I built a micro SaaS marketplace for AI agent skills. Think app store but for SKILL.md files that make Claude Code, Cursor, and Codex CLI better at specific tasks. I'm a non-technical solo founder. Built the whole thing with Lovable and Claude.

This post isn't about the product. It's about the distribution strategy that got it to 15K active monthly users with zero ad spend. Most of this is AEO stuff that I think this sub will find useful.

The numbers first

15,000+ active users in the last 30 days. 219% growth over the previous period. 300K+ Google search impressions per month. 4,000+ organic clicks per month. 878+ page-1 Google rankings, up from about 15 at launch. Cited as a source by ChatGPT, Gemini, Claude, Perplexity, Doubao, and Kagi. 350+ AI-referred sessions per month from 9 different AI engines. Total marketing spend: $0.

The AEO strategy that drives most of my growth now

This is the part I want to focus on because I don't see many people talking about it with real numbers.

AI Overviews are eating Google clicks. I have 121 queries where I rank positions 1 through 3 on Google with zero clicks. The AI answers the question before anyone scrolls down. That insight changed my entire strategy. I stopped chasing rankings and started optimizing to become the source AI engines cite.

Here's what I did specifically:

Every article has a Quick Answer block at the top. 40 to 60 words that directly answer the main question. This is what AI Overviews and LLMs extract. If you bury the answer halfway down your page, the AI will pull from someone who doesn't.

All H2 headings are phrased as questions. Not "Claude Code Skill Locations" but "Where Does Claude Code Store Skills?" AI engines prefer extracting from question-format sections. I tested this across 96 articles and the ones with question headings get cited significantly more.

Every page has FAQ schema. Google's AI picks up our Q&As directly. This is one of the highest leverage things I did and it took maybe 2 hours total with Claude generating the schema.

I built an entity anchor page (/about) with Organization, Person, and AboutPage schema. This tells AI engines who we are and establishes the entity relationship. Without this, you're just another anonymous source.

I created an llms.txt file that explicitly tells LLM crawlers what the site is and where to find key content. Also made sure robots.txt allows all AI crawlers. Most people block them. I want them to read everything.

The result: when someone asks ChatGPT "where can I find SKILL.md skills" or asks Perplexity "what is the best skill marketplace for AI agents," they get pointed to us. That didn't happen by accident. Claude helped me engineer every piece of it.

The content engine behind it

I don't ask Claude to "write me a blog post about X." That produces generic stuff that Google doesn't rank and AI engines don't cite.

Instead I feed Claude my Google Search Console exports (queries, impressions, click-through rates, average positions) and ask it to find keyword gaps. Claude identifies queries where I have high impressions but zero clicks, finds topics where competitors have content but I don't, and spots cannibalization where multiple pages compete for the same query.

Then we write articles together targeting those specific gaps. Every article has the Quick Answer block at the top, question-based H2s, comparison tables where relevant, and internal links to related articles.

96 articles later, we went from 5 clicks per week to 1,000+ clicks per week. 878+ page-1 rankings. All organic.

The technical SEO layer most people skip

I built the app with Lovable (React SPA). Out of the box it was an SEO disaster. Google's crawler saw an empty div and a JavaScript bundle. The JS was 460KB. The logo was a 179KB PNG rendered at 112 pixels. LCP was 4+ seconds on mobile.

Claude diagnosed all of this. It wrote the SSR layer, found the bundle bloat, identified the oversized images, and rewrote the performance bottlenecks. Desktop LCP went from 2.5 seconds to 0.9 seconds. PageSpeed performance score went from 70 to 97. Logo went from 179KB to 7KB.

If you're building with Lovable, Bolt, or any React framework and expecting organic traffic, you need to fix this layer. Google will not reliably index a client-side rendered SPA.

The structured data architecture

Claude built the entire structured data layer:

Homepage: Organization, WebSite with SearchAction, FAQPage with 15 Q&As. Individual skill pages: SoftwareApplication with pricing, BreadcrumbList, conditional FAQPage. Article pages: Article, FAQPage, HowTo, BreadcrumbList, Organization. About page: Organization, AboutPage, Person schema for entity anchoring.

PageSpeed Insights shows "Structured data is valid" on every page with a 100 SEO score. I didn't know any of this before Claude explained it. Now every page is machine-readable for both Google and AI engines.

