How to Build a $30M ARR Business with Just Three Founders and ZERO employees – Amos Bar-Joseph, Co-Founder, Swann

Are you struggling to scale your cybersecurity sales team without ramping up headcount or getting drowned in technical complexity? Wondering if AI can really move the needle for high-value, complex sales cycles? Curious how leading founders are reimagining go-to-market operations to win in today’s fast-paced, AI-driven environment? This episode of the Cybersecurity Go-To-Market Podcast dives right into these pressing questions.

In this conversation we discuss: 

👉 The ground-breaking approach of building a $30m ARR SaaS company with only 3 founders and a suite of AI agents and no additional employees. 

👉 How AI-driven agentic workflows can help sales teams focus on high-value tasks, remove technical complexity, and iterate at the speed of thought. 

👉 Practical strategies for cybersecurity companies to integrate AI into their GTM motion, while building trust and adapting to their unique sales cycles and ACVs.

About our guest:
Amos Bar-Joseph is the co-founder of Swan, an innovative SaaS platform that enables human-AI co-selling at scale. Drawing from two previous B2B startup journeys, Amos is on a mission to rewrite the go-to-market playbook, making businesses radically more efficient by scaling intelligence, not headcount.

Summary
If you want to understand how AI agents can help you work smarter, not harder, and what it would take to scale your cybersecurity sales function without more bodies, you can’t miss this episode. Tune in to discover actionable insights from founders who are paving the way with new GTM models that are designed for speed, adaptability, and intelligent automation. Listen now!

Links & Resources

• Connect with Amos Bar-Joseph on LinkedIn
• Learn more about Swan

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Transcript

Andrew Monaghan:
You know those posts on LinkedIn from people who tell you how they booked 100 meetings in two days using their amazing proprietary AI workflow? And you just comment, I'm awesome down below. You'll send it to you, and then you don't get it. Yeah, look, it's all the time right now, but I think it's indicative of a confusing market. Lots of people claiming big things and some people doing it, some people. Not a lot of skepticism out there. And there's lots of people right now struggling to realize the real value of AI in go to market in cyber. Which is why I'm excited to introduce you to Amos Bar. Joseph Amos and his co founders are building Swann which turns any go to market idea into an agentic workflow in seconds just by chatting in Slack, which is kind of interesting, but it's not the really cool part.

Andrew Monaghan:
All right, the really cool part is that they're drinking their own champagne and are building Swan into a $30 million ARR company just with the three co founders and a bunch of AI agents. That is no further employees. So just think about that. $10 million in ARR per. Per single person involved in the company. Is it possible? Is it not possible? Is it intriguing? Will it work in cyber? Well, find out in this episode. I'm Andrew Monaghan, and this is the Cybersecurity Go to Market podcast where we tackle the question, how can cybersecurity companies grow sales faster? Amos, welcome to the podcast. How are we doing today?

Amos Bar-Joseph:
I'm doing well. Thank you for having me, Andrew.

Andrew Monaghan:
Well, you know, I get. I get too many, frankly, outreaches from podcasting companies, booking companies, sometimes individuals themselves, you know, pitching me why someone should come on the show. Right. We're not a big show, but, you know, we've been around for a while. But, you know, I get everything from, you know, John knows all about how to build a dentist practice. Bring him on your show. You know, I get all the weird and wonderful things, and most of them I pretty within a couple of seconds, I just zip past. But there was something that, when I got your reach, I thought, I think this is what this is telling me is we have three founders at a company building it to $30 million in AR with no employees beyond the three founders and using AI agents.

Andrew Monaghan:
And I sat there, I looked at it twice and went, all right, I need to learn about this. That's really cool. So if I got that right, is that what you're doing at Swann?

Amos Bar-Joseph:
Yeah. So the short answer is yes. The long answer is it's a bit more to that. So it's not my first rodeo, actually. I've built and scaled to B2B startups and the customer communication space based on the old growth at all cost Unicorn playbook where you raise a lot of money before you know anything about product market fit. You get 30, 40 people before you got your first million dollars in revenue. And then each round you try to increase your total addressable market and division rather than the real metrics of the business. And sooner or later you understand you built your entire company on a very unhealthy foundation where you just throw bodies and money at every problem basically and hoping that it will just solve itself.

