. And the more you can articulate it through a combination of storytelling team, past experience, diligence of the market, all of these things are kind of tools to help you make that case.

How much did you raise like in that prey run?

We raised 4 million initially.

And this was when exactly? In 2020, like before or after Covid?

This was, funnily enough, the week of COVID So the economy was shutting down.

And you're raising 4 million bucks?

Yeah.

Nice.

And I mean, talk about founding stuff, right? So the first few years was pretty interesting. And I remember our law firm didn't have a process. They relied on some documentation to be literally faxed at the time and they couldn't go to their office. So some of the closing paperwork and filing paperwork for the state so that we could get paid and get the EIN numbers, they all were delayed by a month or two. So we were having trouble paying ourselves and working for free the first few months.

We're going to walk through that whole storyline. But tell me a little bit about. You mentioned a few different industries. Maybe tell me a little bit about what you did in those early days. I mean, Covid happens. Who do you start working with? What are some of the specific use cases that you decide to tackle?

First we decided to go down high volume document processing. So that was kind of the initial starting point, which is CVE was pretty bad at doing these high highly variant doctypes. And what I mean by that are documents that have the same information but look very different from one another. You know, think contracts, right? So contracts all have probably a name, a signing authority and address and email, whatnot. It has a few important pieces of information.

It's crazy how much things have changed, by the way. Like this stuff is out of the box like post gen AI, like just put anything in. What's the name? It just knows. Right. But literally in 2020, we're probably five years ago, it was a big task to use computer vision to say here are two contracts, the names and dates and dollars are in different places.

Kind of like figure it out.

I mean that is not a simple problem to solve back then.

Exactly. And funnily enough, this is still not a solved problem, which is the crazy part.

Right.

So what if maybe going to eve. But one of the things we relied on is actually this architecture we built to continue to keep improving the models. That's where it gets harder. When you're chasing from 99% accuracy to 99.9. Genai still doesn't fully help with that. So you still have to kind of do more typical machine learning. But that groundwork helped us really set up well for making our E, which is kind of legal AI for plaintiffs. Much better because source of, you know, getting the documents right is really, really important. Legal, like trust is everything. And ocr, like silly things like ocr, information extraction is still a really crucial problem, especially when dealing with like medical records. Right. So if you get in an accident, go to a lawyer, they're going to pull medical records of where you got hurt, how much you owe in bills. So it's like all the old problems are back again. But it so happens that we'd spent many years building that tech, which kind of helped us out.

So you're working with like which law firm you're working with and how do you structure. Is it like a design partnership thing where they're sending you some contracts and you're doing pulling some information out? Like what exactly is that kind of first setup?

Like the very first customer legal side was dealing with very particular workflows. We had a few in parallel, but one of them was doing case analysis. So maybe some precursor for people to understand. Plaintiffs work very differently than normal lawyers. You're used to. These are plaintiff attorneys are things like people that are representing labor and employment, personal injury. And they don't really charge by the hour. They charge when they help you win a case. Right.

So if you contingency.

Contingency fees. Exactly, that's what it's called. And as a result, their business model is very different. So instead of retaining one client for longer, they're helping people that are in trouble and they have to source them, qualify them, see if it's a problem or not, and then decide to take them on as customers and then pour in the Hours of legal work in hopes that they get paid. That's kind of the work. So if you think about it, what happens is if the audience is familiar with typical sales process, they have their own lead gen funnel to closing a customer type of playbook. And at the top of the funnel there's significant volume. So when you're talking about leads, there's sadly a lot of people that are looking for legal help that today don't get it. So there's a surplus of leads. And as a lawyer, you just can't get on every single call. Right. But with certain practices like labor and employment, you're technically at full employment. So if there's anything litigatable or you're ODIN damages, things tend to happen at the edges.

Right.

And you know, this was like established by civil rights movement and you know, things from long time ago are what we still rely on today. And you're looking for exceptions to the rule that are still protected by the law. And that takes legal expertise to do. And the challenge they have is they can't apply this legal expertise at the call center.

Right.

So when you're initially calling them, it's just too expensive to put a lawyer on the phone to field those calls. That was the very first place where Eve was used.

Right.

So how do you help them feel this call? And now Eve actually literally talks to the client, which is kind of cool. Helps get them signed and qualifies them without violating any laws. Like you can't accidentally do unauthorized practice of law.

