How this 1st-time founder went from closing customers for $500 a month to $300,000 a year. | Sean McCarthy, Founder & CEO of BackOps

Sean McCarthy (00:00:00) :
So what we started doing was just building the use cases for the customer and giving them the entire use case, and the ROI analysis that they could present to their board with their branding and all the information that we've gathered to try to make it as light of a lift as possible. Candidly, the platform that we had set was just not built for that scale, and so we made the tough decision to stop selling and just rebuild the platform and so we rebuilt the platform to make it an enterprise-grade AI platform. And then we introduced this AI process center, which alleviated the burden of getting all of this in from the human head. The biggest hindrance we had was, let's say I'm selling to you. You're like, all right, that sounds good. Let's get started. We'd sign the contract, and we'd say, just send us the SOPs that you have for the process, and that's how we would get started to train the agent. And we would go into months where the customer would just kick the can down the road to not write up an SOP. Because nobody wants to sit there and write an SOP, or they have them but they're old. So what we did is we created an SOP recorder into the platform and so they can go to our platform. It records the screen, obviously records the audio, and tracks all the metadata from the clicks. And that's how we build out the SOPs.

Previous Guests (00:01:12) :
That's product market fit. Product market fit. Product market fit. I called it 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.

Pablo Srugo (00:01:24) :
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. Sean, welcome to the show, man.

Sean McCarthy (00:01:40) :
Thanks for having me. I'm looking forward to it.

Pablo Srugo (00:01:42) :
Same here, dude. You're on one of those now-becoming classic AI stories, right? You started less than two years ago. You've raised over $30 million now, doing millions in ARR and growing very, very fast. And you were telling me maybe a few months ago, you were going up against a very well-known, we won't say the name, but a very well-known, like, large AI enterprise. One that we would all recognize, on these two large enterprise deals and you're putting your best foot forward, but you also effectively assumed that you're going to win neither of them because, you know, long odds. And you didn't win one, you actually won both. Tell us that story because that's kind of the product market fit moment.

Sean McCarthy (00:02:24) :
Yeah, I'm happy to. I actually told our board members that I thought it would be a five percent chance and don't get excited about it. Just because I thought it was such a long shot. At the time, and especially when this started, all the way to as we came to the end of it last year. We were still, I mean, less than, obviously, two years old. This company, as you mentioned, is very well known and I just felt like it was, a David and Goliath kind of scenario. And so I told them not to get excited, did everything we could on our side to show that we deserve to be in the race, and were able to give, and go above and beyond on some of the documentation, that we provided and where this goes. And yes, we're early, but this is where it will end for you if you utilize us. And didn't hear anything on both of these deals for, probably two months and just kind of considered them DOA, and then received an email randomly asking for all of our SOC 2 information and, you know, security infrastructure documents and all of these things. And got, obviously, a little excited that maybe there was something here. And told the team actually the same thing, don't get very excited. I don't think this is going to happen and then we were finally awarded the deal officially. It was an incredible moment for us and really changed some of the trajectory of the company.

Pablo Srugo (00:03:45) :
What size deals are we talking about?

Sean McCarthy (00:03:47) :
Entry points are six figures. So you're looking $200 000 to $300,000 on those deals. But we have a usage component on top of that and then where this goes, those should clear to seven-figure deals next year. So that's kind of our entry point. But for us, they're also major logos. So really, like leaders in their spaces. So along with the deal size is just kind of that marker of getting those number one or number two folks in those verticals.

Pablo Srugo (00:04:13) :
Maybe tell me a little bit about what your company actually does. It'll help with some context.

Sean McCarthy (00:04:17) :
Yeah, so BackOps is an AI-native shipping operating system. What that means is we've templatized much of, specifically around the warehousing last mile. On problems that you can think would happen to a package or a pallet, like maybe it's lost, stolen, damaged, and then what would be the downstream effects of something like that. Or maybe if it's a temperature breach, you can imagine there's a lot of paperwork, claims that need to be filed. We'll ingest that information. We'll chase down and gather all the documentation and then make sure that our customers file. In that case, one hundred percent of the claims, or it's reshipping a package. But that's being handled autonomously and so we're taking a lot of the repetitive work from the back office. And then very quickly branching that out into adjacent business units like finance or customer service or ops. Depending on where we start.

Pablo Srugo (00:05:05) :
Maybe walk me through a type of customer and a specific package. And what would normally happen, and what happens with backups.

Sean McCarthy (00:05:11) :
Yeah, so to conceptualize this, and obviously in supply chain, there's not a lot of folks that are really familiar. And so I'll give you a very basic example, and then a more complex example of what we sell for today. Because when we started, we were handling the very basic examples, selling to mom and pop warehouses, 3PLs. And you can think of it as a consumer where you order a lamp, and the lamp gets shipped to your house, it's broken. As soon as you open the box, you realize it's broken, you call customer service and you say, hey, I just ordered this lamp, send me a new one. They're going to assign that to operations, operations will send you your new lamp, and then they have to file a claim with, say, FedEx. Because FedEx broke the lamp, and then finance needs to track that to make sure they get their money back. That's three business units on a fairly non-complex workflow, being a lamp that's broken and so, when you take that to a much higher complexity. Say we're talking about grocery where you have a truck of food that might have Tyson's chicken and berries from Driscoll, and Happy Egg, and all these different vendors on that truck. And maybe the temperature on the refrigeration unit breaks or exceeds where it's supposed to be. What happens then, right? Now you've got all of these vendors at a much higher complexity with invoices and BOLs, and documentation that needs to be gathered. And so, rather than somebody going and doing these manual reports, they can just pick up the phone from the loading bay and say, hey, we just received this truck. There's a temperature breach, and we'll take it from there, run and gather all the paperwork, and make sure that a hundred percent of those claims get filed. Which is millions of dollars a month on these larger enterprises and so, that same scenario can be done with pharma, with lifesaving medicine as an example as well.

Pablo Srugo (00:06:49) :
Going back to the moment where you beat this enterprise, what would you ascribe that to? How do you think you won? What do you think you got right?