Amos Bar-Joseph:
And with Swan, I felt like, you know, it's my time to reinvent the playbook. It hasn't been modified or updated for like 15 years of how you scale a business from 0 to 1 and from 1 to 10. And Swan is an attempt of actually rewriting that playbook that is really designed for human AI collaboration rather than human to human coordination, which is the old model. And you know, SWOT is a business that is architected from the ground up to scale with intelligence, not headcount. And what we're trying to design is a system that leverages AI as a force multiplier. And we're looking at each person, at the team and trying to ask ourselves, how can we turn each person into their 100x version themselves, that 100x GTM person, that 100x engineer, and we're just three founders and a bunch of AI agents. We have more than 200 customers and we're scaling fast, so things are working ish, you could say a lot of things are breaking and it's hard, but we're thinking that we're onto something and.

Andrew Monaghan:
Three founders and you want to make 100x versions of yourself. So what, what do you each do that says, okay, this is my lane that I'm in and therefore I'm figuring out how I can scale without scaling employees.

Amos Bar-Joseph:
Yeah. So I'm in charge of revenue and growth. Basically. We have ido, my co founder, who is building basically products and leading the product and also building all the intelligence backbone in the company. We're going to talk about that, but basically helping build internal agents. And then we have NEV or cto, which is more like the technical person on the team. And that's all you need basically to take a business from 0 to 1, even from 1 to 10. In that sense you need growth product and R and D.

Amos Bar-Joseph:
And I'm a one person GTM department and I use AI agents to operate at enterprise scale but at startup speed. And I'm in charge all the way from generating to the demand, turning into qualified meetings, turning these meetings into revenue and then making sure that that revenue is onboarded properly.

Andrew Monaghan:
All right, so that's the bit we want to get into. Right? Because I'm kind of fascinated being, you know, the go to market side of, of this. But let's, let's address something up front because you know, I looked on your website, you get a pricing page and you know you're comparatively low acv. Right. For someone coming in to, to start using Swan, cybersecurity is not like that. It has long complex sales cycles dealing with multiple stakeholders, you know, you know, higher, much higher ACVs looking at, you know, I don't know, not uncommon at all to be above a quarter of a million dollars. Right. So the question really is where are the boundaries? Like where, where we can't just take what you're doing at Swan and say, oh, this is going to work in a, in a much different world.

Andrew Monaghan:
So how do you think about the boundaries or where things start changing in terms of being applicable for the cybersecurity world?

Amos Bar-Joseph:
Yeah, so first of all, we have a lot of customers that are cybersecurity companies and we're actually, we're seeing from the inside what's working and what's not. And you have this rule of thumb that the higher the average contract value is, the more human in the loop you need to have basically in your GTM operations. Okay. That's kind of like a rule of thumb. And you know, the lower the acv, if you go all the way to prosumers, if you'd like like companies like Monday.com or Asana that they have like this $19 entry point, then it starts with no human. Right. And then you go to the other side of that spectrum to the extreme you get like let's say $500,000 a deal and that's like superhuman heavy. Everything is human, human, human and that specific.

Amos Bar-Joseph:
Right. So first of all you can look at it as a rule of thumb, but I would say that thinking of it just from an ACG perspective misses the point of how GTM organizations should leverage AI. The way that we think about it in Swan and actually our product supports it. We'd love to explain later how is you should always look at the adoption of AI in a human AI collaboration point of view. So what we help our customers build is a machine that allocates resources between AI and humans in the way that fits your unique go to market punch. And every business had their own unique DNA in their sales organization, for example, their own unique buyers or value proposition, et cetera. And it's the combination of all of that that yields a very unique combination of human AI collaboration. If you're a heavy cold calling organization, that's what you're good at, that you want to use AI to maybe automate research and all the mundane parts, but just make sure your sellers are all the time picking up the phone and saying the right messages to the right buyers at the right time.

Amos Bar-Joseph:
Right. So you want to support your cold calling organization. If you're heavy on LinkedIn, for example, then, okay, you want to build a system that amplifies your sellers on LinkedIn so they can build, you know, social trust and they can start the right conversations, et cetera. So it's not like there's really a rule of thumb. Every organization has their own characteristics and need to understand how can we gain unfair advantage by cracking the code on human AI collaboration? It's both of these resources that we should understand how to leverage them in tandem.

Andrew Monaghan:
So people use the word copilot. Is it like that or are you saying, look, there's. It's not really copilot, it's much more, I don't know, co workflowing type things.

