The argument in one line.
Building a SaaS to $1M ARR requires solving a red-hot, high-budget problem for mid-market companies rather than chasing low-touch niches, combining AI-powered iteration with regulatory or implementation moats that AI alone cannot overcome.
Read if. Skip if.
READ IF YOU ARE…
- A founder or product lead building a B2B SaaS in a call-heavy industry like sales, debt collection, or telehealth who wants to leverage Claude Code to ship faster.
- A developer or technical co-founder at an early-stage startup with product-market fit signals who needs a repeatable build-to-market playbook, not just coding tutorials.
- A SaaS operator exploring AI-native moats and pricing strategies for a product where regulatory or integration barriers create defensibility beyond pure software commoditization.
- An entrepreneur in the idea-mining phase for a SaaS who wants to see a concrete prompt structure and evaluation framework for vetting problem-market fit before building.
SKIP IF…
- You're building in a non-call-based vertical or a horizontal tool — the pricing, moat, and go-to-market strategies here are tightly coupled to sales infrastructure and won't transfer cleanly.
- You're already shipping SaaS revenue and looking for advanced scaling tactics — this is a zero-to-one breakdown, not a unit economics or Series A growth video.
- You want to learn Claude Code's technical capabilities in depth — this is a business case study that uses Claude Code as a tool, not a technical tutorial on prompt engineering or API integration.
The full version, fast.
Building a $1M ARR SaaS with Claude Code is achievable when the product targets a high-LTV, low-churn problem in an underserved market — the AI power dialer Clairvo was selected precisely because call-based industries have infrastructure lock-in and few modern competitors. The build loop involves using Claude Code for idea mining through structured prompts, then iterating rapidly through product development cycles while pricing high enough to reflect real ROI delivered to customers. Four protective moats — regulatory complexity, deep workflow integration, high switching costs, and network-specific algorithms — prevent AI commoditization from erasing the product's value, and the same framework applies to any SaaS category where compliance, integration depth, or industry-specific expertise creates friction that a generic AI tool cannot replicate.
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00:00 – 02:38
01 · What is Clairvo?
Introduces Clairvo — an AI power dialer — and the core value prop: 2x calls per hour, 3x pickup rate, 3x revenue via a canvas comparison diagram (industry vs. Clairvo).
02:39 – 05:56
02 · Mining Claude Code for Ideas
The exact prompt structure: spawn 10 parallel sub-agents, each proposes 10 mechanisms, diverge across algorithmic/behavioral/regulatory/psychological axes with zero self-censoring. Mine for the 2-5 that are not trash.
05:57 – 07:06
03 · Predictive Pacing Deep Dive
How simultaneous dialing with algorithmic offsets (Bayesian optimization on historical call data) generates the pickup-rate improvement that is Clairvo's core value.
07:07 – 08:41
04 · The Product Loop
A cycle diagram: Define Problem -> Claude Enumerates Solutions -> Hand-Select Feasible -> Design Simulations -> Iterate in Sim -> Real-Life Test -> Roll Out. The repeatable R&D system, not a one-time build.
08:42 – 14:03
05 · Pricing the SaaS
Started at $100/seat, raised until resistance, now $250/seat. High-touch enterprise over low-touch consumer. AI commoditizes the low end first. Key slide: the new moat is selection, not construction.
14:04 – 17:17
06 · Finding Payable Problems
Venn diagram: what you can build (literally anything with Claude Code) vs. what people will pay for (red-hot problems with budgets). HVAC case study: +$2M/mo revenue in 2 weeks; Clairvo takes 10-15% slice.
17:18 – 19:26
07 · Human Moats Win
Regulatory processes (A2P registration, HIPAA) are natural moats in an AGI world. If something requires human onboarding or regulatory approval, AI cannot replace that layer yet.
19:27 – 22:22
08 · Model-Agnostic Stack
Maintain parallel claude.md, gemini.md, agents.md specs so the team can hot-swap models as token costs and quality fluctuate. CTA: Clairvo and Maker School.
Lines worth screenshotting.
- Predictive dialing — calling multiple numbers simultaneously with algorithmic offsets — generated 50–80% revenue improvements for the companies tested.
- A single 100-seat deal at $250/month produces $25,000 MRR and $300,000 ARR, so you only need a handful of clients to hit $1M.
