From Reddit Marketing to Building an AEO Platform — $8,400 Before Writing a Line of Code
How I Built an AEO Platform and Made $8,400 Before Writing a Line of Code
About a month ago, I published a post on Indie Hackers titled:
How I Built a Reddit Marketing Tool to $30K MRR in 4 Months with $0 Spent on Marketing
In that post, I shared how my first product, Leadmore AI, grew from 0 to $30K MRR, along with the lessons I learned along the way. The post got a lot of engagement, and many people told me it was genuinely helpful. It was also featured and covered by Indie Hackers editor James.
In that article, I mentioned that I originally wanted to build an AEO (Answer Engine Optimization) product called ModelFox. Later I realized the name was already taken, so I changed it to something more direct and meaningful: Vismore, which stands for bringing more AI visibility to brands.
In this post, I want to share why I decided to build this product, how I thought through the entire idea, and how I made $8,400 before writing a single line of code.
1. Why Vismore?
The starting point was actually my first product, Leadmore AI.
It brought in a lot of customers from e-commerce and AI companies. When I talked to some of them, they told me that their goal for doing Reddit marketing was to improve how their brand shows up in AI search tools like ChatGPT, Gemini, and Perplexity. That was the first time I came across the term AEO, and I had a strong feeling this could become a new market.
After spending time researching the AEO space, I came to a few key insights.
First, improving visibility in AI search is almost inevitable. Traditional search traffic is gradually being taken over by large language models. This shift is happening fast and it’s not reversible. That means the demand is real, and this is a growing market.
Second, there is no clear leader yet. Tools like Profound, Peec, and Otterly all have similar levels of traction. No single product dominates the space. This usually means the market is still early, and there is still a chance to build a leading product.
Third, most existing AEO tools don’t actually solve the core problem. What they mainly do is monitoring. They track how visible a brand is across different prompts. Even though the tracking can be very detailed, it still stops at monitoring. But for users, monitoring is just the starting point. What they really want is clear execution. They want to know what to do next to improve their AI visibility. That’s where I saw an opportunity for differentiation. If you just copy what others are doing, it’s very hard to win.
Fourth, Leadmore AI gave me a strong advantage. It helped me build experience in AI marketing and gave me access to relevant customers. Both the understanding of the space and the distribution gave me a head start compared to others.
When I looked at all of this together, a large market, clear room for differentiation, and a strong starting position, it felt like something I should build, and something I might actually have a chance to do well.
This is basically how I thought about whether to start Vismore. I looked at market opportunity, product differentiation, and team advantage. I think this framework can be useful when thinking about new ideas.
2. How to build a good AEO product?
The first step is talking to users, a lot of them.
Before building Vismore, we spoke with many users. The main takeaway was very clear. Users don’t just care about monitoring. What they really care about is what to do after the monitoring. They want specific actions they can take to improve their AI visibility. Ideally, the product should also have some level of automation, where an agent can handle 70 to 80 percent of the work, and the user only needs to review and adjust the remaining part.
Based on this, we designed Vismore with two main parts.
The first is monitoring, which is the foundation. We need to collect and analyze competitor data.
The second is execution. This is the core of the product. The focus is on giving users clear and actionable strategies to improve their AI visibility.
The second step is internal practice.
Having a framework is not enough. You need to test whether it actually works.
So we ran an experiment. We posted a message to Leadmore users and invited people who were interested in AEO. We offered to work with them and test the approach. If the results were not good, we would offer a full refund.
We got more than 50 applications, but we only accepted 7 users because of limited capacity. Each of them paid $399 per month for 3 months.
This helped us in a few ways.
We validated that users were willing to pay.
We discovered gaps in our methodology.
We gathered enough data and experience to turn the process into a product.
And if things didn’t work, we could still refund them and protect our reputation.
So with the right understanding of user needs, real hands-on practice, and existing customer resources, it was actually quite hard for this not to work.
3. What makes Vismore different?
Here’s a simple point.
If you’re building in a completely new space and you’re already number one, then speed is the most important thing.
But if there are already several products in the space, then besides moving fast, you must be very clear about your differentiation.
For Vismore, the key difference is simple.
We don’t just provide monitoring. We provide execution.