Sean McCarthy (00:06:57) :
You know, one thing that we've done that is, and we can certainly talk about how we're getting into these enterprises, I think that's a good topic but I think for us, once we're in, we've tried to not just let the normal cycle run us. I guess would be the best way and so, across the board, there's ELT and board mandates to adopt AI, and we know that. And a lot of times, that's why they're speaking to AI companies in the beginning. And what we know that they're going to have to do is show a business use case of why would we entertain this vendor, and what does this look like. So what we started doing was just building the use cases for the customer and giving them the entire use case, and the ROI analysis that they could present to their board with their branding, and all the information that we've gathered to try to make it as light of a lift as possible. With our solution being like the center of that. Being embedded in basically not just educating on AI, but educating BackOps as actual impacts. And so I think that probably contributed to a bunch of the unlock, where when they're thinking about the AI solutions or maybe the vendors. The deck that's being presented is hopefully the one or the majority of what we send to them and so we did that on both of the deals, and I think that that was probably a big contributing factor.

Pablo Srugo (00:08:13) :
And then going back to the beginning, I mean, how do you end up AI for supply chain kind of automating, you know, back office operations. Not the most sexy space, not like you said, a space that many people know about.

Sean McCarthy (00:08:24) :
Yeah, so most prior to this, I was at Amazon. I started in Amazon as a global sales leader in Amazon's marketplace. So, you know, when you go purchase something off of Amazon, that's the marketplace. We were taking U.S. sellers and pushing them internationally and then from there, I went to Amazon Shipping. And for those of you that don't know, Amazon Shipping was created to head to head a lot of the FedEx, UPS, DHLs of the world for consumer, kind of small parcel. And was early there as a number two BD manager. We actually had an in the field designation. So we had to spend four days a week in customer warehouses, which was a really good opportunity for me to kind of develop this thesis and see these things firsthand. And then, also pen test and ask the customers about these problems, and what I was noticing. But that's how I got in. I think non-sexy is probably a good way to sum it up. But if you go to the grocery store, you see food on the shelf, often we don't think about how they got there. We just think that's normal, and maybe it just appeared. And so it's pretty fascinating behind the scenes, but every day is a new journey, to say the least.

Pablo Srugo (00:09:25) :
Can you go deeper on finding the original pain point? It leads to a lot. I mean, obviously you can pivot and all these sort of things later. But at some point you're in pivot hell and, you're just kind of searching and grasping at straws. Maybe you get lucky. If you figure out an initial pain point that is right, it solves a lot of the other things and you're still going to have to iterate, and figure out things. But at least you're starting from somewhere solid. Can you maybe go deeper on those initial, you're spending time, you're in these warehouses, you're seeing things, at some point you start asking questions. How do you structure that? How does that happen? What are you asking? Just tell us more there.

Sean McCarthy (00:09:59) :
Yeah, I'd be happy to. So as I started to develop this thesis, I would talk to the owners of these businesses and ask them what their number one problem was. And what I was hoping that they were going to tell me was some sort of workflow that they all had in common. That there was this big problem and it actually is not. The biggest problem that they had was churn, is what they would tell me and it derailed my fact-finding for a little bit until I was able to deep dive into why that churn is happening. You're going to have churn on the folks that are doing the physical labor, and there's nothing for us that we're going to touch necessarily on that side. But if you look internally in the admin side, and the customer I'm thinking of in my head. They were churning this person once every six months or so from an administrative perspective. 

Pablo Srugo (00:10:43) :
You're talking about retention on their employees, not customers? 

Sean McCarthy (00:10:46) :
Correct, yeah. Retention on their employees, yeah. That's the biggest problem that they're facing is, they would teach them, it would take them months to get up to speed, and then they would leave. And if you watched, and that's what I had the ability to do, is watch what these folks did on a daily basis, and eighty to eighty-five percent is the exact same thing every single day. You know, it's reshipping the package, it's filing the claim with the carrier, it's talking to multiple shipping vendors, or doing coordination, or saying, where's my stuff, and doing a quick tracking lookup, and giving it back. And so, in talking to these folks, that's the biggest part they actually didn't like about their day. And they'll tell you they enjoyed the complex, like maybe it's something that's new, or it's more of like a complex edge use case that's not happening. But the day to day of reshipping and going through these, monotonous kind of tasks over and over. That's where the biggest opportunity was and so what I started to do was say, well, hey, if, you know, when that came in, if you didn't have to go reship the package and file the claim with Amazon, and just did it automatically. Would that be something you're interested in? And they were like, yeah, absolutely. Amazon's doing that? And I say, no, I'm just curious, you know, if that would be of value to you, right? And I was able to talk to probably eighty-five different warehouse owners and admin folks in this time. And at that point, it felt like I had enough.

Pablo Srugo (00:12:00) :
Of the eighty-five, was it, yeah, all eighty-five of this problem? Or was it, there's twenty that have the problem, but they really have the problem? Do you know what I mean? It's just an intensity thing or it's just a breadth thing?

Sean McCarthy (00:12:11) :
It’s a breadth thing. I mean, if you look at it, you can kind of assume it's a scale problem. If you ship one hundred packages, you can assume approximately eight percent might have. Five percent to eight percent will have some sort of issue. Again, lost, stolen, damaged, not the right thing, delay and that perpetuates with scale, right? So then if you ship one thousand packages, right? Or ten thousand, and that number just continues. And so what you have to look at is, what is the non-controllable for all of these customers? And it's the carriers. They're all using UPS, FedEx, DHL, OnTrac, and, you know, these things are built to not necessarily to save packages, right? They're built for speed, right? You're trying to put something into a box that is going to be the closest length, width, depth that could fit that package, not a bunch of padding and so it's kind of built against the system to begin with. But it's really reliant on the carriers, which all of them are using the same carriers, and they don't have much of an option there.

Pablo Srugo (00:13:05) :
But so of the eighty-five, most of them you found had this retention problem with labor.

Sean McCarthy (00:13:10) :
Correct, almost all of them. There was certainly a handful of folks that have had people there for many, many years with no employee churn and even then, we would sell an efficiency play to them and basically say, hey, the majority of these that are coming in, you don't have to do anymore and they were still excited about that. So it didn't necessarily have to be correlated to churn, but it was more efficiency driven than anything across both of the realms.

Pablo Srugo (00:13:34) :
When did you decide to leave Amazon and go all in on this?