- Every additional framework you layer onto Claude Code is inversely correlated with the amount of money you make.
- Claude Code's creator Boris Terni uses essentially nothing in his CLAUDE.md — the vanilla intelligence of the model is the actual moat.
- Low-touch SaaS at $5–$20/month is now a dying category because any business owner can just spend tokens to rebuild the whole thing.
- Regulations you cannot prompt your way around — like FCC phone number registration — are actual durable moats in an AI-commoditized world.
- Pricing correctly is simple: pick a number, raise it until it gets hard, and stop there — statistical pricing models are usually wrong.
- If the problem you solve is too small to justify a client not just building it themselves with AI, your SaaS has no future.
- Taking 10–15% of the economic value you generate for a client is a reasonable and defensible starting point for enterprise SaaS pricing.
- A SaaS with human onboarding and relationship-dependent implementation survives AI commoditization longer than a fully digital self-serve product.
- Making your codebase model-agnostic — with parallel CLAUDE.md, gemini.md, agent specs — lets you hot-swap models when compute constraints or pricing shifts.
- The majority of Claude-generated ideas in a brainstorm are garbage; the process only works if you generate 200–300 and cull ruthlessly.
- Software velocity is no longer the competitive advantage — what you choose to build and how you price it are the only remaining moats.
The build loop is a system, not a vibe.
Steal the framework
Nick did not stumble into $1M ARR — he ran a repeatable loop: mine Claude for 200 ideas, filter to 5, simulate, deploy, take a slice of the value you create.
- Use the idea mining prompt on your next product feature: 10 sub-agents x 10 mechanisms, no self-censoring, then filter.
- Price high-touch from day one — low-touch SaaS is the first category AI eats. If a stranger can self-onboard without you, someone will rebuild it with Claude Code.
- Pick a problem where the moat is regulatory or relational, not technical. A2P registration, HIPAA compliance, enterprise onboarding — these cannot be hot-swapped by a token budget.
- Make your codebase model-agnostic now: duplicate your CLAUDE.md as GEMINI.md and AGENTS.md so you can hot-swap when token costs spike.
- Frame your pricing as they keep the lift, we take a slice — value-based pricing lands when you can show a specific revenue delta.
Terms worth knowing.
- SaaS (Software as a Service)
- A software delivery model where users pay a recurring subscription to access an application hosted in the cloud, rather than buying and installing software once.
- ARR (Annual Recurring Revenue)
- The total predictable subscription revenue a SaaS business expects to collect over a full year, a primary metric for measuring growth and business scale.
- Power dialer
- A sales tool that automatically dials the next number in a list as soon as a call ends, maximizing the number of outbound calls a rep can make per hour.
- Predictive dialer
- An advanced calling system that uses algorithms to dial multiple numbers simultaneously and connects a sales rep only when a live person answers, reducing idle time.
- Inbound lead
- A potential customer who has initiated contact or expressed interest — by filling out a form, calling in, or clicking an ad — rather than being reached out to cold.
- Outbound calling
- A sales strategy where a representative initiates contact with potential customers who have had no prior interaction with the company, typically via phone.
- Moat
- A durable competitive advantage that makes it difficult for competitors to replicate a business's position — such as regulatory requirements, network effects, or proprietary data.
- Idea mining
- A systematic process of searching for business ideas by identifying specific industry pain points, market gaps, or underserved problems before committing to a product.
- AI commoditization
- The trend in which AI capabilities become widely available and cheap, eroding the advantage of products that relied solely on AI novelty rather than unique data, workflow, or regulatory barriers.
Things they pointed at.
11:58toolHermes agents
11:58toolOpenClaw
19:48toolDeepSeek
19:55toolCodex
Lines you could clip.
02:39
“Spawn 10 parallel sub-agents. Each one should propose 10 distinct mechanisms... do not self-censor for any feasibility.”
12:29
“The intelligence comes from the model itself these days. It does not come from the shiny framework that wraps around it.”
13:27
“These people typically have nothing of substance in their Claude.md files. They are literally just using the vanilla intellect of the model.”
14:26
“The new moat is selection, not construction.”
16:55
“They keep the lift. We take a slice.”