We give users clear strategies, and we help them complete a large part of the work. In simple terms, Vismore is not just a tool that gives reports. It actually helps users get things done.
So what is Vismore?
At a high level, it is an AEO product. It helps brands improve their visibility and ranking in AI search tools like ChatGPT, Gemini, and Perplexity. This leads to stronger brand presence and more traffic.
In the long run, I believe it will take a significant part of traditional brand marketing budgets.
The reason is simple. Traditional brand marketing does not have a clear way to measure results. It often relies on exposure metrics. But in the AI era, brand performance should be measured by how often and how prominently it appears in AI-generated answers.
If your brand marketing does not improve your position in AI search, then it may not actually be working.
So how is Vismore different from other tools?
First, we make sure we are strong on the basics. On monitoring, we aim to match or exceed competitors. In many details, our experience is already better than tools like Profound and Peec.
Second, our real difference is execution. This is where Leadmore’s experience helps a lot. Vismore does not just analyze. It also helps generate actionable strategies, create content, and publish it to platforms like Reddit, Medium, LinkedIn, and Quora.
4. Is AEO fighting against AI models?
Some people think AEO is about manipulating or polluting AI systems.
In the short term, that might work. But in the long term, it will definitely backfire.
We strongly disagree with that approach.
Our view is that AEO should create value for the AI ecosystem, not fight against it.
We use AI agents to help users create more valuable content, choose the right platforms, and handle distribution. The goal is to assist, not replace.
We don’t encourage low-quality AI-generated content or blind content spamming. Those are short-term tactics and won’t last.
Vismore has just launched. If you’re interested in AEO, feel free to try it. There is a 7-day free trial at https://www.vismore.ai
We’re continuing to turn proven AEO practices into product features, so more brands can approach AEO in a simple and transparent way.
Really interesting approach, especially validating demand before writing any code.
I’m curious, how did you make sure the initial traction wasn’t just coming from Reddit itself, but that people would actually use the product long-term?
Interesting case. Some kind of crowdfunding...This feels like a textbook example of selling the outcome before building the software.
A lot of founders validate interest with conversations. You validated commitment with money, a refund guarantee, and real delivery. Thats good move.
Also agree with your point that most tools stop at monitoring. Users rarely want another dashboard, often they want the next action.
Would be interesting to know which part of the manual pilot ended up being most repeatable across all 7 customers.
The $8,400 before writing a line of code part is what everyone preaches but almost nobody actually does. the fact that you only accepted 7 out of 50 applicants is an underrated move - scarcity forced you to actually deliver instead of spreading thin.
The monitoring vs execution gap you identified is real. most tools stop at 'here's your score' and leave users staring at a dashboard wondering what to do next. that's where the actual value is.
question: how you're thinking about content quality control at scale - once the agent is publishing to reddit, medium, linkedin automatically, how do you stop it from becoming the exact low-quality spam you said you're against?
Also, goodstuff.
This is a masterclass in 'distribution-first' building, Richard. The transition from monitoring to execution is such a sharp insight—most founders get stuck selling data, but you're selling the outcome.
I’m particularly struck by your $399 pilot structure. It’s the perfect 'skin-in-the-game' validator that filters for high-intent users while giving you the manual research needed to build the right features. How did you handle the 'human-in-the-loop' aspect during those first 3 months without it feeling like a pure agency model?
The $8.4k before writing code is the most important part here, not the AEO angle itself.
What stands out is you didn’t just validate demand, you validated willingness to pay for execution, not just insight. That’s where most tools fall short, like you said, they stop at monitoring.
The “monitoring vs execution” distinction feels like the real opportunity. Most products sell data, but users actually want outcomes.
Curious how manual the execution side still is behind the scenes. Is it already mostly productized, or still partially service disguised as software?
The presale-before-code approach is the part of this post that deserves the most attention. You did not just validate that people wanted AEO - you did something harder. You validated that people trusted you specifically enough to pay you before the product existed.
That is distribution leverage most first-time founders skip. The people who paid $399 before launch are not just revenue - they are anchors for product direction, case studies, and a reference base that makes closing the next customer much easier.