Just to be clear, this is eve, the postgenai product or even you were talking about before.

No, this is post gen AI.

Okay.

The first product was literally just transcribe to phone call into a transcript, which itself was considered mind blowing tech by most.

Yeah. When were you doing this?

This was 2023, like mid to late 2023.

Okay, so at least post like ChatGPT and even the transcription.

Yeah.

How do you walk into this? Like this is the interesting part I'm trying to understand is like there's a lot of B2B SaaS, companies stuck in B2B SaaS that are trying to. Everybody knows they got add AI, so everybody's doing something, but very few are able to make that full transition where it's like, oh, you're truly an AI first company now it seems like you guys have. So I'm trying to understand like before that October, November 2022 chatgpt moment, where are things at? What are you guys doing that allows you to then truly be AI first? Even in the second half of your.

Journey at 2022 and we were still a horizontal machine learning for NLP style startup with customers in legal finance actually everywhere supply chain and those were kind of the heavy verticals.

And where were you at employee wise? Revenue wise? Ish. Just to get a sense of scale.

We were small so we were about nine employees. I think a few million revenue two or three.

But you had a business. I mean even that 2, 2, 3 million is. It's a business. It's not like nothing's working. Let's might as well throw it all out. Like you've got a real sunk cost to worry about.

Oh yes, it was possible to keep going but what was becoming clear was you had to double down into a use case to derive more value. Right. So you had to get the ACVS hub hire and that was like a business LED call. But two, you know we're technologists, we also see where AI is going. We went from needing to train very complex NLP models to now you can just one shot things. And what that made it possible for us was actually to go deeper into workflows. If you think about where pre eve we were used before it was you would identify one very particular workflow that could be augmented by AI that's happening in very high volume. Spend a lot of labor on the data science side trying to get it to accurate enough that it makes sense to replace what might be one.

Can you go through one workflow for me just so I can make it tangible.

So if you're doing large volume bankruptcy cases or you know, credit accountants, right. What you get is you get a document from the court saying so and so owes X money and every single court has their own format for this. And then you have to go extract how much money and to whom is the money owed. And then the law firm would then take that dollars and kind of go collect the money somehow. Right. So go message people and collect it. That's a very concrete workflow. And here there's a number of problems where you have OCR issues. The actual amount place might be in different places. You have exceptions. The rules always, you know some judges will be like this much so and so for attorney fees, so and so for whatever else fees and then you have to add up the total yourself so you have all of these different variations. And that was something we used to train models for. And to go deeper in the workflow just means what is the actual thing they're trying to accomplish. Right. And in the case of plaintiff works it's to make a client happy, like they're wronged in some way. It's represent the clients, provide legal help to get them some sort of settlement. In civil litigation, it's almost always money is an outcome, right. You're getting some sort of payment for damages. And that is a very. That's a workflow and process. And that's a workflow and process which is composed of about 100 to 200 different mini workflows that involve people. Right. So what people were doing was the challenge is every single person is different. Right. Like every new person coming in, they have their own facts, they have their own problems, if you will. And you have to take that and convert that into those 100 bespoke workflows, which was economically unfeasible before LLMs. But with LLMs, we saw that we were actually able to go deep into the workflows and as a result provide more value for the firms and, you know, as a side effect, also get more value accrued for ourselves.

So this is a, this is a junction in the story. Like basically inflection point. I want to go as deep as possible on. I think it's critical. You're at this point, you got 2,3 million ARR. Let's say you've got nine employees and you're horizontal. Right. Like, you're not just in legal, you're cross finance. A few different verticals.

Yeah. And yeah, we had to decide to throw it all away. So which is.

This is exactly it. Right. I find that, that for what it's worth, this is the key. When you look at it from the outside in, like, everybody knows maybe the story of, you know, say, Netflix, you know, they used to do DVD rentals and they go to streaming and you look at it 10 years later and you read about it and you're like, well, wow. But also like, so obvious. Like, of course, you know, they would do that. Like, that's the thing to do. And then when I talk to founders who have an existing business line, but they're not at scale, like, they're not 50 million, rather 5 million in revenue, there is a genuine fear of letting all that go because you do worry about this shiny object type syndrome. You did that. Right. So I'm just curious, obviously the tech piece that I get, I mean, everybody would see, you know, what's possible gen and be like, But a lot of you might say, okay, let's stay horizontal, let's not burn our revenue. Let's just find a way to augment with AI versus throw it all away going, walk me through as much as you can how you thought about that, what some of the internal discussions were, and how you ultimately made that pretty massive move, which I think is a big reason for you being where you are today.