Amos Bar-Joseph:
Yeah, co workflowing is an interesting term, Andrew. I would say more to that. I think the number one reason for AI implementations to fail these days in GTM is that you're just looking at what you want the AI to replace. You're saying, oh, you're doing that now? We want the AI to do that, but it's the, it's the wrong type of thinking. What you should ask yourself is, okay, we have the talent, we have our talent stack. How can we build a system around them so each person on our team becomes a 100x seller eventually? And you know, if you're working at an enterprise and you have like, you know, 2000 Salesforce, then maybe you can, you should think about it differently. But as long as you're not there and you're a startup in that sense and you're trying to sell, you have less than 200 sellers on your team, which is pretty big salesforce, then you should still look at how can I turn each seller on my team into their 100x version, each marketeer on my team into their 100x version. And if you put the human at the center, what you start looking at is how can I use AI to automate the mundane but to amplify the core, to amplify what's in our zone of genius, what we call it swan.

Amos Bar-Joseph:
So you need to understand what's your zone of genius in your gtm. And each organization should over time figure that out. Are you a cold calling organization? Your reps should excel at cold calling. How can we turn them into the best cold callers in the universe? And so when you look at that, AI can start automating research and CRM entries and whatever, but it's all in the context of just making time for the reps to just make the best calls all day long. Right. And it's a different type of mindset and I think go to market leaders should start adopt that human centric approach to AI implementations.

Andrew Monaghan:
Yeah, I think this is interesting area because you know the profile that I tend to work with the GoPro using five and I don't know, 25, 30, 40 sellers depending, you know, how mature the company is. And it's all about in my mind how do I, how do I make these people have more conversations with high quality opportunities in the ICP and get so good at running the, let's call it the mid funnel of the sales cycle, mid to end funnel that they're not spending all the time or what they often don't do is don't spend any time doing which is all the upfront work, the building and all the rest of it.

Amos Bar-Joseph:
Right.

Andrew Monaghan:
One area that kind of fascinates me a little bit is if you take a sales team of let's say 10 people at a startup, they'll be allocated probably either a territory or a bunch of accounts. They'll say look, it's unrealistic, but here's 200 accounts. You got to figure out which accounts you go target and which ones you get into. Or they got territory and just says look, everything in these states where you go and, and it's a lot of accounts. And the first struggle for the rep is how do I even know which of the right accounts I should focus on beyond the mundane of size and vertical market and things like that. Right. So this idea of signals I think is an interesting one. And talking about problems in implementing AI, one of my clients last week told me they blew, I think they blew like 20 grand in AI credits because they use clay and it, I don't know, it kind of went out of control or something and they got a real shock at the end of the month going what the heck just happened? There.

Andrew Monaghan:
So how would you think about that with Swan, about the whole signals element of saying give me the 10 of the 200 that are much more likely right now because something is happening that says this is the accounts that I should go spend time with.

Amos Bar-Joseph:
Yeah. So I think something that is changing fundamentally in how we're approaching go to market is that everything is becoming a question of resource allocation in real time. Okay. And I think that, you know, we used to have this territory planning everything is in advance. This is my plan for the next quarter. We were so like not real time and we didn't have this notion of resource allocation baked into our thought process enough. And today you can leverage AI to just, @ any given point of time just tell you, okay, this is how you should allocate your resources to these accounts. Because this week we have a good angle, next week it might be different and you need to reallocate your resources accordingly.

Amos Bar-Joseph:
And we were used to working kind of like in these sequences where, okay, this is the account I'm working on. I'm going to spend all my time for the next 45 days hacking into that account. If I can't make it, I'm going to throw it away and move to the next account. But what AI can allow you today is to actually work in tandem on all of your accounts in that list. It's not like you need to pick one at any given point of time. You have an AI agent that can tell you is there an entry point right now or not? How high is that the level of conviction that we have based on these signals? And if the timing is right, you can just shoot off a short sequence and just test the waters. If it makes sense, there's a reply that's great. If there's not, okay, let's wait for the next opportunity.

Amos Bar-Joseph:
I always have opportunities to reach out to these accounts and I want to be mindful, just making sure that I'm adding value to the buyer. And basically what we realize at Swarm is that specifically for startups, and we talked about your icp, Andrew. These type of teams don't have a huge rev ops team internally. And basically what they need is a living system that they can adapt together to their growing understanding of their market and how to use AI in the context of their market. And so Swan, in contrast to actually tools like clay or other ABM tools that are very complex and cumbersome and requires a lot of technical work. And if you don't spend that technical work, you can may end up wasting 20 grand on credits with Swan. Swan is actually an AI go to market engineer. It's kind of like a technical resource that works with sales and marketing to turn any GTM process into an agentic process.