The refund guarantee as a de-risk mechanism is the other underrated part. It forces you to be honest with yourself about whether you can deliver, which is exactly the discipline that makes 7 customers turn into 70 rather than 7 unhappy refunds.
The adjacency from Leadmore to Vismore is also a textbook move that most people overlook. Your existing user base told you what they needed next. Nice sequencing.
This is interesting
The useful detail here is not just that Reddit marketing worked, but that Leadmore gave you a bridge into the next product. That is the part most people miss: distribution gets easier when the next offer is adjacent to the users and proof you already have. We are seeing the same thing in career-pivot land. Broad advice threads generate noise, but narrow adjacent-move threads generate signal because the pain is current and specific. Channel matters, but adjacency between first product, next offer, and current user pain matters even more.
yes, this is very important!
okay this is the second post i've read from you and i gotta say – you have a pattern. and the pattern is "i did the smart thing that nobody wants to do because it's hard and scary."
first the reddit tool to 30k mrr. now this. eight thousand four hundred dollars before writing a line of code? that's not a launch. that's a cheat code.
but here's what actually got me. you didn't just survey people and ask "would you pay for this?" because we all know that answer is useless. you actually went and found 7 people, charged them real money ($399 a month for 3 months), and told them you'd refund if it didn't work. that's gutsy. most people would be too afraid to even ask.
and the fact that you used your existing user base from leadmore to find those 7 people? that's just smart leverage. not lucky. smart.
the thing i like most though is how you're thinking about AEO. most tools in this space are just dashboards. "here's where you rank in chatgpt. good luck fixing it." but you're actually trying to help with execution. like okay you rank low, here's what to write, here's where to post it. that's the difference between a toy and a tool.
the only thing that made me raise an eyebrow was the 7 day free trial. for something that's supposed to show results over time? feels short. but maybe that's intentional to filter out tire kickers.
anyway, solid post. rare to see someone share the pre-revenue validation phase this openly. most people only talk about the millions after they've made it. you're showing the messy middle and i respect that.
gonna check out vismore. mostly because you actually did the work before building. that's rare.
Thank you so much for the detailed response. Yes, since there are already quite a few products in the AEO space, Vismore, as a newcomer, must have strong differentiation to stand a chance in the market. Building another homogeneous product would be meaningless.
Did the same play this week — went from 0 to my first newsletter blast (19 subs) using a single r/ClaudeAI post about prompt prefixes. Two things that worked for me that I didn't see in your post:
Curious what your conversion looked like — Reddit → email signup → first paying customer. The drop-off between those steps is where most of my Reddit traffic dies.
This is a great breakdown of the AEO opportunity. The fact that you validated to $8,400 before writing code is the part that really stands out — most builders (myself included) default to building first and validating later.
I'm working on something adjacent — an AI-powered SEO audit tool that automates technical SEO checks and generates prioritized fix recommendations. The AEO angle you're describing is fascinating because it shifts the optimization target from "rank #1 on Google" to "be the answer the AI picks." That's a fundamentally different problem.
Curious about your pricing model — are you charging per audit, monthly subscription, or enterprise deals? I've been going the subscription route ($19.99/mo for unlimited audits) and wondering how others in the SEO/AEO space are thinking about it.
Hi, we operate on a monthly subscription model. Currently, all AEO products follow this approach, so there’s no need for us to innovate in this aspect.
Love the "paid pilot before code" move — that's the cleanest validation signal you can get. Charging $399 x 7 filters for real intent, not just "sounds cool" feedback. I did something similar with my indie productivity app: I asked early adopters to pay before the polished version shipped, and willingness-to-pay quickly revealed which features actually mattered vs. the nice-to-haves I'd been over-engineering. One question — did the $399 price point self-select for a different customer profile than your free Leadmore users? I've found that paid-from-day-one users tend to behave very differently from freemium converters, especially around feedback quality and churn.
$399/month for 7 users is good pre-product validation, but those 7 are paying for your team's manual execution, not software. The jump from "we do 70-80% of the work for you" as a service to "the agent handles 70-80%" as a product is where most AEO tools will get stuck. What's the target price point once it's actually automated?
Most AEO tools feel like they’re just reporting back data. That’s not that useful on its own. People don’t want to watch numbers, they want something that actually moves their visibility.