This is a tough call, right? I mean, I was up many nights for months probably before making this call. But what we did was kind of break down the decision into multiple steps. The only economically feasible way for us to continue growing to hundreds of millions of dollars in revenue was to go increase our ACV so our sales productivity go up and be able to continue to hire.

What was ACV before? But what were you trying to get it to do? You have a sense.

ACV was like tiny. It was like 6k or something. And we were trying to get it to 60k. Right, right. So that requires doing something different, at least on the qualification side, product side, somewhere. And that was kind of the journey we went on. And as a result, we just started working more deeply with customers to validate problems that they have. And we picked, you know, funnily enough, I think most people don't know this, but we actually launched a mini app for finance that went viral very minorly on Twitter.

What did it do?

It was called Fin. And what it did was it was the early days of, you know, call it. You dump in a earning statement of some kind and you can then just do live analysis on it. And it was useful, like, for a short amount of time. People were actually using it a lot.

This was one of the outputs of you talking to, in this case, finance customers and you seeing that this could be a potential product.

The challenge you have is we have a timeline. Right. So you don't have all the time in the world to make this call. We're trying to make a decision on this a month from when we decide that, oh, we gotta go double down somewhere. We don't have that much time left.

What was this time, by the way? Is this late 2022? Like, what's the timeline at this point?

This is late 2022. This is probably December. So.

Right. ChatGPT happened. You guys are like, oh, my God, we gotta do something. Let's explore.

And it was independent of ChatGPT. Right. It was more our own business was we had to figure out a way to double down. And then ChatGPT, of course, gave us a way to add more value. Like, the technology made it possible.

It's maybe an intersection of two things, but either way, you have to make a choice.

That's right. That made things I Guess somewhat easier. It's like, you know, you have to focus and do something, but within that, because we were still spread broad and our revenue was kind of split in a few different verticals, we still had to do our work and due diligence on it. So what we basically did was try to go have conversations with our top clients in each one of those verticals and just spend time doing product discovery. On what does going deeper look like? And basically working backwards without telling it to like, okay, how do we get to 60k, 100k ACVs?

How much do you pause everything to do product discovery? When you don't have a product, everybody does it. Everybody goes through this MVP motion. You don't have a product, you gotta, you kind of gotta do it, right? Like once you have a business, you have, like me, people are asking for features. You got sales domos coming in. Like, there's a kind of nature of things are moving. It's hard to stop them. How, how hard do you go on? Just, you know what, guys, we're not fixing bots, we're not doing any features. We're just going to talk to customers for a month. You know what I mean? Like, how black and white do you do you do that?

It was not that black and white because as you said, we still had customers, we were still getting new customers, we still needed help, bug fixes were still important, we still were supporting customers that were paying us and recurring as a result. Like, honestly, it was myself and my co founders just took it out of ourselves to do extra credit work, right? So instead of thinking about the future, about where this particular product goes, we instead went into what is the product direction, PMF area we want to really focus on. And that's where we spend all our time.

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How did you set that up?

Like, what kind of questions were you asking? What were you trying to get out of your customers through these conversations?

Ultimately, we didn't hit the ground running with ground zero, right? So we already had relationships with a number of people in these firms. And what we used to do is like, they're also not silly. You know, like, oftentimes customers that adopt a startup are visionaries themselves. They're seeing around the corner, they understand something has happened. And they also saw chatgpt independently of us. And as a result they saw our technology, which was kind of doing what ChatGPT was doing in a way for them. Right, but they saw that, oh, but now you guys could probably also do this and this. So we had these, we called it like product roadmap brainstorming questions. But like, you know, we basically said if we were to do more for you, that is business critical coming up this quarter, that you would drop everything and kind of get on and adopt it right now. What would it be? And that type of question you want to really get urgency and get an understanding of, like, what is the budget you're actually going after? Figure it out as early as possible and after that you're honestly iterating on a week by week basis. There's no other way. You know, you're looking at what's happening with each of those conversations. Where is it moving and applying your own sense of what the technology can do and what the market can do into that. And when it became more clear. So when one of these workflows is figured out. So for example, let's take legal, right? So in this case, evaluation workflow was becoming clear to us that, you know, that has a lot of value.