Word for word.
analogy
00:00And so we just hit a million dollars in ARR with our SaaS product, which we use Cloud Code to build. And I know a lot of people here are probably interested in using Cloud Code either independently or within an organization to put together some sort of SaaS app and then take it to market. So I figured in this video, I'd run you through basically everything that we did in order to get to where we wanted to and also share all the learnings along the way.
00:17So what is the SaaS? It's called Clarabo. It is essentially an AI enabled power dialer.
00:23And just to unpack those words, what this does is it allows us to make more calls per unit time and then have more of those calls picked up on the back end. And that works really well and is very powerful if you're in an industry that is traditionally pretty call based. So either you have some sort of funnel, where you have inbound leads and you need to call them very quickly and at mass, or, uh, you know, you're doing like traditional cold calling or outbound calling to try and acquire clients whom you don't have preexisting relationships with.
00:47And so anytime you're starting any business, whether it's a SaaS, e com, you know, service company, whatever, you need to have a very clearly defined problem that you were trying to solve. And so I'm gonna run you guys through exactly how we picked this problem later. But essentially, at a high level, we picked this because it has very high lifetime value, meaning that a single client that we get on our service will pay us a lot of money over the course of the next few years.
01:06Uh, it's very low churn because once we install it into a company, it's very unlikely that they're gonna just bow out. Their whole infrastructure depends on us. And then it's also very straightforward and easy to do in a market that didn't have a lot of, uh, other entrants.
01:18Cloud Code helped us come up with every single way, and I'll run you through a quick step by step on how to do it in a second. But just so that we're all clear, essentially, you know, an industry competitor might make a 100 calls an hour. These might be outbound calls to try and close some deals to strangers they've never met before, or it could be inbound calls calling a list of people that opted into some offer.
01:36From those 100 calls, because of dial times, connect times, people aren't present, people aren't picking up the phone from numbers they don't recognize, maybe only 40% of those will actually pick up. So if you think about it right off the bat, the salesperson is making a 100 calls, only 40 people are picking up. There's sort of a 2.5 x drop off right there.
01:50And so if you just do the math, you have a salesperson working eight hours a day, they're capable of getting 40 pickups an hour. It's like how many actual conversations are you having? Let's say they do that every day for a month, maybe they make 10 k a month.
02:00What Clarabo does is allows you to make more calls in the front end. So now we're capable of doing, let's say, 200 calls an hour instead. And then it also increases the fraction of people that pick up because the calls are more recognizable.
02:09We use a couple of cool cloud code based algorithms to, like, uh, dial multiple numbers simultaneously and then also double and triple dial if needed. Uh, and then, basically, at the end result is you you just make more money. So in our case, we have more calls.
02:21We have a higher pickup rate, and so there's significantly more people that are actually on the phone. And, uh, right now, we're capable of generating, you know, somewhere between 50 to 80% improvements to the companies that we work with. We took a pretty sizable business from Texas from somewhere between 3 to $5,000,000 per month, uh, which is almost, uh, you know, double their their revenue.
02:37So And this is the sort of value proposition that NetClarbo has. So how do actually use Claude here? Well, I should note that we didn't actually know how to solve this problem when we started.
02:44Uh, we actually had Claude walk us through every possible way that it knew of to improve pickup rates and increase the total number of calls we could make per unit time. And, uh, most of the ideas were absolute trash. But after mining Claude for 200, 300 ideas, a couple of them were actually pretty good.
03:00And so the process, if you're interested, is we literally said, hey, we're building insert product here. You know, it is in our case an AI power dialer for local service businesses like HVAC, plumbing, roofing, etcetera. Our core metric to optimize is call pickup rate, which is defined as the percentage of dialed numbers that result in a live human answering within say ten seconds.
03:20So here we have the current baseline, we have the industry ceiling, and then we even had our target. So what I told us to do was spawn 10 parallel sub agents. Each one should propose 10 distinct mechanisms we can use to increase pickup rate.
03:31I also want you to diverge each of these wildly. Do algorithmic, behavioral, infrastructural, regulatory, psychological, time based, identity based mechanisms. Don't self censor for any feasibility.
03:40I'm gonna do all this later. And so after it comes up with all of these ideas, and it's gonna come up with a lot of ideas as mentioned, what we're gonna do is we're just gonna take them and then verify, okay, is this, like, a total BS idea or is it like an okay idea? And so here we go.
