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.
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.
"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.
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?
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?
Very interesting article, thanks for sharing it! And totally agree on your vision about polluting AI search.
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?
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.
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?
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, 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!
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.
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