Sean McCarthy (00:13:38) :
So, July 2024, is when I quit Amazon and had probably started this fact finding journey maybe in February or March. So, it was three or four months of making sure and looking at what's happening in the market. You know, when I looked, there was a lot on the freight side, but there wasn't a lot on the warehousing side. I thought it was an underserved market, and I also thought that having some domain expertise was helpful. And we'll talk about this, but we still use that today. Again, just making sure that when we're going and talking, I'm using specific examples to cite, like, are you having this exact problem and is it causing this exact pain point for you so that they could relate? And so after July 2024, left, raised venture within weeks. Actually, of quitting and then we were kind of off to the races.

Pablo Srugo (00:14:27) :
This is your first startup?

Sean McCarthy (00:14:28) :
It is, yeah.

Pablo Srugo (00:14:29) :
How did you raise so fast? How did you structure that?

Sean McCarthy (00:14:32) :
So I didn’t really know the venture game. I didn’t know that the warm intros were quite a thing. So I was just emailing VCs and it did work. You know, for the initial check that we got in actually was from 10VC, and very quickly, Gradient had reached out to us maybe like a week after that. So I think once you're in kind of the ecosystem, you're in, and then they ended up leading our pre-seed. But to answer your question, I mean, it was literally just me sending a bunch of emails with a quick background of what we were building and I feel lucky because I understand. Now I understand that that's a long shot and maybe probably the hardest way to get in.

Pablo Srugo (00:15:05) :
You know, first time founder, non-YC, still early, kind of pre-product. It's, you know, obviously AI is making it so that there's more pull and there's more interest in funding companies. But that's not an easy place to be in and it sounds like you fundraised relatively easily. I have to guess, like besides, you know, you and the team. The fact that you had talked to these eighty-five people and were from this space, and really understood the nuances of the pain point, must've been a huge reason for why they saw like a bet worth making.

Sean McCarthy (00:15:35) :
Yeah, I think that that was super important in understanding more specifically how the technology would help these folks. I had kept a spreadsheet of all of the people that I talked to, what pain points that we talked about, and certainly there was things that it wasn't just a perfect fit throughout. This river of gold, there were different things. But you could use what's coming from the agentic side and even RPA to say like, oh yeah, this could probably be solved fairly reasonably. And it was that coupled with the fact that they actually talked to customers, they asked for referrals. One of them went to the customer's warehouse and spent time. I didn't even know they were going to show up. This just showed up at this customer's warehouse and spent time with this customer. But I do think that it was kind of the combination of all of those factors and the cards are a little stacked against me, to your point and I wasn't in SF. I was in LA, and not quite in the ecosystem.

Pablo Srugo (00:16:28) :
So tell me, what was the first version of the product? What exactly did it do?

Sean McCarthy (00:16:33) :
So essentially, the first version of the product was actually built into Slack, and so what we found was the majority of these 3PLs used Slack to communicate with their customers. If you're not familiar, very quickly, a 3PL, if Pablo and Sean, we're going to go get our warehouse together, we're going to go start to find e-commerce brands that don't want to store their own inventory and they'll just pay us a fee to have it, picked and packed. And also if the customer wants to return it, we'll manage it for them and we'll just charge them a fee to do so. And so the majority would have thirty or forty brands that they fulfilled for and then communicate with them on Slack. And that's part of what I noticed at Amazon is these guys are just communicating with forty different threads of these customers asking for different order numbers, or tracking IDs, or unique identifiers, and asking very quick things. I just need a status on this, or this was shipped with FedEx. It's been stagnant. Can you reship it with UPS? But very basic, and again, eighty, eighty-five percent, or you can call eight out of ten of the requests could fall into two or three buckets. And so what the first product was is we built into where they could tag our bot on Slack, and give it a command to then go execute. So it could be like, go reship package one, two, three, four, five, or file a claim with ABC carrier and so that's where we started. It was very basic and that's really kind of all it did. But quickly, the customers started to ask for things that are way outside of what we had built. Certainly were on the roadmap, but way outside of, you know, our comfort zone and something simple like reshipping a package.

Pablo Srugo (00:18:09) :
What were some of the things that they started to ask for that kind of expanded your view of things?

Sean McCarthy (00:18:13) :
So they started to ask for things around billing, as an example, and finance. Where after maybe a claim was filed, they would need to again track that. They want to know, was it approved? Was it denied? Is there a request for more information? Do I need to reach out? If it's a picture maybe that's requested as an example, if it's a damaged package and they didn't supply a picture. And so there was this kind of cross-business unit request starting to come through. And then that was on the back side, and on the front side it was, can you communicate with our customers? So, can you build something that our customers can self-serve this information and we don't have to bother our employees? So, if they want tracking or they want something they can actually just hit the BackOps system and take the action that way instead of it going through a human in a loop on their side. And so, you can think like the customer service is before and then the finance is after. But very quickly making us realize that we would need to expand reach, you know, outside of just kind of that central operations layer.

Pablo Srugo (00:19:11) :
So by maybe mid-2025, which is like a year into running this company. You raise a $6 million seed. The median seed is like $3 million. So this is twice as big. What's the story there?

Sean McCarthy (00:19:23) :
Yeah, so we were kind of finding our footing and had got a couple of customers back to back to back. I think we had five or six customers that we had acquired within a two month or so time frame. Selling primarily this Slack-based or reshipping-based kind of platform.

Sean McCarthy (00:19:40) :
And what are the ACVs at that point?

Sean McCarthy (00:19:42) :
ACVs at that time were $20,000, $30,000, $40,000, something like that. Very small, in fact, we had one customer. I think, that was paying like $500 to $700 a month as one of the first customers.

Pablo Srugo (00:19:55) :
So how do you get a $6 million seed round done?

Sean McCarthy (00:19:57) :
Yeah, so we had enough where we felt like within our pipeline and what was happening in the market, and the market pull from our customers, that we could go and put together a round. So we did, we put together a fundraise. We had a pretty competitive round. We actually got six or seven term sheets in that round and chose Construct. Construct has a focus in supply chain and really understands the space, the GPs came from Uber and NEA. And I just think they really understand kind of what we were trying to do. And so, I decided to go with them and haven't looked back, but that was a really big unlock for us as well to start to get quality talent. And what we saw was that we did have a high need to hire engineers, didn't quite have the capital to do so, or were trying to be very resource constrained during that time until we really kind of started to see things work. So that was a pivotal time for us.

Pablo Srugo (00:20:44) :
But I mean, how do you? Just about every founder wants to raise a $6 million seed round and have six or seven term sheets, right? What do you think you did right to get into that position? Because sometimes it's like, oh, dude, we're like growing, you know, we're $2 million ARR, right? Or whatever. It was just crazy fast growth. You had obviously compelling vision, but you were still very early.