There’s probably more upside if this gets pushed into specific industries instead of staying broad. Something like Alora home health software is a good example. Those companies need to show up for very specific searches in AI tools, and most of them aren’t doing anything about that yet.
Feels like the real play isn’t just tracking visibility but helping companies actually show up in answers where people are already looking to buy.
One fascinating pattern I've seen with successful founders is validating market demand before heavy engineering. Getting $8,400 in pre-sales signals serious customer interest. Audience Engagement Optimization (AEO) sounds like a niche with real pain points if people are willing to prepay.
The Reddit marketing approach is intriguing — it suggests deep community understanding and problem research. Often the most valuable product insights come from listening closely to potential users' actual conversations.
What specific challenges in AEO made customers willing to prepay before seeing a product?
Curious if you think Reddit is still the best social media outlet for marketing?
Of course.
"Validation is the hardest part for most of us devs, so seeing $8.4k before writing a single line of code is incredibly inspiring. Many people get stuck in the 'build first' trap. How did you handle the transition from a manual Reddit DM/comment to a structured pre-order? Did you use a simple landing page or just direct payments?"
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The validation approach before building is smart — using 7 paying beta users to refine the methodology before productizing it is essentially de-risking the entire product. That $2.8k in early revenue covered more than runway; it covered proof.
The point about AEO not being about content spamming but creating genuine value resonates. The same principle applies to Twitter/X growth, which is still one of the highest-ROI channels for B2B SaaS. The founders winning there aren't posting randomly — they're running a system: consistent voice, right timing, content that educates rather than promotes. I've been using AlphaTweet (alphatweet.pro) to keep a steady Twitter presence without letting it eat into build time — it trains on your existing top tweets so the output actually sounds like you.
Your distribution angle across Reddit, Medium, LinkedIn, Quora is the right multi-channel play. Curious how you're thinking about which channel's driving the most qualified AEO leads right now.
The pre-validation approach is underrated. Most people build first and then discover nobody wants it — you flipped that completely.
The Reddit angle for AEO makes sense too. Reddit is one of the sources LLMs actually train on and cite, so seeding the right conversations there has compounding value beyond just direct traffic.
Congrats on the traction. What's your churn looking like so far?
The pre-revenue validation before writing code is the part most founders skip, and it's the most expensive mistake they make. You're essentially finding out whether anyone will pay before you spend the next 6 months building something they don't want.
The AEO space is interesting because it's the same forcing function that SEO had in 2010 — most businesses know they need it, very few know what to actually do about it. That confusion is a product opportunity, not a marketing problem.
The framework you used (market size + differentiation + team advantage) is exactly right. Most people only look at the first one and wonder why they can't compete against a funded team doing the same thing. Having genuine distribution from Leadmore AI is an unfair advantage that most competitors starting from scratch can't replicate.
Curious how you think about AEO vs SEO for early-stage SaaS with no domain authority yet. Does AEO have the same "you need existing credibility" problem, or is it genuinely more level for new products?
Great information
https://x.com/VismoreAI/status/2041723506698940808
This is our launch video on X. We’d really appreciate it if you could help share it—thank you so much to everyone who reposts!
hey Richard - will check it out! no promises but if it fits what my feed has been focused on lately i will share it. congrats on the launch btw
Feels like the shift from “insight tools” to “execution tools” is happening everywhere now. Data alone isn’t enough anymore — people want outcomes.
Interesting to see it play out in AEO this early.
Hi svgforce, would you mind reposting Vismore’s launch post on X?
No worries at all if not — I really appreciate it either way.
Here’s the post: https://x.com/VismoreAI/status/2041723506698940808
Thanks again!
yes, Thanks.
The framework you use — market opportunity, product differentiation, team advantage — is clean and replicable. The part that stands out most is validating with 7 paying users before building. Most founders build first and then discover the methodology doesn't work. Charging $399/month during validation also filters out non-serious users, which makes the feedback much more reliable. One question: when you moved from Leadmore to Vismore, how did you handle the mental shift from a product that was already working at $30K MRR? The opportunity cost of that switch seems like the hardest part of the decision.