What's the evaluation workflow? Exactly?

Case evaluation is when a new person calls a law firm and you have to figure out whether to take the case or not.

This was one of the things that you built in response to the roadmap.

We haven't even built it at this point. So we had figured out through product discovery that that was a valuable pain point that people would be paying a lot more for. Like now, like it's, it's a burning pain point. And what we then did was basically you have to then validate it with the funnel because it's not only important for us that one customer does it, but we're able to set up a repeatable sales engine that is capable of continuing that sale. Like, is it actually a market? Right? Not just a Hopia market. And for that, honestly, it's back to hustle, right? Like, I think I messaged all AMLA 100 and funnily enough, back then we had 80% response rate to cold outreach. This is because they also saw ChatGPT coming. Yes, of course, I think there's very few players with, you know, good resumes. If you Will that were reaching out to them and they actually just aggressively signed up and got on calls. And you know, we're not made for Big Law, right. So we're for plaintiffs. And then we also had plaintiff firms coming. The interesting difference was the plaintiff firms came from network where the early people we were working with actually referred other friends of theirs. And the Big Law largely came from outbound and some connections.

Right.

Like Most of these VCs are connected to someone in Big Law.

Was the offer different? Like, was the product offer different for Big Law versus plaintiffs?

It was, yeah.

What was the difference?

When we started digging into it with Big Law, it was about accelerating non billable hours largely at the time. So knowledgeable hours think about things that paralegal might be doing. And the other challenge is it's incredibly bespoke. Every single big law firm has their own thing going on. So one, the culture and workflows are different. The second problem is they represent companies most often and the companies have different things going on. Right. And on top of that, they now have to follow 10, 20 different workflows for each one of those companies, even as a paralegal. So when we dug into it, it actually seemed like the amount of value AI was going to have for us at the time wasn't going to be that large. You're talking maybe 5, 10% improvements overall.

For Big Law.

For Big Law. And there was kind of this tension between not replacing billable hours. Right. Because if you go after, if you try to make it too good for the company, suddenly the law firm is making it.

This is the crazy thing about legal tech and almost like one of the, one of the amazing pieces that's, that's, you know, like there's so many legal tech companies doing so well today. In spite of the fact that this has always been the problem with legal tech, which is if you make it too efficient and you just bring down billable hours, you're just cutting down effectively the revenue. I don't know if now like three years post gen AI, they're just feeling like if they don't, if they don't.

Do it, they'll just die off.

Like they don't really have an option. But I totally understand. At that time it was still probably facing a lot of resistance towards anything that would just, you know, where the efficiency is in non billable hours, that's one thing. But if the efficiency just means you charge fewer hours because you do it faster, that's not so good.

Early on, most CIOs were excited. Is Kind of the initial thing, but they had to think about the business, you know, so I think they see the value of what it can do eventually, but they don't know how and when and to roll it out safely such that they don't cause damage to themselves. And the damage is also multifold for defense firms because unlike plaintiffs, where you never have recurring customers, really, you don't want one person just suing everyone. On the defense side, you actually do retain customers. So the cost of a mistake is actually much higher. People tend to be a lot more conservative as well. But this is one of the hard decisions we had to make. Even though all this was true, there were still big law firms willing to spend hundreds of thousands of dollars to a million dollars to us on bespoke kind of almost consulting like engagements. Right at the time it was, hey, come customize ChatGPT for me. You know, make prompts and fine tune it a little bit. For me, that was kind of what they wanted at the time. Versus for plaintiffs. We saw this consistent pattern about everyone's doing casework the same. They can't even Google it today. Like they don't even have processes for how do I make a demand letter? How do I make a timeline that is high grade and quality? All of this they kind of figure out again and again across all these law firms. But they're all the same. It's all the same process and same workflow. There was significantly more repeatable and they were seeing value literally, you know, when we made the product one weekend, which I don't think we would have seen on the big loss side.

And for them, they don't have that conflict because they charge contingency. So the faster they get something done, that's just money in their pocket.