Sean McCarthy (00:21:01) :
Yeah, no, I'm happy to share more and you know, I've tried to guide founders as they've reached out as much as I can as well. First and foremost, started having some of the conversations early just to share high-level what we were doing, never sharing the strict details in. Second, making sure that the story of where this goes was really, really clear and could be conceptualized even if they weren't a domain expert in supply chain. Third, run a really tight process and so stack-rank the VCs that we wanted to talk to and making sure that the ones that we really wanted to target were probably towards the end of that pitching. Because there's always going to be a little bit of kinks in the pitch in the story and then that tightly. And so, I think when we went out we said we were going to close it in a week and a half or two weeks. And that's exactly what we did.

Pablo Srugo (00:21:46) :
How well did you personally go? Were you doing like ten meetings a day? How much volume?

Sean McCarthy (00:21:51) :
I probably took five to six meetings a day would be my guess and then obviously, as we got a little bit closer, and as diligence was happening. That ramped as far as some of the more in-depth conversations that we were having, but it was probably five on average a day.

Pablo Srugo (00:22:05) :
And then the other thing you're telling me is that, you know, sometime after the seed round. You're now mid-2025. By the end of 2025, that's where this crazy product market fit moment happens. In between that time, you actually pause selling in order to rebuild the product. What happened there?

Sean McCarthy (00:22:21) :
Yeah, so that was tough, you know, especially for myself coming from a sales background and wanting to just drive towards revenue. Essentially, we were getting more and more of that pull to service, finance, and procurement, and ops, and CS, and all of these adjacent business units. During this time, concurrently, we're also getting pulled into the mid-market and a little bit of early enterprise conversations. So we're able to pen test what they're looking for, how they're thinking about us and over and over and over again, they articulated they didn't want ten AI vendors. So they didn't want one for every single business unit with all these compliance and regulatory, and things that they would have to go through. And they were really looking for something that was considered more enterprise grade. That multiple people or multiple business units could benefit from, not just the director of operations or something. And candidly, the platform that we had set was just not built for that scale. And so we made the tough decision, I want to say it was like August of '25, to stop selling and just rebuild the platform. And during that time, rebuilt the platform, but also did a retro to understand where we were getting hung up on deployments. And a lot of that was actually getting the context from these companies out of the human heads. And so, we rebuilt the platform to make it an enterprise-grade AI platform. And then we introduced this AI process center, which alleviated the burden of getting all of this in from the human head. And so that was a huge unlock for us.

Pablo Srugo (00:23:52) :
What is that? How does that work? Because that's a huge thing across industries, is like context, right? There's a massive thing right now. So how did you set it up?

Sean McCarthy (00:23:59) :
So the biggest hindrance we had was, let's say I'm selling to you. You're like, all right, that sounds good. Let's get started, we'd sign the contract wnd we'd say, just send us the SOPs that you have for the process. And that's how we would get started to train the agent. And we would go into months where the customer would just kick the can down the road to not write up an SOP. Because nobody wants to sit there and write an SOP, or they have them but they're old. So what we did is we created an SOP recorder into the platform and so they can go to our platform, click new workflow, hit record, it records the screen, kicks on the audio. Obviously records the audio, and tracks all the metadata from the clicks, and that's how we build out the SOPs. So if it takes the customer eight minutes to record the video, then that's their total time for us to initially give us that workflow or that use case.

Pablo Srugo (00:24:51) :
But from the customer's perspective, because we've all had this where we're doing a task and we're like, this is an annoying task, I wish I didn't have to do it, I wish AI could do it. That's probably the moment where they're like, oh, record, and then they just do the task they were going to do anyway. And you pull that into an SOP.

Sean McCarthy (00:25:06) :
Yeah, exactly. So they were already going to do it. They can hit record and walk through, and as they're walking through, of course, they can verbally articulate anything that they want. From there, once we have it, we'll process-mine it. So we'll actually make sure that it's the fastest route to get from A to Z. What we see is that historically, maybe they're pulling down an Excel spreadsheet, doing some sort of manipulation, and uploading it somewhere else. Where obviously they don't need to do that anymore. So we'll make sure that it's efficient. After it's built, it'll go to staging, and then they can actually push it to production. Really where that pulls ahead is, let's say that there's two different things. Number one, let's say the use case is in flight, but then there's an edge use case, and they're like, oh, I totally forgot to tell BackOps that when customer A, B, C has this problem, we do D, E, F. They can just record a two minute of this is how you handle this specific case, right? Then we can very quickly adapt the agent and then the second part is that on that enterprise-grade side. Now if it's that claim and finance wants to track it, they can see what the current process is. So there's no more silos and they can say, hey, well, every time that happens, we have to do our process. So can we just attach our process on? And the answer is yes, they can just attach that on via the process center. And so that was a huge unlock for us to also get a wish list of customer wants after the fact. Because they're just kind of, in the example that you said, going through their day and now they're just recording a bunch of these use cases, right? And wanting to give those over to us.

Pablo Srugo (00:26:28) :
You love this show, you don't want to miss the next episode, why would you? So hit that follow button. Trust me, it's in your own best interest. One of the things I'm hearing a lot about right now is you got to think  about building AI, like you're building a worker, right? The same way you would train and onboard someone is the same way you want to onboard AI. And this is very much in line with that, right? Which is, if you hire a new person, you're going to show them. You're probably not going to send them, here's like a hundred-page SOP, you know, go read it. You're probably going to be like, okay, this is the thing you're going to do today. Here's how you do one, go do it for the rest of the day and let me know when you run into problems, and I'll tell you. That's basically how I train someone. The one question I have is why have this friction of like hit record, the natural thing would be like you're just always on recording and you'd like prompt them, and you're like, hey, I just saw you do something. It seems annoying. Do you want that to be a workflow? What's the thinking there?

Sean McCarthy (00:27:19) :
Yeah, I mean, you're seeing a lot of our roadmap already. Already today, we're pairing the recordings with ticket data and email data to also measure some of those things. The always-on recording is where a lot of this process center goes. However, we're still in the early days, I think, of some of the comfortability of AI watching what they're doing and we found that most customers have asked that we started this way. And eventually would be okay with more of a screen watching type scenario. But for right now, have felt the vast majority think it's a little too much too soon.