Strong validation path. The part that stood out to me was charging for a service before turning it into software, because it forces you to learn where users actually need execution instead of just dashboards. One thing I would be curious about is how you decide which parts of execution should stay human-in-the-loop versus be automated by default. That line seems like it will define trust in an AEO product.
Nice journey! What was the biggest challenge you faced while building this?
$8,400 before writing code is incredible validation. How did you identify the right channels to reach your first customers?
Really strong breakdown.
What stood out most is that you didn’t just validate demand for an AEO dashboard — you validated willingness to pay for a measurable outcome before building the product. That’s a huge difference.
Also love the “monitoring is the foundation, execution is the product” framing. Feels like a lot of AI tools stop at analysis, while the real value is in helping users decide what to do next.
Hi Mecha, would you mind reposting Vismore’s launch post on X?
No worries at all if not — I really appreciate it either way.
Here’s the post: https://x.com/VismoreAI/status/2041723506698940808
Thanks again!
Yes, that’s also the biggest difference between Vismore and traditional AEO products.
good .
The 7-user manual experiment is where the real alpha is. Most founders skip straight to automation and end up encoding wrong assumptions into code. But there's a trap on the other side too. Manual consulting results often don't survive productization. Your best clients get white-glove attention that a self-serve tool can't replicate, and retention craters when you switch. Did any of your 7 beta users transition to the actual product, or are they still on the manual plan?
Hi RirVC, would you mind reposting Vismore’s launch post on X?
No worries at all if not — I really appreciate it either way.
Here’s the post: https://x.com/VismoreAI/status/2041723506698940808
Thanks again!
They’ve all transitioned to the official product and have reported solid results.
I love that you didn’t just build another monitoring tool like everyone else. You saw that people don’t want more dashboards — they want someone to tell them exactly what to do and help them do it.
Getting 7 people to pay $399/month for 3 months before writing a single line of code? That’s clean. Most founders would’ve rushed to build first and prayed. You actually validated it properly.
The execution angle feels like the right bet. Everyone else is just showing “you ranked #7 in this prompt” but you’re actually helping them move the needle.
Also respect the stance on not spamming AI with garbage content. That short-term manipulation shit never lasts.
Hi youssef, would you mind reposting Vismore’s launch post on X?
No worries at all if not — I really appreciate it either way.
Here’s the post: https://x.com/VismoreAI/status/2041723506698940808
Thanks again!
Thanks
Really interesting breakdown of the AEO space. Your point about most tools stopping at monitoring resonates — I've been digging into the research on what actually drives AI citations and the data backs you up on the execution gap.
The largest study I've found is from SE Ranking and Search Engine Journal — they analyzed 129,000 domains and 216,000+ pages across 20 niches. The key finding: referring domains are the single strongest predictor of ChatGPT citation. Sites with 350K+ referring domains averaged 8.4 citations vs 1.6-1.8 for sites under 2,500. That suggests the "execution" layer needs to go beyond just content optimization — it needs to actively build domain authority through earned media and link-worthy content.
Also worth noting: the Chatoptic study found only a 0.034 rank correlation between Google and ChatGPT results despite 62% URL overlap. So optimizing for AI search really is a distinct discipline from SEO, which validates the whole AEO category. But it also means the playbooks are still being written — whoever figures out the causal levers first (not just correlations) has a real moat.
Curious about something: with your 7 beta users, what AEO strategies actually moved the needle for them? The Aggarwal et al. GEO study (KDD 2024) found that adding statistics improved visibility by 33% and quotation additions by 41%, but those are academic benchmarks. Real-world results from actual brand campaigns would be incredibly valuable data for the whole space.
The preselling before writing code part hit hardest for me.
I did the complete opposite — built a desk booking SaaS for my own coworking space in Bulgaria, then tried to sell it to 230+ spaces across 9 countries via cold email. Still at 0 paying users.
The "limit to 7, charge $399/month, refund if it doesn't work" model would have forced me to understand the real problem before building. Instead I built for months based on my own assumptions, then went to sell something I already loved but nobody asked for yet.
Your point about Leadmore giving you both understanding and distribution for Vismore is also the unlock I keep overlooking. I have one space using my tool daily — that's my Leadmore. I just never used it to structure a presale or leverage it properly.