Amos Bar-Joseph:
So they could scale that process with intelligence, not headcount, and most importantly, iterate on that process at the speed of thought. So the way that sellers work with Swan is they can see, for example, a prioritization of accounts based on signals, but they can just tell it, you know what, let's change the way that we're prioritizing it because I actually think that this signal is actually more important. And you know what, I think that this is how I want to approach today's work. And so Swan is kind of like a living system. It's not staging and it's not like you needed to invest three months and just building it out. This is what we see is what we get. So one can adapt to what the sellers understand in real time, to how the organization understands their evolving AI capabilities in the market and their value proposition. And we believe that a go to market is always in motion and so does your AI system too.

Andrew Monaghan:
So in that mode, then let's get practical. Would we, if you were the head of sales, would you upload the list of accounts somewhere and say, okay, Swann, here's what we think are the right triggers or signals start telling us which ones to work on. Is it as simple as that or is there more work involved? How does this actually come to reality?

Amos Bar-Joseph:
Yeah, so first of all, Swann lives on top of your CRM. Okay? So that's like a design principle that we have that we're not building another system of record. Okay. We don't want you to start uploading lists to other places, et cetera. Everything that happens inside your CRM we can just work with. In that sense, you work with Swan in natural language, okay. And Swann has access to, for example, intent signals. So let's take, if someone visits your website and don't convert, okay, Swan can de anonymize it, understand who they are up to the person level, for example.

Amos Bar-Joseph:
And what it helps you do is build an agentic motion of how do you want to work with these types of leads, for example, okay, so it comes with a lot of preloaded best practices. Okay. For example, it knows that if a lead visits your website and they're already in the pipeline, Swann will automatically notify the owner of that account, right, in Slack, and we'll tell them, yeah, this person in your account just visited your website. Maybe there's something in the deal that you know, is waking up here. But let's assume that the organization wants to do something differently. Maybe they want to say, yeah, you know what, but if they visit our website, I want to update the CRM as well. And also I want to loop in the sdr, just different type of motion. They just tell Swan, look, Swann, this is what we want to do in case the visit is coming from the pipeline.

Amos Bar-Joseph:
Then notify the SDR based on this assignment logic and update this property here in the CRM. They tell someone to do it. From now on, Swann will perform that process for every every new lead that comes that visits their pipeline. So it's easy as just sending a message to Swann to have these feedback loops. And what we realize Andrew, is that God is in the feedback loops, not just in the onboarding.

Andrew Monaghan:
So in that model then I would let you say, look, if someone comes to visit the website and they're from an account that's already in, you know, the pipeline as it were, you know, just notify the AE because they've got a deal running. If it's not in the pipeline, but it's in the CRM, send it to the sdr, give them the context and kick off this sequence or whatever it might be that they should be doing. Right?

Amos Bar-Joseph:
Yeah. And so that's how you work with swan, basically. But it already comes with like these preloaded scenarios. For example, it has a closed loss scenario, right? What if the visit is actually a closed loss? We want to handle it differently than if it's just, you know, a contact in the CRM without any previous engagement. Right. So it has all these previous scenarios, but you can just tell it how do you want to handle it differently, etc. And just teach it how to work like you would onboard an engineer in the go to market organization that works for you 24, 7. And what that creates is two things.

Amos Bar-Joseph:
One, you can experiment at the speed of thought so you think that this is the right process to handle closed lost, you know, accounts that visit your website. You don't know, you think and maybe tomorrow you have another idea so you can change it and you can operate differently. Maybe tomorrow you have another idea and you can change it. And that worked perfectly. So you iterated at the speed of thought. Just you need to tell Swann it works and you realize that's the motion you working and is working for you. Now you can scale it with intelligence, not headcount because you can start allowing SWAN to take more and more responsibilities over that process. Just make sure your humans are focusing on what you believe they should spend their time.

Andrew Monaghan:
And let's take our scenario then. I love the concept of this. I'm trying to think, you know, for some of my clients and people I'm talking to how this would work. I got another client who they're in a submarket inside which is kind of new. And one of the things that they like to see is hiring signals from an account. So you know, oh gosh, suddenly, you know, in the last week these guys have posted three jobs with certain job titles or certain skill sets required. This is a great indicator that they're investing in this area. So instead of a website visit, is that something that you can keep monitoring for over a list of accounts and say okay, paying, you know, if the 200 these, this one or two are suddenly showing their hiring.