03:51We now have a variety of results. Uh, a lot of them are hard duplicates as well. But just going top to bottom, the first is a temporal propensity model, which is basically using AI to determine, um, an optimal call window, aka when to call people.
04:04This So is legitimately something that we do at Claro. We have optimal call windows based off of, uh, average pickup times per, you know, time of day, essentially. But at the same time, some of these other ideas are total b s.
04:15So weather times pickup progression, you know. Can we run a regression, which is a statistical analysis on historical pickup rates versus hyper local weather?
04:23Uh, you know, just off the top of my my head, that's probably not gonna be anywhere near as valuable as doing some sort of like call based, uh, on time, let's say. And so you're gonna get tons of ideas like these and, the majority of them are gonna be junk. But you're gonna find a couple that work.
04:35And so in our case, this is literally what we did. We ideated over all of the possible ways to improve something. After you're done with that, we shortlist one of these ideas.
04:42And so in our case, predictive pacing was actually a pretty well known idea. It's not something we invented. Uh, ClockCode definitely didn't invent.
04:49But, know, it's an idea that we wanted to explore and see, okay, what sort of alpha would there be if, you know, rather than just call one person, we have to call multiple people simultaneously. Essentially, uh, because the amount of time it takes to dial somebody is very fixed. Like, you think about it, you enter a phone number in and then you you stay on the line, it goes ding ding ding ding ding ding.
05:06Uh, what that means is if the person doesn't pick up, you've just wasted all that time as a salesperson. So if your your goal is optimally to be more efficient, And so this isn't just limited to two people.
05:31We actually use an algorithmic model that specifically imbues, like, offsets into our multiple call thing that is proven, and we've seen it in our data, to call and get picked up by the optimal amount of people per unit time.
05:45Do some people pick up at the same time, and then that results in kind of an op a weird awkward situation? Yeah. But we also have a built in call routing so that if, you know, we make multiple dials here, one of them doesn't get picked up, it actually goes to an agent that might actually be available.
05:58So it's a queuing system which, you know, Cloud Code obviously helped us build. But it all started like right here. This is the exact same approach that we use in order to figure all that out.
06:05And so once you have the simulation harness, you know, you feed it in a bunch of data on historical call times, which we accumulated through our own businesses and then businesses of other people. Now we have something we can run stats on. And we can figure out, okay, what's the optimal offset for this, you know, batch of 50,000 calls, let's say, in order to determine, you know, what our what our offset needs to be.
06:23Once you're done with that, you feed it in another prompt that says, hey, I want you to now implement this predictive pacing simulation from spec above. Here is some historical data. I want you to optimize for these things using, in this case, Bayesian optimization.
06:35Obviously, this is gonna depend on the specific problem you're trying to solve. But what I'm trying to say is, we just had Claude code, you know, figure out the ways to improve what we wanted to improve and then actually implement that in a simulated environment.
06:46Finally, you build the thing, which in our case was this predictive pacer, and then you roll it out in real businesses. And, you know, I think this is probably the thing that's gonna trip up a lot of people because don't have real preexisting businesses that are currently live right now that they can test things out on. And that's why data is ultimately quite the moat.
07:01If you have the data and then you also have the means to deploy something and do, you know, parallel testing, you can you can usually get through this sort of thing way faster. Okay. And that takes me to this general sort of loop.
07:10In order to do this sort of thing effectively, what you always start with is you start by defining a problem. And of course, you can have Claude code help you do the idea mining and the problem definitions. That's okay.
07:20But in our case, we just knew this was a problem that a lot of people were willing to pay a fair amount of money for. Then you say, hey, Claude. How can we solve this problem?
07:27I want you to enumerate aka list all possible solutions to, you know, the problem of, let's say, call pickup rates. Then what you do after that is you apply your little human brain, your little sponge, and you say, okay, which one of these are total bullshit? And which one of these are actually somewhat feasible?
07:42And so in our case, we had a short list of maybe five or six out of several 100 that were actually feasible. And, you know, over time, we're going through the the the the rest of them as well just to verify that this is something that can actually add some alpha, some delta to, you know, call pickup rates. But the vast majority of the time, it's one of those things that you'll just read and you'll be like, okay.
07:58Yeah. This is obviously the one. Once we're done, we design some simulations with clog code, usually based off some form of historical data.