Good reminder that validation is a feature, not a step you can skip.
Hi Alex, would you mind reposting Vismore’s launch post on X?
No worries at all if not — I really appreciate it either way.
Here’s the post: https://x.com/VismoreAI/status/2041723506698940808
Thanks again!
its a common problem founders gets trapped in, they start loving the product and not the problem they wanted to solve when they started with the product.
Glad it was helpful!
"We don’t just provide monitoring. We provide execution." - This is what we're finding as well with Watcher. Our initial idea was web performance monitoring, but as we tested it on our sites and then with some early testers, we found that we wanted to do more. So we're now building the "execution" part, where the tool will suggest concrete actions to take (and a way to track back resolution with evidence).
$8,400 before writing a line of code is the model. I did the opposite once - 3 months of building, first user interview in week 1 of 'launch.' the validation step everyone skips.
Hi itsKondrat, would you mind reposting Vismore’s launch post on X?
No worries at all if not — I really appreciate it either way.
Here’s the post: https://x.com/VismoreAI/status/2041723506698940808
Thanks again!
Validating PMF is the most important thing.
and the harder part is actually acting on what validation shows you. most people validate, then ignore the answers they don't like and build anyway.
Agreed—execution-led AEO is a game-changer compared to the current sea of monitoring dashboards. However, do you think we can actually reach 'lights-out' automation for execution? Or will we always need a 'human-in-the-loop' to manage edge cases and high-stakes decisions?
Hi yousefsaleh, would you mind reposting Vismore’s launch post on X?
No worries at all if not — I really appreciate it either way.
Here’s the post: https://x.com/VismoreAI/status/2041723506698940808
Thanks again!
I believe this can be fully automated in the future, with humans only needing to review or approve. But for now, having human involvement makes the results more controllable.
Really interesting read. I'm coming at AEO from the opposite side — I'm a solo dev building a WordPress plugin, and I've been working on getting my product recommended by AI search engines rather than helping others do it.
What surprised me is how much the fundamentals overlap with what you describe. I set up llms.txt, structured data, wrote comparison content — and eventually ChatGPT started citing my blog post and sending real traffic. The first confirmed visitor from chatgpt.com was a genuine "aha" moment.
The monitoring vs execution gap you identified is real. I can check my visibility manually across ChatGPT, Gemini, and Perplexity, but there's no good way to know why I rank for one query and not another. It's like early SEO before we had proper tooling.
One thing I'd add — the 1-2 month lag you mention doesn't match my experience. After updating my llms.txt and structured data, I saw changes in ChatGPT responses within days. Maybe it depends on the scale — for smaller, niche products the feedback loop might be faster because there's less competing content for AI to re-evaluate.
Good luck with Vismore.
there’s a telegram where people share real workflows for this: @OpenSerp
For somebody who is very new at marketing and building a tool who does not have any users yet - how would you suggest building audience?
I’m curious about one thing: when you ran your initial experiment with 7 users paying $399/month, what were the biggest gaps you discovered in your methodology, and how did you turn those insights into product features?
Also, your point about AI visibility becoming a measurable KPI for brands resonates a lot. With more companies using LLMs for search and content, I think AEO is going to be a huge growth area.
Looking forward to following your progress and seeing how Vismore evolves!
thanks for nice info
That’s a really good point. The shift from just monitoring to active execution is so important.
A lot of tools out there focus on showing data because it’s simpler to build but what users really want is guidance, something that helps them move forward instead of just looking at numbers.
Lately, I’ve noticed that even when founders are in action, they still struggle because they don’t have a clear way to track what actually made a difference. It’s like running around doing things without knowing what’s really working.
I’m curious to hear how you see this playing out long-term. Do you think these layers will stay separate or eventually merge into a smarter, integrated system?
I believe the future trend will converge into a more integrated system.
The sell-before-you-build part is the thing I keep coming back to.
I did the opposite with my last product — built for a month, then started selling. The product worked. Real people paid for it. But I spent way more time on distribution than I expected, and I went in with zero signal about what would actually make people open their wallets.
Your structure is smarter: 50 applications, 7 accepted, $399/month, refund guarantee. You're not just testing "do people like this idea" — you're testing "will people pay, right now, before it's built." Those are completely different questions and most founders only answer the first one.