Amos Bar-Joseph:
Yeah. So we're constantly expanding kind of like our signals. And what started is initially we just had intent like website visitors. Then we expanded into LinkedIn Signal. So people that are attracted with posts and liking and commenting and a lot of like, you know, things that might Signal awareness in LinkedIn for your domain or type of value proposition, et cetera, you could start working with that. And we have job changes and hiring, which is also another interesting signal that we offer. And then additionally on top of that, any CRM event could actually be a trigger for Swann as well. So what people undervaluate, undervalue is how much first party data people have in their CRM.

Amos Bar-Joseph:
And over time, you know, if someone downloaded a report of yours and you know that they were already visited your website and you know that they're commented, they commented on LinkedIn posts. When you start stacking these signals together and combining with your first party, that's when it comes becomes super impactful. And what we've seen is that one signal is powerful, but actually seeing multiple signals, that's an exponential improvement on conversion. Because if someone visits your website once then that's cool, but if they visit your website twice, that's a total different stage at funnel of how interested they are in your product. And if someone is just hiring for a specific position, but for example also the CTO has just been released and maybe there's a new cto. So connecting the dots might mean that there's a major need right now at the company. So stacking signals is also something that becomes super easy when you have an intelligent agent that can just look at the data and you can just talk with it and make intelligent decisions upon.

Andrew Monaghan:
That Data and that was kind of the promise of these. I mean Sixth Sense is the one that people talk about a lot, right? It's like the intent based companies. I'll tell you just from experience, it's kind of interesting. I think probably more on the marketing side maybe. I used to always get frustrated on the sales side because they say, oh look, there's really high intent from Wells Fargo Bank. And you go, Wells Fargo bank's probably got 10,000 people in it. Help me out here, who's the one that did it or how do I do this or whatever. And even these days I use, in my business I use something called, you probably heard RB2B, right? And I, I look at the data it gives me and it, I think it's trying to give me individuals.

Andrew Monaghan:
But I mean, I'm going to guess a lot of this is not highly accurate because I get, I'm trying to give you some examples here. I'm scrolling down here. There was someone from a parole officer, here's someone from an aluminium company. You know, I don't know what they're doing to try and connect that, but it seems like it's a little bit off. How are things advanced in this so that the sales, because the salesperson loses confidence in it very quickly if they go, okay, you're telling me that the CEO of an aluminium company is my high value target right now, so how do they advance so that the sales team actually has confidence in this whole process?

Amos Bar-Joseph:
Yeah, that's a great question. And it's actually one of the reasons why we started Swan actually. So Swan is an AI go to market engineer, but the areas that we directed it at initially were the areas that require the most engineering to actually work with data. And these areas when we started was intent. Why? Because if you look at companies like six Sense, they just give you the data. And GTM organizations today don't need more data, they need the ability to act upon data. And so swann as a go to market engineer helps you connect the dots so you can really create a workflow that is end to end that not only creates the data but helps sales act upon it. And so what you can start doing basically for example with RB2B.

Amos Bar-Joseph:
So for the folks who are listening to us, RB2B provides intent data like de anonymizes your website visitors up to the person level. Right. So it can give you actually personal identifications. But what SWAN can do on top of that. So you can connect RBT to Swan or you can use our own intent data providers And Swan can start a filtering out the noise for you. It can go over these leads, say yeah, you know what, these are maybe false positives because you don't really get patrol officers that visit your website and just focus on if you know the head of GTM at a new cyber security startup visit website that might be more accurate in that case. So it can a filter out these leads, surface the sales only the leads that you think as the person working with Swan that they would want to see. Okay, you can, Swan can provide them with all the buying committee already.

Amos Bar-Joseph:
Okay, these are the contacts that you need to work with in this account. It can provide them with their contact details with an ABM report about that account and with a personalized messaging, you know, as a recommendation for them to reach out to any specific individual within that company that is within their buying committee. Still the seller is at this point of focus here and Swan is just preparing all the work so they could just, you know, think of the strategy and just focus on the leads that are actually valuable to them that they want to spend their time. And so when the moment that you remove all the technical complexity that stands between your ideas and their execution, so that's what Swan does for you, then you can start making sure that your sellers are happy with the workflows that you're building for them.

Andrew Monaghan:
So this is, this gets really interesting. Right? So get, go back to our example. You got a rep at a five to ten man team. This reps got their 200 accounts. You know, they get something in their Slack that says Wells Fargo. This person came and did whatever. They've got these new job titles getting advertised for jobs right now. The CTO just got transferred, transitioned that new one coming in.

Andrew Monaghan:
And here's the messaging, here's the, the way to approach them. Here's the, here's the research report on, on you know, the value hypothesis about how we could actually engage with them. That's all going to be delivered into my Slack. Is that what you're telling me?