08:05And then we run a statistical model, in our case, the, uh, predictive pacing algorithm in order to actually have that perform better. Then we iterate in some sort of simulator. AKA, we have Cloud Code just, like, change the the the parameters of our models that it gets better and better and better.
08:17And then finally, we have, like, a real life stress test where we actually roll it out. And, I mean, it can fail at any step along these lines here. We've had a variety of, you know, pretty cracked out approaches that we thought were gonna work really well in the sim because we saw better improvements in our stats.
08:31But then when we rolled them out to real life, we're like, oh my god. Wait a second. There's actually this third variable here that confounds and kinda ruins everything.
08:37So, you know, it's not easy. If it was easy, you'd have everybody doing it. And if everybody was doing it, nobody would be making any money.
08:43But this is how we ideated on the set of core features, uh, of Clarvo that ultimately ended up making us a fair amount of money. But the pricing is $250 month, which is not like a scientifically determined price.
08:53We started by pricing close to, like, a $100 a month, and we figured out that people were willing to pay for it. So then we increased the price, figured out people were still wanna pay for it, increased the price. You know, I think people that are trying to use these big statistical pricing models or have AI, like, determine what the best price is are usually just wrong.
09:09A much easier and simpler way is just like pick a price and then sell it to a bunch of people. And if it's easy and they say yes, then just keep increasing the price until eventually it gets hard. In general, with SaaS companies, there's a big spectrum of possible prices.
09:20If this is our spectrum here, at the very left is basically what is called low touch. Low touch SaaS businesses, generally speaking, are like self serve.
09:29What that means is it's like a self guided onboarding. There's like maybe a video from the founder. You pay like $5.10, $15.20 bucks a month.
09:36And then everything's like kinda done for you. And, you know, these can be really good, but my head canon, my my personal belief is in an era where a cloud code and other agents are capable of whipping up basically any SaaS, you know, like, gotta ask yourself, at a certain point, any business owner will be willing or able to make the trade off of just paying money for tokens to actually just rebuild the whole thing.
09:55So rather than us sort of going really cheap and really small and solving a tiny problem, we decided to go the exact opposite direction. And we ended up solving a pretty big problem, kinda closer to the enterprise, with what's called a high touch SaaS.
10:08So Clarivos sits sort of right around here. And typically, we don't just sell individual licenses. It's not like, you know, a single user can't sign up if they want to.
10:15But in general, we work with companies and then roll this out to a pre created team of people that are doing calling. So for instance, you know, we sign a 100 seat deal at $250 a month.
10:25Well, if you think about it kinda mathematically, that's $25,000 MRR, which is 300 k ARR. So that's more or less what we've done.
10:30We've closed a handful of deals with sort of, like, mid market, to maybe larger businesses that operate in a variety of very call heavy industries. Only takes a couple of those people to say yes, to roll it out to their team, and then make a fair amount of money.
10:42On the pricing point, my big take on a lot of this is nowadays, anybody can build virtually anything. If you look at the total number of commits over time, okay, they are skyrocketing, and that's because AI is doing the vast majority of the intellectual heavy lifting now. So it's no longer can you build insert software product here because we can all build it.
11:01The the the bottleneck, the moat, like the value that you have is what should you build and, you know, essentially, how should you price. So what you quickly realize is that the vast majority of frameworks are total fluff. You know, we tried a lot of agent frameworks for Claro.
11:14We tried, uh, Ermies. We tried OpenClaw. We tried a bunch of these context libraries, basically made, like, vector dBs of your memory.
11:22We probably tried, like, 50 different approaches. And I can definitively say, for the purposes of creating a software product that later generates revenue, basically, every additional framework you use is, like, inversely correlated with the amount of money you make.
11:35Because every time you jump on a different framework, you are not only distracting yourself and pulling away from, like, the thing that you're trying to build. Um, typically, you have, like, regression within whatever the code base is because now the prompt is being understood or mediated a little bit differently than it was before.
11:51And for those of you guys that don't know, regression is just where, you know, you had an approach previously that worked really well. Let's say, some vanilla thing with, a small little CloudMD. But because now you're you're doing it through a different framework, like a lot of the assumptions and memories and and and things that the model used to know about your code base no longer works, uh, which is quite unfortunate.