Pablo Srugo (00:27:52) :
You know, it's an important thing, especially in enterprise. The security and data piece is obviously huge. And I've seen this before, where if your initial product can be simple from a security perspective, and simple usually means something always-on recording is definitely not simple. Because the access that you have is uncontrolled. Also, if you don't touch customer data, like if you can have something that somehow sits on the side, and you sometimes can't. But if you can, you're going to get in way faster and if you can at least add some controls, that's also going to help. And then, you know, you work your way to full visibility on everything. It might sound like the best place to end up, but if you try to have that as a line. You just won't get into these enterprises to begin with.

Sean McCarthy (00:28:33) :
Totally agree, and there's a reason the system that's built today works. Because they're picking and choosing what systems access, and what information we're getting, and what workflows we're getting. And obviously with an always on scenario, either a human would need to toggle that off if they're going to do something sensitive and remember to do so. Or you have always on, you might have recorded something that didn't. I think that the technology that we're building also still has some ways to improve to make sure that those safeguards are in place at the enterprise level. But I do think that something similar will become the norm as it relates to gathering inefficiencies within a large organization and being able to have better visibility into what those tasks, and workflows are.

Pablo Srugo (00:29:14) :
You know, another thing that you mentioned there is this idea that nobody wants to have ten AI vendors. In the old world, like five years ago in the SaaS world. People would talk about nobody wants to have ten, fifty SaaS apps, you'd rather have all in one. It sounded right, but it wasn't true, except for the enterprises that bought Microsoft. In most cases, you did have this world of best-of-breed apps. Everybody's in a silo. They're great at what they do and you end up with a large enterprise would have fifty of these apps. And that's just the way the world works. And so many different people try to come in and stitch them together. It's not a thing, again, unless you're Google or Microsoft. In this world of AI, that seems to have changed. You know, we had, for example, Serval on and Jake was telling me about how he's rebuilding ServiceNow. This entire behemoth of a twenty-year-old company, instead of building on top. In the past, you would say, okay, take ServiceNow, you're not going to move that. I'm going to build AI on top and yeah, it's a dependency, that's annoying, but what else am I going to do? And now you're like, I'm going to rebuild ServiceNow. In your case, something similar where like, you know, you started with this Slack bot and maybe, you could have verticalized and stayed, okay, well, we're just going to own operations, somebody will do finance later and yet what customers are demanding is, no, you need to be across. Maybe tell me more about what you're feeling on the field, what you're hearing from customers around this concept of one platform that can kind of do it all.

Sean McCarthy (00:30:26) :
Yeah, I'd be happy to and I think there's probably a lot of synergy with the way that Jake looks at it as well. I think what we're hearing from our customers is if this thing is going to be really effective, it has to take the end-to-end of that entire workflow and so if I have to insert a human in the beginning to take the phone call and understand what the tracking ID is, and whatever the problem is, and then at the end. Finance or whatever department has to go do all of these additional actions, it's not that beneficial. You're actually selling or solving for a very small sliver of minutia that is not that impactful across the company, and that means that I'm going to have to go get either more humans or other vendors to service the rest of that. So what they've been adamant on is like, not that you necessarily have to, let's say it's a forty-step process. Not that you have to take the entire forty-step process across five business units right away. But the ability to maybe start from the beginning and build a classification system, and then kind of take that piece by piece is important. Because they are trying to establish relationships with AI vendors that they trust that are taking more and more as it relates to not only complex workflows, but maybe there's opportunities like the claims where only twenty percent are getting filed. They're looking to hand those off to a reliable person that is going to take the end-to-end and so for us, that has been something that we've really indexed on and making sure that is it possible for us to actually take that entire workflow. And if it's not, we'll tell the customer too and say, hey, there's actually a dependability here or there's something that might block this. But that's been super impactful and I think that that's really where it goes is all of these point solutions will become a commoditized layer here very quickly. And you have to think the simple ones are going to be able to be solved with things like cloud code, and we're seeing a lot of that already today. And so, I think that where a lot of our moat and where the customers look at us as a more of a one-stop shop is the fact that we can go deep, and we don't need API access as an example. And we can call and email these carriers, and gather documentation, and do a lot of the things that before they had to have humans do.

Pablo Srugo (00:32:29) :
I think it's interesting to compare the before and after, like the SaaS app world and this world. As much as synchronization always mattered, if you think about the apps we would all know, right? You might've had Slack to message people on your team, Superhuman or Gmail to email people externally, Miro to whiteboard stuff and collaborate internally, Notion to write up docs and yeah, in a sense it'd be cool if they all talked to each other. That's kind of your Microsoft suite option. But it doesn't really matter that the data doesn't go across. It doesn't dramatically change the value prop and in many cases, why these apps are all big in their own right. You'd rather each of these tools be great at what they do than be mediocre at what they do but somehow synchronized. In the world of AI, and as I'm thinking about the value you're delivering. Which is you're taking something that touches many departments and moving and solving the entire problem across, the data sync is critical. If they have ten AI vendors, like they would have had ten SaaS apps, you absolutely are going to need to talk at each step of the way, right? As you do all these handoffs, which every single one, and they're going to have to all fully integrate together. Which then raises the obvious question, well, shouldn't it just be one product? Which is what you've built, but that's a big change. Data has been talked about for a long time. It's not until very recently, at least in my eyes, it's clear. Not just the value of data, but the value of having data that follows a workflow all the way through.

Sean McCarthy (00:33:55) :
Yeah, one hundred percent and I think that that's been another big thing that we've tried to really mitigate against is that we also don't want to be reliant on other companies for this critical data. Where they're owning maybe these ontology layers, or they're owning critical information on the customer success side that we have to ask to make sure that we're getting routed at proper API or whatever it might be and so the more of that that we can pull in, it's a win-win, right? Obviously, we're not relying on somebody else and then the customer doesn't have to pay an additional vendor to perform that same task. And then additionally, what's happening right now is a lot of these enterprises are still testing the ROI from AI. And with that, they're not going to test with ten or fifteen companies, they're going to pick a handful of companies, you know, and test those out. And if you're successful, you can really dig your heels in, and that's what we've done and what we can hope to continue to do.