Amos Bar-Joseph:
And just to take it one step further because we know that sellers, they're not lazy, but they love things handed to them on a silver platter. So they have a button.

Andrew Monaghan:
Oh, we're lazy. Let's, let's be honest, we're lazy.

Amos Bar-Joseph:
Maybe that. You said it Andrew. And so they get on Slack everything that you mentioned, but there's a button in Slack that they could just click on it if they want to send that email. They don't need to copy paste it even and go to somewhere Else, even if it's a LinkedIn connection request or a LinkedIn DM, they can just click on that button Slack or edit it and then click on it and it will just send it over. So what happens is that they get the data and the time between seeing the data and making an action is like 60 seconds and the quality of the action is unprecedented.

Andrew Monaghan:
Yeah, that's if I've got, if I'm a seller, I've got trust in that. If I got trust in the accuracy of the information and what I'm going to be sending them, I feel like that's groundbreaking for me as a, as a seller. Right. If I don't have the trust, I'm going to sit there and go, let me just dig in a little bit deeper. And before you know it, you spent an hour, you know, trying to find all the reasons why the data's all wrong or it's not going to work for you, I would imagine. So it's all about trust.

Amos Bar-Joseph:
I, I will tell you Andrew, that you, there's a lot of companies right now out there that are trying to build towards the signal, LED motion, the ATM motion. But what we've realized that for startups, again, you know, as you mentioned below the 50 landmarks, you know, 50 sellers that work complexity is their biggest enemy. Technical complexity is their biggest enemy. The fact that they need to invest in a company like 6th Sense or Common Room or Clay to just spend three months just to build one motion, that's the reason why they will fail. That's their time to market is their most important metric. They need to move fast. And technical complexity is what's standing between them and winning for most of the times. Because if they will just be able to move faster, they will experiment more, they will realize what's working and what's not working faster and then they could just invest more resources in the motions that are working for them.

Amos Bar-Joseph:
Right. And so the way that Swan is really different from the entire market in that sense is we actually took all these AI capabilities and we packaged them so they could remove all the technical complexity in your GTM stack, basically. And if you look at the fast growing startups today, you can look at Cursor, if you're familiar with Cursor or Lovable, these are these AI developers and these are the fastest growing companies in the world. And you look at companies like 11X or AISDR or Artisan, these are AI SDRs that solutions in the go to market space and you look at how fast they are growing, if they are at all. But what you learn from that if you compare these two types of companies, is that today AI is much better at engineering than mimicking a seller. And what you should focus on using AI is to harness AI capability as an engineer, to remove all the technical complexity so your sellers excel. So your GTM creativity is what stands between you and your competitors and why you will win them. And so I think that that's something that the audience should take from this conversation.

Amos Bar-Joseph:
To actually look at AI as an obstacle remover rather than a magic wand.

Andrew Monaghan:
That's an interesting area because I've said it to people before, somewhat tongue in cheek is the richest people in go to market right now are the clay agencies. The. The clay agencies that try and make clay work for people. Because you need to, it seems to me you need to have a real expertise in how it works and how to use it best to get the most out or get anything out of it. Never in the most out of it. And then of course, you get examples like I had there earlier on with, you know, they blew $20,000 just like that. Cause they didn't get quite right. But I think what you're telling me is that you're less reliant on that level of expertise than with Swan to make it work.

Andrew Monaghan:
Is that what we're saying? Or you still need expertise? It's just a bit more elegant? I'm not sure.

Amos Bar-Joseph:
Yeah. So you don't need expertise. We believe that technical literacy is what is fading out of the GTM world. Okay, that's what's fading out. You don't need to understand tools anymore. You don't need to learn tools. You need to understand GTM playbooks, processes and how your systems are connected. But you don't need to understand the language of how to work with each tool.

Amos Bar-Joseph:
That is something of the past, basically. And we believe that clay was actually built on the old model, the complexity value proposition, like Salesforce, like HubSpot. You build a very complex tool, you build an implementation ecosystem around it, and now your customers are stuck with you because they cannot just, you know, they cannot do it themselves and they cannot migrate because everything is built on your side. And what we're actually doing is we're replacing the need to have these consultancy agencies that are just proficient in a tool because SWAN is that implementation agent in that sense. Swan is acting like the clagency in that case. And what we're actually helping is the GTM operators that brings experience to the table. They understand go to market, they understand playbook, but they didn't learn how to work with an Excel sheet new tool. So they don't need to learn that now they have Swan.