12:06And so, you know, rather than jump around a lot and try and like, uh, aim for that a 100% quality, uh, or like a 100% score, uh, IQ test of the model, I would rather have the model work 90% as well of like its total potential, let's say.
12:21But I'd have it work consistently and be the same every single time. The real value that I think not a lot of people understand is that, you know, the intelligence comes from the model itself these days.
12:32It does not come from the shiny framework that wraps around it. You slapping on some new framework to, you know, the way that your your team is building on cloud code is kind of like people that put a fuzzy cover on their steering wheel, and then they pretend that that's the reason why their car works so good. Like, obviously, that's not the reason why your car works so good.
12:48Your car works good because it has wheels. It has an engine. It has a chassis, and so on and so forth.
12:52It's the craftsmanship of the person that built all of that. But, you know, you, because you wanna be all special and and new and stuff like that, put put your little fuzzy steering wheel on and they go like, oh, yeah, this is way better. That's not a genuine improvement.
13:05That's just your subjective improvement. And so I think human beings, we wanna take credit for everything, even if it's not necessarily ours. And so we do the virtual equivalent of slapping on a bunch of, like, fancy fuzzy covers, aka all of these Hermes agents and and and Open Claw tools and stuff like that.
13:20Um, when in reality, the thing that's making the car go is is the is the base model. And so that's why if you guys look deep into the people that actually created a lot of these technologies, like Boris Terni, for instance, who's one of the creators of Cloud Code, these people typically have, like, nothing of substance in their Cloud.
13:35Md files. They have nothing in their system prompts. They they're literally just using the vanilla intellect of the model.
13:42And the vanilla intellect of the model is usually, for all intents and purposes, pretty damn good. You'll only get marginal improvements applying one of these frameworks. And what you find is, you know, Cloud Code's getting so good so quickly nowadays that if there is a marginal improvement that gives you like a 5%, uh, plus ROI, the next generation of the tool, maybe like three or four days later, will actually already include that.
14:01Either hard coded into that system prompt or maybe actually just part of like the training of the model. The second thing is to pick problems that actually pay. And so the idea is, k, you can build more or less anything.
14:12And so this left hand side Venn diagram are all of the things that you could build, and every green dot is the thing that you have decided to build. You're not gonna make any money. What you want to do, k, is find that small little slice of the Venn diagram on the right hand side that people will actually pay for.
14:27So these are things like red hot problems. They're industries and issues on a big budget. So it's people with a preexisting pain.
14:33And then what you wanna do is you just wanna focus all your time over here. And so with Clarivo, that's what we did. We saw just how inefficient a lot of salespeople were and how literally just getting on, um, a power dialer, because this isn't a new idea of of power dialer.
14:46But we saw, like, the difference between not having a power dialer and then having a power dialer was, three x effectiveness. Then we're like, okay.
14:52What if we could just make actual preexisting power dollars even better? And we're like, okay. If we can generate even, like, a two x effectiveness, we'll be able to to take a a large portion of the value that we provide for companies.
15:02And so that's that's the moat. That's sort of where you need to sit if you really wanna crush it in SaaS nowadays. And so everything exists on this problem value spectrum.
15:09You know, on the left hand side, you have a bunch of lukewarm problems. These are things that are nice to have, but they're not necessary to have. And this is unfortunately where probably like 90% of people spend their time.
15:19And I built, you know, a bunch of demos showing you how you could put together to do apps and simple browser extensions and simple productivity tools and so on and so forth. But the harsh reality is, you know, if the problem isn't big enough to justify somebody, you know, choosing your SaaS over, like, building it all themselves, because as mentioned, software is now quite easy to build.
15:38Anybody could just convert tokens into product just at some sort of exchange rate. You know, if it's not a big enough problem, people are just gonna do that.
15:46And the longevity of your SaaS is going to be significantly smaller than if you picked like a red hot burning problem. So in our case, we picked something that is currently costing organizations millions of dollars a year. They'll pay anything to fix their to fix their pickup rates or improve it if they know that it's an option.
16:01And, uh, so this is more or less what what we've done. So instead of solving a, you know, I don't know, marketing for dog walkers, where it's like the average dog walker probably makes, like, a thousand bucks a month or something like that, you know, solve a core need for a large, usually mid market and up style company.