Pablo Srugo (00:34:49) :
So let's kind of go deep, maybe really tactical on. Because now you're mainly, you started with SMB, as you said, you moved up mid market, you got pulled to enterprise. Now you're at the place where you're winning in enterprise, you're beating well-known large enterprises at that game. Let's go deep on just kind of that enterprise playbook, right? From your vantage point, in terms of now you've done it for a while, you're starting to perfect that. What does it look like? Maybe start with, as an example, you've got an enterprise that you want to win. What happens? How do you make that happen over time?

Sean McCarthy (00:35:20) :
So we've done a number of things to kind of be scrappy and get in. I wish that I had twenty-five years in and could just pick up the phone, and call one of the C-suites and say, you're going to buy this for me for a million dollars. We've had to dogfight our way into the enterprise.

Pablo Srugo (00:35:36) :
Just to add more clarity here, these enterprises that you're selling to, is it just like 3PLs, but just bigger ones? Or what types of customers are we talking about?

Sean McCarthy (00:35:44) :
No, we're talking like the Fortune 500, Fortune 1000 now and so there was one thing maybe that's helpful is there was a really large switch from in the end of 2025. As well when we kind of made this enterprise grade solution, which was that we were selling primarily to supply chain companies and now we are selling to companies that operate a complex supply chain. So you can think some of the biggest grocers in the U.S., some of the biggest automobile manufacturers, Arma, and what those customers, they might sign kind of all over, but they all operate a hyper complex supply chain, right? So if you take Sprouts or Whole Foods, they're a grocery chain, but they have a very complex supply chain and so that is really who we are selling to now and have found a lot of market pull. And I think we're also not vying to go sell to the same brokerages and some of the other folks that our competitors are, and that's been beneficial for us. So what we'll first do is obviously see if we know anybody. Hands down for us, our ICP has been the senior vice president of operations. I would say ninety percent of the time, so we'll go find that person on LinkedIn. We have sent champagne, we have sent donuts and bagels to warehouses. We will look and see, do we have anybody in common? I'll have as many employees as I can to see, and here's who we're looking to target. Do you know anybody? You know, if we have the cousin's friend's brother's uncle, we'll reach out and say, hey, would you mind saying some words and maybe getting a meeting set up? And so we try to back channel first as much as we possibly can. And when we are approaching these enterprises, we also learned the efficiency playbook and what that means is, going to these customers as an AI company and saying, hey, we're going to make you more efficient. What are your problems? Works almost never because they have so many problems and they usually don't know where to begin. What we've changed to and where we've had a lot of pull is in the grocery example that I gave you, Going with that exact use case to say, we know that you have trucks showing up that have temperature breaches on them, this is exactly what we built, this is what you can expect in the first ninety days, and here's the average that our current customers are saving by doing this. So it's a very pointed, tailored use case to something that actually matters to them and we're seeing that work eight out of ten times. I would probably say, where they will say, okay, this sounds interesting enough, where I want you to have a conversation with the director of operations. And they'll kind of kick us down after that. They'll kick us down to the director of ops, or maybe even the manager of ops, and we'll work really closely with those teams. Doing a lot of cross-threading during that time, showing that we can understand their use cases, building and conceptualizing what it would actually look like for them, giving them custom demos of exactly what they would be getting to get the buy-in and making sure that they have a really clear line of sight. And that we're getting as use cases and all that critical information. And then that's where that presentation comes in. Because most of the time, they're going to give a presentation to that same VP, and that VP is probably going to then give it to the board or the ELT or AI committee if they have one. And so that's where that comes in. We're putting all of that data in there, everything that we've grabbed from the use case information to the ROI analysis and building this deck for them. Sending it to them, so that hopefully it's just one less barrier that we have from an entry point.

Pablo Srugo (00:39:00) :
So that's great as the high level framework. So let's kind of maybe dive into each step there. So the first one is, you know, you identify the SVP ops. Okay, that's easy. Then you go and you figure out if you have, you know, mutual connections. You start pinging. Do you kind of just set up? Okay, we've got these fifteen potential wins. These are the best, this is the best one, the best three, the best five. Let's do those first and see what happens. And within that, how meticulous are you about the messaging? Is it just, hey, you know, we're trying to speak to this person. I started connected, can you reach out or do you have like a much more crafted, very tailor made message you wanted to send? How do you think about all that?

Sean McCarthy (00:39:34) :
Yeah, so we will go off of the strength of the relationship in the beginning. So who has that closest connection? So again, if we pull five people in and we've established who our target is in the funnel meetings. Then maybe somebody knows this person, or again, somebody who's actually close. They used to work together, we'll always take that as like, a warm way in and we're crafting the message that we want that person to send. A lot of people make mistakes as they just say, hey, can you make an intro to Bill? I want to talk to him about what we do. It really doesn't set who you're asking the favor to do. It makes them do more work and it doesn't really feel like maybe it's a good fit. Because they have no clue what you want to talk to them about and so, we'll actually say, we built this, we believe he has this problem, and this is what we've built to solve it, as an example. Or we'll just put a copy paste message of, can you just say this? And just copy paste this to the stakeholder. And so we're trying to make sure that they don't have to come up with this context, because they also might pitch us in a way that we don't want to be pitched. So we'll give them all of the context. We are also cross-threading a lot of the times between LinkedIn and email. Assuming that we don't have the warm intro or we get the warm intro but it doesn't go anywhere. We will cross threads, we will send them an email and a LinkedIn message and they'll be different.

Pablo Srugo (00:40:53) :
Is it usually you or is it an enterprise AE that's doing it?

Sean McCarthy (00:40:56) :
We were founder-led for a very long time, so it was just me. With the enterprise AEs now, they will run them and if they're getting blocked, I will, of course, offer to reach direct to the stakeholder. But right now, it's the AEs primarily and so, they will send the email, and they'll send the LinkedIn, and they're hyper-personalized. So, none of the AI sequencing, I think we're past that, doesn't really work very much anymore in my opinion. So, again, coming back to the basics of that and picking up the phone. We've had a crazy amount of success on the phone lately as well. And so, kind of just getting back to the basics of what it used to be for enterprise sales.

Pablo Srugo (00:41:34) :
And what about in person? Anything in person you're doing?