Amos Bar-Joseph:
They just need to tell it what they need and it will, you know, develop that for them.

Andrew Monaghan:
I want to go a little bit better right now almost with you. So, you know, we've been talking about go to market for the last probably half hour or so right now, right? We've been talking details. You know, I've been asking questions about details. And one of the things I'm getting from you is really a lot of thought and different viewpoints about the overall things that are happening in the market. Right. So I'm going to ask you more about Swan and how you're doing things. I kind of like in this. It's almost like you're building this movement, right? You're trying to shift the think in a way, cybersecurity is important as well, because there's so many companies all trying to do their thing.

Andrew Monaghan:
You need to think about how do I position what we do in a way that people stop and listen as opposed to go, this sounds like the next guy, right? So you're taking me out of features, you're taking me out of logical stuff and talk about big changes. How did you, as you were thinking through how to advocate and evangelize for Swan, was this a natural thing you just started doing? Or did you as a team sit down and say, look, there's some key messages we need to get across that's going to enable us to really stand out here.

Amos Bar-Joseph:
The short answer, that it actually started from resentment against the system.

Andrew Monaghan:
Okay, chip on the shoulder. I love the chip on the shoulder. This is awesome.

Amos Bar-Joseph:
In my previous two startups, I went by the regular playbook and I felt like something is wrong basically by following that path. And when I started Swann with this notion of AI agents coming, I felt like we need to do things differently. At Swan, it's time to actually, the playbook is not working and you need to think first principles. And then we started thinking first principles about everything. And what we realized soon is that most of the conventional, quote, unquote wisdom at the moment is actually misleading you. Most of these discussions are driven by fomo, by fear, not by optimism, not by the opportunity, but actually by fear. Fear of being replaced. If AI won't replace you, an SDR who knows how to use AI will replace you.

Amos Bar-Joseph:
Everything is just about fear, fear. And what we try advocating for is conversation that sparks optimism, that sparks hope within our audience. And we started Realizing that that's actually the future, that is more sustainable, that we think that people will actually fight for and will drive more economical value eventually. And we started building our whole thesis around it.

Andrew Monaghan:
Yeah, this is, I talk about there's the logical, there's the emotional is a philosophical, right. When you get to philosophical, it's like, you know, you use word resentment. It's like the epic injustice. Right. There's just something wrong with how things are being done or happening to people or whatever. And when you get someone leaning in on the epic injustice, then that's when people take it to the streets and, and want to say this wrong. We need to change things. Right.

Andrew Monaghan:
They join you in this big movement and it sounds like, you know, you said you want 200 customers plus and you know, people joining you on this movement right now.

Amos Bar-Joseph:
Yeah. And what I've realized in this round with Swan in Go to Market is that people respond to emotional arguments more than to logical arguments. And I was a very logical person before Swan and I, you know, as a seller I always felt like this is the value proposition. This is, you know, the problem, these are the buyers. This is how I penetrate everything. All the plan was formulated against my eyes and sometimes I would lose accounts and ask myself how did, did I lose it? I can't understand how come the buyer didn't have enough urgency to buy my solution. But it felt like it did happen and today I can understand that. What drives people more than anything is their emotions basically.

Amos Bar-Joseph:
And I feel like it's hard for a lot of people to maybe to grasp their hand around it. But for me in this round, when I'm talking about the story of Swanon, I'm talking about, you know, the emotion, you know, the positive future of human AI collaboration versus these negative fear driven discussion around AI replacement. And I feel like, you know, this optimistical narrative is what driving a lot of our customers because they feel like, yes, one is advocating for a future where they actually want to go towards rather than feel like they're escaping from something.

Andrew Monaghan:
Yeah, this is fascinating. I think this is a good parallel for cyber, right? Because you know, cyber is very confusing. There's all these submarkets and they overlap and if you get lost and they're trying to justify, well, your feature is going to do this and their feature is going to do that and how our features, it must be the same in your world where everyone is either bamboozled by AI or has fear about AI or thinks that it's all the same and whatever. Right. And somehow how do you get someone to get out of that and just want to lean in with you? It's such a powerful thing to be able to do.

Amos Bar-Joseph:
Exactly, yeah. And people don't really care about features. These. And I think something like old school thinking is thinking by features. Because if you look at 20 years ago, you had like an RFP as an enterprise buyer, you pushed an RFP and you said, these are the features that I want in your, in my software. And if someone would actually manage to build these features, you said, wow, this is an amazing vendor. I can buy it without even testing it because they have the features that we're looking for. That was like 20 years ago, but today features are commoditized.