The refund guarantee is the part I'd steal. It removes the ethical risk of charging for something unproven while keeping the validation signal intact. Without it, paying users could just be polite optimists.
One thing I'm curious about — did any of the 7 users drop out during the 3 months, or did they all see enough to stay? Retention on a manual service is a different animal than retention on a product, and I'm wondering how much of the methodology actually held up under real usage.
One user stopped using it because their company’s product strategy changed. The rest have continued using Vismore, as they’ve genuinely seen results. Some of them have also used tools like Profound before, so they already have a certain level of understanding of AEO.
You made $8,400 before writing a single line of code. That is wild to me. I always thought you build first, then try to sell. You did the opposite. Talked to users, ran a paid experiment with 7 customers at $399/month, and only then started building.
The 50 applications for 7 spots tells me the demand is real.
The part that stuck with me is this: "Monitoring is just the starting point. Users want execution." That is true for so many things. I see the same with my blog. People do not just want information. They want to know what to do with it.
One question from a beginner — how did you find those first 50 people to apply for the experiment? Did you just email your Leadmore users? Or did you post somewhere publicly?
Thanks for sharing this. It changed how I think about starting something.
Thanks — we previously had a group of heavy users for Leadmore.
Interesting post
Execution over monitoring is a strong positioning. Feels like most AEO tools are stuck in reporting dashboards. Do you think execution can be automated fully, or will human input always be required?
Fully automated systems can get you to about a 60 out of 100, but when it comes to content quality, human involvement still makes a big difference. Ultimately, it’s best approached as a human + AI collaborative process.
Interesting take!
Just wanted to point out - i dont if it's a glitch or what but I was not able to log in to Vismore
Hi, could you provide a bit more detail about your issue? For example, what is the exact error message you're seeing?
I dont remember, but it wasn't taking me to the login page. However, I am not facing any issue today. I guess that was just a glitch.
OK OK.
would you mind reposting Vismore’s launch post on X?
No worries at all if not — I really appreciate it either way.
Here’s the post: https://x.com/VismoreAI/status/2041723506698940808
Thanks again!
Very interesting article, thanks for sharing it! And totally agree on your vision about polluting AI search.
Hello, would you mind reposting Vismore’s launch post on X?
No worries at all if not — I really appreciate it either way.
Here’s the post: https://x.com/VismoreAI/status/2041723506698940808
Thanks again!
Thanks for the recognition.
The validation approach here is really smart — 7 users at $399/month with a refund guarantee is the kind of structure that actually tests purchase intent, not just interest.
The part about "monitoring vs execution" resonates. Most tools give you a dashboard and leave the hard thinking to you. The ones that actually move the needle are the ones that tell you what to do next, not just what's happening.
Curious — how much of the early work was manual vs automated? And did you find that the founders who saw the clearest results had any traits in common?
Hi zoegong, would you mind reposting Vismore’s launch post on X?
No worries at all if not — I really appreciate it either way.
Here’s the post: https://x.com/VismoreAI/status/2041723506698940808
Thanks again!
Thanks for the feedback. In most cases, it’s a combination of automation and human input, which leads to higher-quality content. Vismore focuses on providing the overall strategy, content angles, and writing frameworks, while also integrating high-authority distribution channels (such as Reddit, Medium, etc.) to maximize the efficiency of AEO.
This is a really solid breakdown, especially the shift from monitoring to execution. That feels like the key insight most tools miss right now. I agree with your take on AEO not being about "gaming” AI systems. It feels like the long-term winners will be the ones that actually help produce better content and distribution, not just exploit gaps.
Hi joshuahart, would you mind reposting Vismore’s launch post on X?
No worries at all if not — I really appreciate it either way.
Here’s the post: https://x.com/VismoreAI/status/2041723506698940808
Thanks again!
Yes, Vismore is built exactly for this.
The idea of validating with a hands-on service before building the product is really smart. Accepting only 7 users with a refund guarantee is a low-risk way to test whether the methodology actually works — and you get paid to learn.