Sean McCarthy (00:41:37) :
We're doing a lot in person. You know, surprisingly, the conferences have been really, really successful for us. We were just at Manifest in February and actually closed two deals for Manifest already, and so that was great. And I think it's a good way to get in front of either folks that are in our industry or maybe even net new. And then for our actual customers, yes, we're flying. I think this month I'm in six or seven different cities meeting with prospective customers. I think one of the big things that we're also trying to do is these onsite activations with our customers. So if they're just becoming a customer, when we need to get all that context information that we talked about, getting that in person, and even if the contract's not signed. We'll fly out and meet with them and it just helps to conceptualize like what this would actually be like. Because we're workshopping it together, we're answering their questions live. They're bringing people into the room that are potentially going to be the blockers down the line, kind of the gatekeepers. And we're able just to work everything out in person. And this is before a contract is even signed. And so it's an investment on our side to do it, but it's paid off multiple times. And so I think that that's also been a huge unlock, because a lot of this sell is educating the customer on AI as well. And so you have to pair that with making them feel comfortable with what it's actually going to do in their environment.

Pablo Srugo (00:42:54) :
What would you say is your kind of like reach out the first meeting ratio conversion rate if you had to guess?

Sean McCarthy (00:43:00) :
Let’s specifically talk about the large enterprise. I think it's obviously a higher barrier to entry. Of every ten reach outs, I would say we maybe get four that either eventually respond or respond in a fairly quick manner that would probably turn into one meeting if we're lucky and it may be on a bigger scale. And I think if you had to push that out, it probably is something like, again, thirty or forty percent of the time we're getting somewhere on messaging, half of those turn into a meeting, and then call it ten to twenty percent of those actually turn into an opportunity.

Pablo Srugo (00:43:34) :
And if the person doesn't respond, what do you do? Do you just go to a different account, come back later? Do you stick them in some sort of nurture campaign? Or do you go down in the org and you're like, okay, well, you know what, let's find the director levels. Let's find the managers and go after them.

Sean McCarthy (00:43:47) :
We'll go down knowing that we're going to get kicked down to probably that director level or manager level anyways. We will also cross thread there. So we'll go down, contact them and see if we can get some movement there. We won't go to the individual contributor level, we actually have a whole slew of lessons learned on that. So manager kind of being the lowest that we will try to do and then, you know, from there, that's where we do some of the gifting suites where we'll send, maybe champagne or donuts to the warehouse and say like, hey, we just sent you as, you know, something we'd love to talk about ABCD. So that's a possibility. We will also send case studies proactively that are hyper tailored to that customer persona. So it'll just be redacted case study of one of our customers and talk about what they're experiencing, hoping to play off maybe some of the FOMO tactic there. I would say there's no hard set rule that we have to put an opportunity as lost. But if we've reached out seven, eight times, we've tried every angle, and that's seven, eight times, like seven, eight phones, seven, eight emails, seven, eight LinkedIn, and tried back channeling and nothing's working. Then we'll kind of move along there. We are seeing that it does take multiple touches. I mean, again, multiple emails, multiple phone calls, multiple LinkedIn messages before. I can't say the exact name, but I'll tell you that one of the largest beverage manufacturers in the world, it took me eight LinkedIn messages and I think four emails to get the response, to get the opportunity and, you know, I was persistent. I was afraid I was gonna get blocked, but I didn't get blocked, so.

Pablo Srugo (00:45:18) :
There is an element of persistence that is undervalued in the sense that if you're on the receiving end of it, like these people. I mean, they get in pitch things all the time. So actually, probably first impulse on anything, even if it sounds kind of valuable is to absolutely ignore it. If you're reaching out so much, you're either just completely annoying, or maybe you really believe that you can help this person. But you're basically filtering yourself from everybody else just by the sheer persistence and at some point they're like, oh my God, okay, let me read this message. And maybe they're like, you know what? Actually, this is kind of interesting and that's like message number six or seven. Dude, I've been saying this to you, you know, for a long time, but this is what it takes.

Sean McCarthy (00:45:54) :
Yeah, I couldn't agree more. I mean, the old SaaS days, I think it was something like it takes seven touches to create an opportunity and in today's world, it's so easy to enroll somebody into an AI sequence and just hammer them on emails. And I'm sure you see it. I know I get hundreds of emails a day and I think, again, kind of going back to the basics is where this goes. And it's hyper personalized messaging. It is not just on one platform. You're sending them emails, you're doing phone calls, and you're doing LinkedIn. You're trying to get creative with maybe, it doesn't have to be a gift, it could be a number of things, finding the commonality. If we do have somebody that works there, even if they are an individual contributor. Maybe trying to get them to bring us up in passing, or say like, hey, we've even done that where we'll send a case study to an individual contributor, just to send up the stack. If we have somebody that we know really well. But yeah, I think that the persistence pays off, and you have to today, especially in the AI era. Where sending out outbound is easier than ever.

Pablo Srugo (00:46:49) :
And then if you get a first meeting, how do you structure that meeting? Fifty minutes, thirty minutes, an hour? Is it a demo or are you just qualifying? How's it go?

Sean McCarthy (00:46:58) :
So traditionally it's thirty minutes. We kind of go against the grain, I guess, as it relates to running a playbook and doing just discovery, and making sure that you're deeply embedded on that first call. What we find often is that we have to demo on that first call, and really just so that it conceptualizes what we do in, AI and the impact to the customer. Otherwise, they don't quite understand what it is. You can talk a little bit about it, but they need to see it. So we will demo, we'll get a little bit of information. We have a demo that we have a number of demos actually depending on what they tell us is relevant and we'll demo that. And then from there, we'll collect information. Almost always, they're going to bring in more team and so if they do that, we'll try to get more information from them as it relates to what they liked. What was relevant in that demo to you guys and what was not relevant. What are some of those things? And we can make sure and customize that demo a little bit next time for the broader audience. Sometimes they're so excited, they'll offer, we'll offer to sign an NDA. So that they can give us some of that information right off the bat so that the next meeting we have is hyper focused on their use case. But that's traditionally, it's usually thirty minutes, forty five minutes, we do the demo, and then our ask is always to make sure that we have the relevant stakeholders in. That are either going to help us shape the use cases or utilize the product or are going to have that signing authority.

Pablo Srugo (00:48:17) :
On the demo, what have you found is that wow moment and how fast are you able to get to it?