16:18Uh, people that actually have budgets and typically also have many seats that would need to subscribe to these budgets in order to solve some problems. So as mentioned, uh, we implemented this on our HVAC clients, And it says, uh, a year here, but it's it's literally a month for the AI. Just didn't believe me when I said it was legitimately a month.
16:33Uh, and we took them basically, uh, we increased their their multi revenue by 66%. And so if you think about it, like, did we do?
16:40The delta there is 2,000,000 a year in revenue. And typically, way that it works is if you solve a problem, k, you are, uh, I don't wanna say entitled to, but you can typically negotiate or ask for somewhere between 10 to 15% of the total amount that you are providing. So And we provide $2,000,000 a month to this company, 24,000,000 a year.
16:59It is not unreasonable for us to ask for or at least be in a position where we can negotiate a tenth of that or $2,400,000 a year. And so this is the sort of problem that ultimately you want to solve.
17:09You know, you wanna find people that have the means to pay for, uh, this red hot burning thing, but you also need the problem itself to be quite valuable. If it's not, probability of you, you know, getting anywhere with that is quite low. Another hack is to pick an industry or a SaaS type that requires some form of human implementation or, like, human onboarding.
17:28What I mean by this is, you know, if everything that you do is entirely digital, then it is pretty reasonable to expect that in the next couple of years, AI will be able to do it better than your team.
17:40And so, you know, you're onboarding your tool into the company is nowhere near as valuable as just like, hey, Cloud. Can you do it all for me? And I think Cloud will be able to do that for most things fairly shortly.
17:49But the one thing that I can't currently do is it can't upend, like, regulation. You know, if you need, in our case, a bunch of numbers applied for, you need a two p registration. And that's just like a fixed thing.
18:01That's like a law. That's like a regulation. You can't just say, Claude, screw screw the ATP registration.
18:06Get me 5,000,000 phone numbers. Because both for moral, ethical, and programmed in reasons, Claude will will say no. But also, uh, there's just no way to get the number unless you actually go through this like pretty bureaucratic process.
18:17And so what I mean by that is like in a future where, uh, there's no moat to to doing, you need to look for natural notes that are created by regulatory environments. In our case, things like numbers, for instance. Another great example of that is like in health care.
18:31Everybody complains about HIPAA all the time, myself included, because, you know, it's it's quite the blocker to US health care implementing any sort or or building any sort of, like, cool transcription service. We'll require you to, like, fastidiously adhere to HIPAA principles, and that can slow you down a lot. You need to anonymize your data and so on and so forth.
18:45But viewed another way, that's actually a major opportunity in, an AGI world because that's the only thing that is currently stopping us from being able to, you know, do things. Legitimately having some sort of, like, certification, let's say, or some sort of board approval of rolling something out.
19:00And so as a as a company, as a SaaS, if you could build some form of human implementation, human onboarding, you know, a human responsible for maintaining the relationship between you and the advisory board that needs to to rubber stamp the thing, then you'll go way further.
19:14And so in our case, you know, we have a bunch of relationships and connections with people that know how to do these things and facilitate them a lot faster, and that that's one of the moats that I think will actually carry us forward in the next couple of years as opposed to, you know, big AI just pulverizing the vast majority of these low touch, low ticket SaaSes.
19:28Finally, one last tip is to make whatever your code base is model agnostic. So I know the whole point of this video is that we built it with Cloud Code. Um, I would say that's, like, 90% true.
19:37In addition to Cloud Code, we obviously tried a variety of other models. So we tried a deep seek to arbitrage token costs on, like, constant long running twenty four seven, uh, like, restructuring and refactoring and stuff like that.
19:48Constant, like, bug fixes and and and so on, and, uh, that worked okay. We tried Codex a number of times. Our team is increasingly using Codex just as we've run into, like, some token issues and the the tokenomics essentially are the main thing that are that are holding us back from going all in on Cloud Code twenty four seven.
20:04But also, I think, uh, over the course of the next few months, you'll probably see fluctuations in the quality of each of these models and the availability of each of these models because, uh, you know, like the major AI companies are starting to get very compute restrained because everybody on planet Earth wants one of these models now.
20:17They're realizing how economically effective they are. And so you need to be able to just, like, hot swap your code base at will from, let's say, like a Cloud Code based project to, a Codex project. And this isn't really that hard at all.