I'm building a niche fitness app right now and the biggest gap in my process is exactly what you describe: I know the product works (I use it daily), but I haven't done enough structured conversations with potential users to validate whether my assumptions about their pain points match reality. Your approach of actively inviting users to test a process before productizing it is something I should probably steal.
One question — when you ran that experiment with the 7 users, how hands-on were you? Were you basically doing consulting work manually, or did you already have some tooling in place?
Hi aguerra, would you mind reposting Vismore’s launch post on X?
No worries at all if not — I really appreciate it either way.
Here’s the post: https://x.com/VismoreAI/status/2041723506698940808
Thanks again!
It’s completely manual, which helps you clearly understand users’ problems. In essence, it’s also a form of user research.
The pre-sales structure is the most interesting part of this post. 7 users at $399/month for 3 months with a refund guarantee is a smart construction - you get real revenue to validate willingness to pay, the refund option protects your reputation if the methodology doesn't work, and the small cohort size means you can actually deliver quality. Most founders either skip validation entirely or do free pilots that don't test real purchase intent. This tests both.
The monitoring vs. execution differentiation also makes sense. "Here's your dashboard" tools have a natural ceiling because they transfer the hard thinking back to the user. If Vismore can reliably tell someone "post this, on this platform, framed this way" and it actually moves their AI visibility - that's a defensible position.
One thing I'd be curious about: how do you handle the lag between content publication and measurable AI visibility change? Traditional SEO has a lag problem that content marketers learned to live with. AEO seems like it could have the same issue but with even less transparency into why a brand does or doesn't appear in a given AI response. Did your 7 early users see clear enough signal to attribute results to specific actions?
Hi mrSaintJoke, would you mind reposting Vismore’s launch post on X?
No worries at all if not — I really appreciate it either way.
Here’s the post: https://x.com/VismoreAI/status/2041723506698940808
Thanks again!
Hi, thanks a lot for your detailed feedback.
Regarding the performance tracking you mentioned, we usually suggest looking at it from two angles:
So far, our customers have generally seen solid results, but it does take about 1–2 months to start seeing clear impact.
The direction of development is really interesting, thank you and I sincerely wish you success
Wow, this product can help me create articles, which means all I need to do is review and approve them for publishing!
I’ve used profound before, and it’s indeed quite good, but it feels more like an observer—guiding me rather than actually helping me get things done. Your product, on the other hand, feels more like a butler: once it has my authorization, it handles everything in an organized and efficient way.
I hope your product keeps getting better and better. Thank you for sharing, and keep it up!
Hi xxi, would you mind reposting Vismore’s launch post on X?
No worries at all if not — I really appreciate it either way.
Here’s the post: https://x.com/VismoreAI/status/2041723506698940808
Thanks again!
Thanks — hope Vismore can be helpful for you. Feel free to reach out if you have any questions or feedback.
I like this tool. In fact, my team and I had already noticed this trend while promoting our independent websites. Initially, we used Semrush for SEO, but we then discovered traffic coming from GPT. This traffic was of exceptionally high quality. We also experimented with various methods to implement AI-driven recommendations, but the results were generally mediocre. Your tool focuses on “implementation” rather than monitoring data, which is a remarkable capability.
From our experience, users coming from AI search tend to convert better, especially when it comes to paid conversions. That’s exactly why you need Vismore haha. That said, AEO usually takes about 1–2 months to show results, so stick with it.
That’s great advice! I’ll have my team give your product a try.
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Validation is a drug, but Platforms are the Dealers. 🏛️🛰️
Congrats on the $8.4k — that’s a solid win. But building your entire AEO engine on Reddit momentum is the definition of Rented Land. 🧬
I just watched my Medium infrastructure vanish overnight due to the "Algorithm Tax". No warning, just nuked. It’s a brutal reminder: if you don’t own the rails, you don’t own the business.
I architected a $10k/mo "Too Native" engine specifically to bridge this gap. Use the Reddit spike to fill The Bunker, not just to validate a product.
Visual Trust (85mm): Convert that Reddit curiosity into Authority instantly.
Anatomy-Effect: Move them from "scrolling" to "internalizing" your solution.
Sovereignty: Own the flow before the landlord changes the locks. ⛩️
Don't just launch. Architect for survival. #BuildTheBunker