That's exactly right. So our pricing and our business model is really aligned with theirs, and their business model is really aligned with the clients. Right. Which is another thing people don't think about. But ultimately, when you need legal help, you want to find the best person for your task. And as a consumer, you don't want to be on the hook for paying unknown amount of money with no cap, which is a scary feeling for anyone. Right.

One company I know in this space is Evenup, and I know they've been around even before, like pre Genai. Where were they at when you're thinking about going? Because they're in kind of this demand letter generation. At least that's kind of how I understand the business. You'll know it better. But where were they and how did you think about them when you decided to go after the plaintiff space?

Yeah, I mean, we were aware of them, but we didn't really focus on them because when we started, we were in labor and employment, which is a very different market than personal injury. But what we did that was different was we kind of went all in on AI from day one. So from day one, we had built up a pretty robust eval stack, and we wanted to do the case. Workflows vs Europe at the time was kind of done by offshore human labor on two very specific tasks, and they're now layering in AI. And that was a pretty important difference because what ended up happening was labor and employment tends to go a lot more into litigation, which means you don't settle before you file a case with the court system that you oftentimes do have to file and then go into discovery before you settle. And that allowed us to actually work with our early customers on nailing the product across the entire workflow, which took a while. Right? I mean, as you imagine, early on, if you have a chat interface, they can really type in anything at all, which is not great from the engineering perspective. So figuring out the right product experience that tie that together with showing value quickly is what helped us kind of explode.

And why do you go to labor employment versus bodily injury?

We do both now, but labor and employment was largely network driven. We thought it was interesting to go through the entire case lifecycle, and we saw a lot more examples of going deeper in the case lifecycle, also our early customers for labor and employment. So we just started doubling down into that vertical, and then basically, labor and employment firms, some of them, actually do personal injury as well, and there's an overlap. And that became kind of a natural way for us to expand into personal injury.

When do you decide to go all in on this product and shut down all your other business lines?

Pretty quickly. I think the scariest time was probably the first three months of making the call.

This would be what, early 2023.

Early 2023, correct. So we still had customers coming in for the old product, and we were getting a new pipeline going for the new product.

Right.

And keep in mind, the product is barely there. You know, it was probably a ChatGPT wrapper at the time. Actually, it wasn't ChatGPT. It was anthropic first, interestingly enough, but still, like, you know, of course we were doing more than just a wrapper, but that's how initially started. And as a result, like, you have to be very careful qualifying the early customers to make sure they're aligned with the larger market. And the problems are roughly the same. And on the AI side, you know, building evals is tough. Right. We don't. You have to be careful in the space about, you know, you can't really use client data to build evals, so you have to get clever with how you build it. And as a result, like, we were kind of nose deep in both the customer discovery, the tech and feasibility, because you had to make a rough call on is this even something we should do or not? Pretty early on. And that process took about three months, I would say. But what we noticed during that process was our conversion rates were ridiculous to interest in pilot. So we, you know, did the stage one discovery call. Just understand the need. But even before that, just outreach. Right? So just outreach to customers and how many people sign up for demos or interest was significantly higher.

Can you give me some numbers on that? Whether it's the conversion rates, demos per.

Day, et cetera, you're talking 40% conversion rates from cold outreach into demo requests, from 1% for the other product, from.

Demo to pilot or demo to close. What did that look like?

Demo to pilot was 90% of the time.

Wow.

Because I mean, at the time, like when we show them examples, it was interesting. And what is unique in the space, which we didn't realize at the time, was law firms weren't actually used to seeing working demos. You know, it sounds silly, but they're used to buying services.

Right.

So you oftentimes you pay someone by the hour to go write a demand for you, and you don't really know until you get the demand back if it's worth continuing or not. And this whole idea of a demo kind of blew their minds.

What was your demo actually? Yeah. How did it work?

We basically fine tuned it for. Not fine tuned it, but made the product really, really good at one workflow we knew was good in this case, case evaluation. And then soon demands and then brainstorming use cases came right after that.

That demo would be what you'd get them on a zoom or whatever and you'd upload or they would upload like some new case they got and you, you do the evaluation right in front of them or like how.