20:27It's just like a a little bit of friction that I think slows people down. But, uh, Clarivo, we just made our our code base totally model agnostic. And what that means is, like, you know, Claude code has, a skills spec.
20:36It expects a claude.md and so on and so forth. Uh, we just have, like, you know, an agent's MD. We have the agent's skill spec.
20:43We have, uh, you know, the same things for Gemini, gemini.md. Just in case at any point in time, we wanna hop over or maybe employ a different model to see if maybe that model can solve a problem that we're struggling with. You know, it's just like that.
20:55And anybody in our team has the ability to to do so. And so the real actionable tip here is just duplicate everything and then probably have Claude go through the specs of each of these models and just like make sure to prepare the workspace so that at any point in time, you have the ability to, you know, instant preload all of your system prompts and and so on.
21:11And MCP specs and then skill specs are actually currently understood differently from, like, Claude versus other, uh, platforms. Like, not all platforms do the YAML front matter tuning, for instance, where they'll only preload, uh, like, the name and the description of the skill. Um, some of them will actually load the entire thing.
21:27These are just slight little model differences that you can optimize around that'll, you know, allow you and other people within your company to operate much faster. Okay. I hope you guys liked the video.
21:35I had a lot of fun putting it together for you. As mentioned, obligatory pitch for the SaaS company. That was sort of the case study for this whole video of Clarivo.
21:42If If you you guys guys wanna wanna improve your pickup rates, definitely check that out because, you know, we're experimenting with with pricing in a variety of different things. You know, I I'll add a link to the top of the description so you guys can give that a quick click and go through if you'd like. More generally, if you guys wanna learn how to monetize AI automation and SaaS apps in this way, definitely check out Maker School.
21:59It's my ninety day accountability program where it will guarantee you that you get your first customer for an AI or automation related service within that time period, or I give you your money back. And if you guys have any ideas for future videos, or if you guys want me to record something on a specific topic that is trending, interesting, or just sort of stream of consciousness, feel free to let me know.
22:16I take most of my video ideas at this point from people in the comments. Okay? Thank you again for watching, and I'll catch all y'all in the next
The Hook
The bait, then the rug-pull.
Nick Saraev opens on a rooftop terrace with the result already in hand: a million dollars in annual recurring revenue, built with Claude Code. No buildup, no mystery — just a credibility anchor dropped in sentence one, followed by a promise to walk through every decision that got him there.
Named ideas worth stealing.
02:39model
The Idea Mining Prompt
Spawn N parallel sub-agents, each proposes N mechanisms across algorithmic/behavioral/regulatory/psychological axes with zero self-censoring. Mine for the 2-5 non-trash ideas.
Steal forAny session where you need to explore a solution space before committing — product features, pricing models, content angles
07:07model
The Product Loop
- Define Problem
- Claude Enumerates Solutions
- Hand-Select Feasible
- Design Simulations
- Iterate in Sim
- Real-Life Test
- Roll Out
Repeatable R&D cycle: define -> mine -> filter -> simulate -> iterate -> deploy. Applicable to any Claude Code SaaS product.
Steal forAny SaaS feature or product ideation session — makes the Claude Code build loop systematic instead of ad-hoc
09:14concept
Low-Touch vs. High-Touch Spectrum
Self-serve $5-20/mo at the left; enterprise multi-seat $250+/seat at the right. AI commoditizes the left side first. Build right.
Steal forPricing positioning for any AI-built SaaS
17:18list
The Moat Checklist
- Regulatory friction (A2P, HIPAA, board approval)
- Human onboarding layer
- Multi-seat enterprise relationships
- Data accumulated from real deployments
Anything entirely digital is replaceable by AI in 2-3 years. Stack regulatory dependencies and human implementation layers to create durable defensibility.
Steal forEvaluating whether a product idea is worth building long-term vs. as a quick flip
14:04model
The Selection Venn
What you can build (anything) intersected with what people pay for (red-hot problems, big budgets, existing pain). The new moat is selection, not construction.
Steal forProduct market selection — pressure-test any new idea before writing a line of code
How they asked for the click.
21:40product
“obligatory pitch for the SaaS company... If you guys wanna improve your pickup rates, definitely check out Clairvo... More generally, check out Maker School.”
Double CTA (product + community), low-pressure, framed as natural conclusion. No hard sell.
Visual structure at a glance.