Amos Bar-Joseph:
Everybody has everything. It's not really interesting, especially more than ever in the age of AI, when it's just an API call to OpenAI or to anthropic or whatever, that it's commoditized and AI has been democratized so everyone can do everything. And instead of talking about features, you need to understand what is the narrative that you're pushing towards that actually promotes a different societal change within the organization. So what is powerful in Swan is what we're saying is all the platforms that all the other vendors are pushing towards complexity, technical complexity that creates a lot of bottlenecks in your organization, in your go to market org and requires a lot of rev ops resources to actually push through. So you can run these experiments. But with Swan, there's a different organization that you can imagine that you move at the speed of thought because you don't have that technical complexity, no features involved in that narrative, zero features. Right. It's just the transformation.

Andrew Monaghan:
Yeah, it's essentially. I talked to people right now about this. I said, look, you know, you're. If you're competing the same way you're competing 10, 15 years ago, you know, you're, you're competing for a different world than you're in right now. And 10, 15 years ago, it was the battle card. It was our feature, our blue widget. And that was a, that was a time when it took six months to deliver a new feature. Right.

Andrew Monaghan:
These days, that's not the world we're in. Right. So if you're competing in that world, you're competing like it's 2010 or 2015. Let's think about how we compete in the modern world. So I think it's just an interesting area listening to how you were describing what you do. Amos. I was kind of picking up on. You're talking in a Very different way than some people might be tempted to talk about when they're looking at changes like this.

Andrew Monaghan:
Look, if I'm a leader at a cybersecurity company, how fast could I get up and running on Swan and start getting what's my time to value? Basically? How can I start getting things that can make a difference?

Amos Bar-Joseph:
That's a great question, Andrew. The question that I love the most, because one of our design principles at Swan, which really revolves around technical complexity as the enemy, right, is that Swan is designed for adaptation, not for perfection. What does that mean? It means that all of the AI solutions out there right now are optimized for performance perfection. Do you have very long onboarding processes you need to work on? All the orchestrations, connect to your knowledge sources, et cetera, Everything. You spend one month, you hope it's working. If it's working great, you have a full year to enjoy it. If not, the entire implementation failed in Swan. How we built it is, it is designed for feedback loops.

Amos Bar-Joseph:
What does it mean? It means that you start with a very simple onboarding. It takes like one hour and everything is set up basically because you chatted with the agent, you explained it and it got the overall sense of how do you want to operate. But then it works in a very kind of like approval based process where it will tell you, look, Andrew, I want to send that email to that person or I just want to, you know, recommend that, that rep with that lead, it will ask for your approval and for your feedback and you can in Slack just reply to Swan's messages, say yes, one. You know what, from now on, when it's a closed loss deal, actually make sure that decision maker is still in the company. Okay, Just so we don't, we don't just reach out to them if they move the company. You just tell that Swan, okay, it adapts to what you said and it's constantly adapting to what you have to say because GTM evolves with you. So onboarding can take as low as 40 minutes, one hour, and the rest of the process is continuing just into your work where you're passive, you're just getting kind of like a knock on the door from Swann saying, hey, this is the work that I did. What are your thoughts about that? And you can just reply in five seconds, tell it how you would think it should work better and you move on with your day.

Amos Bar-Joseph:
Right? So it's a different type of onboarding process in that sense, more similar to actually onboarding an employee. Right. So you give them a task, they start working on it, they come to you with, you know, their work being done and you just give them feedback and that's how they prove.

Andrew Monaghan:
Well, Amos, look, this is, this has got my head spinning for my clients. Frankly, it's got my head spinning for my own business. If someone wants to reach out, what's the best way to get hold of you?

Amos Bar-Joseph:
So I'm on LinkedIn, Amos Bar Joseph or Amos Bar Joseph and you can search me there also. So I have a digital clone basically. So if you want to ask questions, pick my brain. So it's almost as good as asking me. It's in ChatGPT, it's called autonomous. So maybe we can share the link somewhere, Andrew, after the episode. And finally I have a newsletter by the way called the Big Shift so you can search for it online for Swan, the Big Shift where actually document the journey of how to build an autonomous business behind the scenes, the wins and losses. So if you want to take a front row seat to that journey, you can just join the dealer.

Andrew Monaghan:
Well, that's what we'll do. I'll put those both, all three of those resources in the show notes if someone wants to do that. But been fascinating. Thanks for joining us.

Amos Bar-Joseph:
Yeah, thank you so much, Andrew.