Of course, yeah, I mean, we. We went and downloaded some, you know, documents from Casepacer, which is a public data set of cases, and had some client transcripts and depositions in there and showed them. What do you want to learn about this? Let's make an evaluation of where this case is at. And you just type that in and they see it live, right? And that kind of blows their mind. And then you set expectations. So then after that, for pilot, at least early on when the product was still iterating, you said clear expectations. Like, you don't trick them, you tell them like, hey, listen, it's early, it's going to iterate, but you get this amazing team to help you through it.

And what was the, if you think about it in terms of roi, like, how much were you charging relative to what they were paying? Was the delta big or was the time big? Like, what was the biggest? Besides the fact that it was impressive technically, what was the underlying ROI for them?

Case capacity is something they look at, which is how many cases can an attorney handle at once? And that is indirectly tied to how many cases can their marketing engine feed them with? How many cases are they able to kind of move on to the next step so that are not actively working on it? And ultimately all of that is labor constrained. Right. So they're working on tasks, you know, one case might be stuck at go interview the witnesses. Right. And one might be stuck at responding to the opposing counsel's request for discovery. And they're all very different tasks, but they're all legal work involved.

But your first product, which part of that funnel did it deeply accelerate?

The top, the intake. So that was directly tied to. Now you don't need to be limited by case managers and call center people. You can accelerate the number of cases and also the cases that qualified because you could run through that at least first pass of lawyer, like intelligence early on.

And you would feed it like somebody would call in, they would still presumably at that point talk to a human, but then that conversation would be fed to your AI and then it would tell them, yeah, this is a case we're taking. This is not a word case. We're taking kind of the output it would give them.

Like, I mean, obviously it's not going to say yes or no, but it's.

Enough qualitative output so that they could easily make a decision on. And that's for them. I mean that basically, again, like you said, they can just review so many more cases and therefore take more cases on ultimately.

Correct? Yep. And I can give you an example of why stuff like this is annoying for lawyers. You know, like in one case, if you have missed wages as the main claim, meaning an employer, when they fired you didn't actually pay you properly. Now, for example, they didn't count lunch breaks. Right. They didn't Count, check in, checkout times. Right. They kind of gypped you half an hour here. There's that. You get back in like a worksheet format or sometimes clients literally just output calendars in screenshots. That's the type of data law firms are dealing with. Right. And to do case evaluation properly, ideally someone went through, tallied up how much in hours are you missing potentially and what's your hourly rate and how much dollars are you missing? That's what your ideally you did. Right. But instead, because that's so time consuming, a lot of times the call center reps were trained to just say, is there a missed wage? Yes. Then they would pass it on. Right. But that could be a bad case and you end up wasting hundreds of hours representing them when it probably wasn't worth it for anyone involved.

And once you go through these three months, you're seeing these insane numbers. Do you literally call your non plaintive customers and just churn them or how do you actually effectuate that? How do you make it happen?

You know, the engineering team did an amazing job of managing that actually. So the product was low touch enough that largely what we were on the hook for was bug reports. So we pretty much communicated, hey, we're going to stop development on new features. If you want to keep renewing, we'll allow it to happen for this amount of time and if not, no biggie, will kind of eat the costs. Let us know in some cases where that was feasible to do so we were kind of generous with how we let people go. And interestingly maybe a surprise learning during that when we sent the we're shutting down the service product is when we got probably like really strong signs of product market fit for that product. Because we saw other examples of cases where they were like, no, don't take it away from me. What do you need? So you know, that's always an option for people in the future if they want to, want to find product where I can fit, get put in hands of people and then take it away and see who complains the most. Not a bad way to go.

How long do you kind of stay in this beta pilot type phase? Where do you get the product to until you decide to say, okay, this.

Is working, like let's make it happen a few ones. So we had a few kind of 100k plus deals come in pretty early on as soon as we started the firm and something magical happened where once we had like the initial workflows and the additional workflows they wanted working usage on the product went up by itself without us doing anything. And what I mean by that is like we had kind of put restrictions to stop them from inviting other people. This for stupid reasons. We ran into QPS limits on anthropic and OpenAI and you know, scaling issues at the time, they were also scaling and as a result, like, you know, we put limitations on who could invite users into their account. But they found a workaround which was kind of a bug on the product and they started inviting their entire company on it. When we had planned a three month rollout and within a month they had just kind of onboarded everyone else themselves. And on top of that we started seeing usage every day. And then within a month we started seeing signs of long sessions. So you know, we track how long is the average session on eve and we started seeing multiple like four hour plus sessions of people. I mean, who knows what they were doing there, but they were doing some deep work continuously for four hours, which was kind of scary at the time, right, Because AI back then wasn't good at that many turns. So we were like, oh, I hope they were doing this right. So all of this showed that there was strong interest in using the product. And in period of checkpoints you had with the managing partners of these firms, they were seeing value, you know, they were giving real anecdotal evidence around. Oh, I took this case on that I would have missed completely otherwise. Oh, this person sent a discovery request the same day which when it were taken a month to do so before. And these were kind of the anecdotal piece of information we were getting and that enabled us to basically make a hard call. Okay, this is it, let's just go for it.