Sean McCarthy (00:48:23) :
We intentionally insert two wow moments in the demo. Essentially, we have a couple of different demos, but the first is around the intake from, we can show voice or email. Voice tends to get a better wow factor where it can be in a warehouse. You know, for a lot of our customers that are at a loading bay, and calling in with a problem. And then very clearly, they're able to articulate what's happening, but then the AI will start to go chase the information. So send a Slack to Pablo, call Sean, log into FedEx, like all at the same time, to pull this route to result. And that is happening before their eyes, so that's been powerful for them to start to see. And then the second and kind of ending part is the resolution. So now we've got all this aggregate information, we have enough to solve, but it shouldn't still go to a human for then a human to input into a system. The way that this should work is that we should be able to update QuickBooks or the ERP with the financial outcome and then update the warehouse management system with maybe the relevant stock or whatever happened there. And so, actually showing the systems being updated and the ticket being resolved across the board. And so, we try to always have at least two wow factors in the demos and even the custom demos that we do. So that they can understand not only what this looks like in practice, how their customers are going to utilize us, but internally what it's going to look like as well for them. Because I think those are two different experiences. They're worried about the customer experience, but they're also worried about the employee experience and so I think if we can make each wow factor around both of those elements, it usually sits really well.

Pablo Srugo (00:49:59) :
And then from there at that point you have interest. You're trying to go to something, probably like some kind of a pilot and the biggest thing I found is just the challenge, like the failure mode is you just get stuck somewhere or it just starts taking too long. And, you know, time kills all deals, priorities change, whatever. Because you're already in at that point. Clearly, they like what you have. What have you found works in order to shorten the time between interest and some closing of some sort, even if it's a pilot?

Sean McCarthy (00:50:27) :
I think the pilot thing for AI startups can be very dangerous and we've learned with lessons too. I think, you know, when we look at those pilots, and what I say by learn by lesson. You don't want to after the pilot be in this purgatory of, where is this going to go? Yes, we're going to go to commercial contract, but that's got to go right back through legal and you know, you might be still having your solution deployed. And so I think it's a dangerous area. The way that we do, we always try to push for a one year contract. But if we have to do a pilot with many of these large enterprises, they require some sort of observation phase. We'll build the pilot into the one year contract. So it's a one year contract, but it will include a thirty day pilot, sixty day max, but thirty day hopefully pilot that will auto convert to a one year. And that way, all of the legal documentation and infrastructure and all of that is pre-approved, it's all part of the normal agreement, and everybody's on the same page. Obviously, you need to make sure that you deliver in that thirty or sixty day window so that you can get that auto conversion. But that's hands down the best way and to answer your question, when you talk about like, how do you get it, that's a really good way. Because it still is a light enough ask to the customer, their feet aren't necessarily held to the fire if they don't like it. But on our side, we've got some insurance that as long as we do what we said, we have that commercial agreement.

Pablo Srugo (00:51:45) :
So I love that idea. But between them saying, yeah, okay, this is interesting and then them signing that one year contract with let's say with a pilot out.

Sean McCarthy (00:51:53) :
So number one, making sure that the use case that you're talking to them about. I do this whole analogy with our customers. So I'll say, all right, so one to ten. One, it doesn't matter at all to the business. Ten, business goes down tomorrow. Where is this use case for you, right? And you're hoping for the seven, eights, nines, tens, right? Being realistic, they're probably going to give you a seven or an eight, hopefully as kind of like a max starting point. They might not give you the most important critical thing. But if they say, oh, this is like a two, right? Or a three, you shouldn't take that use case. Especially as the first one, because it doesn't mean that much to them. It's not going to make that much of a difference and your pilot is probably not going to go very well. And so what I'll couple that with is, let's say they give me a seven. Okay, great and then I'll ask, how many times a day does this happen? That's the other thing. You don't want it to happen once a month or four times a month. That's also bad. So hopefully it's happening every day. It's a seven, great. Where does this fall for you guys on a prioritization scale? I mean, is this painful for you right now? And if so, you can kind of quantify and back in and on the ELT side, they'll say like, yeah, we're losing money on this. So this is high priority and often they'll say, yeah, this is high priority. We do need to get this fixed. Great, well, let's get it fixed. We can actually do time to value here in probably three or four weeks to get the initial use case handed over to you guys. You can measure it. You can measure down to the second how our AI works. If it's monetary, you're going to see what that is on our dashboard. And you'll hopefully be able to fill the gap here. And this is the best way for you guys to just measure fast. And that's usually where we're pushing it again, especially if we have to get them into a pilot. But if you ask them, how big of a priority. Especially if they're giving you those six, seven, eight, nines, they're going to tell you it's a priority and so you can play off of that. Like, hey, you just told me this is a really high priority. So let's get this in flight for you.

Pablo Srugo (00:53:40) :
Perfect, let's stop it there. I'll ask the last question, which is what would be a top piece of advice? Maybe a piece of advice you wish you knew when you were starting, you know, two years ago.

Sean McCarthy (00:53:50) :
I think the biggest piece of advice is to be honest with yourself as a founder as well. It's very easy to convince ourselves that either the problem that we're solving is really, really mission critical. Even though it might not be, or that you get one customer telling you that it is like super important to your business. You just take that and run as a blanket statement for like that entire vertical. Because one customer told you that. I think you need to make sure you do enough diligence and be honest with yourself on a retro the feedback that you're getting. And I tell you that is like an important lesson because I've made those mistakes in blitzing our entire team based on one or two customers telling me some product feedback and going off the rails. And quickly learning like it was just those one or two customers. It was nobody else that needed that and so I think that that principle from the very earliest stages, like you're trying to raise a pre-seed round and just figure out what you're going to do. All the way through, even past where we are from an organization, you have to make sure you have a decent sample size and that you operate off data, and not gut checks. And that's been, for me, probably one of the most powerful lessons. And frankly, one that, you know, I still make the mistakes on today.

Pablo Srugo (00:55:04) :
Sean, thanks so much for jumping on the show, dude. It's been great.

Sean McCarthy (00:55:07) :
Thanks, Pablo. I appreciate you having me.

Pablo Srugo (00:55:08) :
So picture this, it's months from now, years from now, and one of your founder friends. A really close founder friend of yours, guess what? Their startup went bankrupt, and it turns out if you had just shared the Product Market Fit show with them. They would have learned everything they needed to, to find Product Market Fit and to create a huge success. But instead, their startup has completely failed. You have blood on your hands. Don't let that happen. You don't want to live like that. It is terrible. So do what you need to do. Tell them about the show. Send it to them. Put it on WhatsApp. Put it on Slack. Put it where you need to put it. Just make sure they know about it and they check it out.