And when you went for it, tell me a little bit. Go to market I think is something that every founder wants to know more about. Like what were some of the go to market tactics you used that either really worked or didn't work at all?

You know, early on, like I mentioned, cold outreach worked surprisingly well. Better than expected.

Say cold email campaigns.

Yeah, cold email campaigns and actually calling them because every firm has phone number publicly available. But they gatekeep a lot because everyone calls them. But if you sound interesting enough, they will let you through. The other one is LinkedIn sometimes works for people that are managing partner and tech oriented. So that was another channel. But eventually once we had the first few customers, we really leveraged our network, right. So really blowing it out of the water for them such that they'd want to talk about it naturally without us Forcing them.

Yeah. I was going to say, did you run a referral program or was it just organic word of mouth?

It was organic word of mouth. But we asked them, like, where do you go, right? Like, I mean, where do we find more of you?

And are they not competitors? Maybe is a dumb question, but why do they tell others?

So this is actually an interesting question. So they are competitors, but they're competitors locally, right? So if you look at it, you know, in, I don't know, Los Angeles, in one neighborhood, there might be two or three personal injury, labor and employment attorneys representing that area. They'd be competing strongly each other. But outside the city, outside the state, they all have their own friends that are teaching them tips and tricks. Because similar to what I said, like, there's no book on this, right? Like, if you go online and say, how do I start a plaintiff firm? There's not very many good materials and a lot of things at the edges, you know, how do you know which cases to evaluate? Even that is acquired knowledge for these law firms. And they actually share that information. The second thing that happens in this space is like, there's incredible wealth disparity between the plaintiff side and defense side, where defense, because they're paid by companies and they have massive paralegal teams, when they show up to court, they literally show up with 10 people. Where in the plaintiff side, you have one or two people there, right? And they're used to being kind of smothered like that. Where defense. One tactic they have is actually threatened trial work and litigation work, which takes up a lot more time for the plaintiffs, which means the plaintiff has to decide between, do I do this trial work or settle these smaller cases just to pay my family, right? Get paid. And that is like a real pressure that happens. And with AI that has completely been flipped on the head, right? With Eve, like, Eve now is incredible at helping individuals with these workflows. And as a result, you're just putting pressure back onto the defense side. And they love this, right? And some of the ways they were doing this before technology was they go to conferences and share tips and tricks. Like, hey, in a closed room, right? Like, here's how I defeated State Farm's defense team in this particular litigation, right? And here's who you should kind of settle with quickly. Here's who you should kind of push to trial, because they don't know what they're doing in trial. And all of these type of tips and tricks doesn't exist anywhere. But the only way to share it was by meeting People locally and getting to know them. And because the product was so good, they were actually bringing us naturally in these events. And then we kind of doubled down and made that into an actual motion.

First full year, 2024. How fast do things go? Like how fast do you hit a million? How fast do you have 10 million ARR?

I think from when we ga'd the first quarter we ga'd probably end of 2023, probably early January the first quarter. I think we hit a million in ARR already for that product and I think the two months after that we had another million and a month after that we had another million. So it was kind of been rapid.

Growth since you probably 10x in a year. Ish.

From 1 to 10 we've been growing a lot. I think the Official number is 800% but I think it's ever increasing.

And tell me a little bit, maybe just high level, what are some of the things that Eve does today? At first it was just that first piece, right? Just the evaluation of kind of a qualitative piece of what this case is about or whether you should take it or not. How much have you penetrated these organizations today in terms of what you're doing for them?

So it's going deeper and deeper into the casework. But if you were to up level, what Eve really helps with is ultimately AI is like an efficiency thing pretty much everywhere. But how do law firms actually get value out of it? Right. And this is a combination of few different factors. Like you need to get the product right, you need to get the arranging the people to use the product right. And the technology has to be really good, right. To make all of this happen. And we call this like AI transformation. I think we were kind of in this whole trend of digital transformation for a long time. Now we're in this workflow where work that was done by people can be done by software. But all that's going to do is change what people have to do. And you know, in anecdotal evidence so far, every firm that has us has chosen to do more with their people than let them go.

But are you like answering calls now? You're doing voice like you're doing kind of full, full AI features.

Exactly. So part of the benefit is we've kind of taken the best of the cultures from my time at Facebook and my time at Rubrik and built a culture of high engineering discipline, launching high quality products that work for the most secure of environments. And the Facebook speed, which we operate also, as you can imagine, in a fully AI driven way. We do AI prototyping to do PRDs and we iterate quickly. Of course we use all the vibe coding tools and that's enabled us to launch incredible features in weeks of effort. Right. And on top of that, we're also changing our tech stack whenever it makes sense. Like, you know before, if you imagine like a year or two ago, you had to put a lot of effort into very bespoke rag stacks and routing. But now LLMs are getting more capable of doing more agentic behavior. As a result, you have to change your architecture, otherwise you're going to be left left behind. But that inefficient motion has helped us go deeper into workflows that were otherwise like billion dollar industries by themselves. Right. So we talked about voice. So EVE now has a voice AI component where it's actually able to take the call and do this live filtering of is this a good case? It can even be empathetic. Right. Which has been trained on personal injury cases and labor and employment cases. And on top of that, deeper the workflows, like getting this medical chronology demands right through pure AI is really hard still because there's very low tolerance for errors. You know, if you get a case wrong, you're just going to lose that settlement and the cost is too high. So we have put a lot of work into making that accuracy as high as possible and that ends up being the source of truth data, which then helps all the other use cases. Perfect.

Well, Jay, let me stop it there. I'll ask the last three quick questions that we always end on. The first one is when did you feel like you'd found true product market fit?

I think that moment where I mentioned where usage customers were working, breaking our system to go invite people to go from like one user to the to I think 100 attorneys getting on board in four weeks, that was like a oh my God moment for me for sure.

And was there a time where you thought things might just not work out?

I think one of the difficulties of being a founder is you need to have full conviction in whatever you're doing and unbridled enthusiasm. And I think I do have that. I'm hard enough to know that, you know, it's reality. Like you have to react to data that actually you see, but when you're chasing something that you have data points for, you have to see it through. Unlike a VC where you take multiple bets, you have a portfolio of one and you have to make it work. So were there doubts in individual execution? Absolutely. But were there doubts Overall, that we would succeed? I think no.

And last question, what would be your number one piece of advice for an early stage founder that's looking for product market fit?

I think it's to set really high expectations of what product market fit is.

Right.

I think early on. So before you make your first million, you have to really focus on the stage all the way from the top of the funnel. Right. So meaning how are you doing your outbound activities, how quickly are they converting? And even that is a pretty good signal early on because that tells you how close to home you are in urgency of problems and how articulate you are with what you're actually solving for. And I would track those percentages. Right. So when you're doing something, if you're really early on, you are iterating on multiple different ideas simultaneously. So at that point you just literally track. I emailed thousand people, two people got back to me on this campaign. I emailed, you know, a thousand people here and like 50 got back to me. You know, directionally that's better. But you should aim for something, I think even higher.

Well, just to be, to put into number, I mean, 40% demo means you email a thousand people, you get 400 demos, which is insane. Like it's, it's almost too much. What do you do with that?

Right?

Like, but that just shows you just how much people resonate.

That's right. That was the problem. Like we didn't have capacity to actually handle those calls and you know, that was a good problem to have back then. And I think we still, still have it. But that's kind of the bar you should shoot for. I think the second thing is when you do hit product market fit, this is like call it a million plus. When you have real product market fit, it just feels different. You know, I hate to say this, but like the Paul Graham essay, you know, the Peter Thiel essay, like everything that all these people have written is kind of true. Where every single metric you do track suddenly looks different. And that is how I think you know for sure you hit product market fit.

Awesome. Jay, thanks so much for sharing your story, man. This has been great.

Fantastic. Thank you, Palbo, for having me on.

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