The weekly growth loop

Every Monday I do the same thing:

1. Export Google Search Console data (queries, pages, clicks, impressions, positions)
2. Upload the CSVs to Claude and ask: find keyword gaps, cannibalization, and CTR problems
3. Claude identifies 3 to 5 specific opportunities with exact numbers
4. I write 2 to 3 articles targeting those gaps, rewrite titles on underperforming pages, and add internal links from high-authority pages to weak ones
5. Ping IndexNow to get new pages crawled within 24 hours
6. Manually request indexing on GSC for the new articles

Takes about 2 to 3 hours per week. Highest ROI activity in the business.

What I'd tell someone starting AEO today

Set up Google Search Console before you launch. Even with zero traffic it starts collecting data on what queries your site appears for. That data becomes your content strategy within 2 to 3 weeks.

Write articles targeting specific questions, not broad topics. "How to install skills in Claude Code" beats "The Ultimate Guide to AI Agent Skills" every time.

Set up structured data from day one. Organization schema on your homepage, Product schema on your product pages, FAQ schema on your content pages. This is what AI engines read to decide whether to cite you.

Create an llms.txt file. It's like robots.txt but for AI crawlers. Takes 10 minutes and it tells every LLM exactly what your site offers.

Don't fight AI Overviews. Become the source they pull from.

Happy to answer questions about any of this. The site is agensi.io if you want to see how this looks in practice. If you want to support a bootstrapped one-person startup, making a free account genuinely helps more than you'd think 😄

I am the founder of IndexerHub which helps sites get indexed on Google, Bing and AI search engines. This post is not a pitch for it though.

I want to share what actually happened when I tried to grow a brand new domain from scratch because I made enough mistakes along the way that the lessons feel worth writing down.

The biggest mindset shift for me was realising that writing for Google and writing for AI search are genuinely two different things. I had been treating them the same and wondering why my content was not showing up in ChatGPT or Perplexity answers even when it was ranking okay on Google.

Google cares about authority, backlinks, technical signals, keyword relevance. AI tools care about one thing. Does this content directly answer the question someone just asked. That is it. They are scanning for content that leads with the answer, uses plain language, and does not make the reader hunt through paragraphs to find what they came for. Once I understood that I rewrote everything. Every article became one question with the answer in the first paragraph and everything else supporting it. Within a few weeks content started showing up in AI-generated responses and that traffic converted better than anything else I was getting.

The other thing that tripped me up early was indexing. I kept publishing and nothing was moving and I eventually realised half my content was not even in Google's index yet. For a new domain Google crawls when it feels like it and that can mean weeks of your content just sitting there invisible. Once I started actively submitting every page to Google's Indexing API and Bing's IndexNow the moment I published, things started moving much faster.

From mid-March to the first week of May traffic grew from basically zero to 560 visitors a week and revenue crossed $1,195. The growth compounded once the content format was right and the indexing was fast. Those two things together made the difference.

Tools I used: 

EarlySEO for content, it handles keyword research and structures everything for AI citations. 

Faurya for analytics, completely free with no card needed, connects to Stripe and shows you revenue per visitor not just sessions.

Happy to answer questions on any of this.

One month ago we launched EarlySEO, an AI blogging and AEO tool built for founders who want their content to rank on Google and get cited by AI tools like ChatGPT and Perplexity.

The honest thing about building a content tool is that you have to use it yourself or you have no business selling it to anyone else. So we dogfooded it completely. Every blog post on the EarlySEO site was written and published using EarlySEO itself. Every piece of content went through the same workflow we built for our users.

Here is what one month of that looked like.

1,807 visitors. $724.76 in revenue. Revenue up 817% from the period before launch. Session time up 29.3% to 1 minute 15 seconds. Bounce rate down 9.3%.

The content workflow was simple. EarlySEO pulls from DataForSEO, Keywords Everywhere, GSC, and AI models to identify the exact questions our target users are asking and then helps write content structured to answer those questions directly. One article, one question, answer in the first paragraph, plain language throughout. That format is what gets content cited in AI-generated responses and it is also just better content for human readers. The session time going up and bounce rate going down tells you people are actually reading not just landing and leaving.

IndexerHub handled the indexing side. Every article we published was automatically submitted to Google's Indexing API and Bing's IndexNow the moment it went live. For a brand new domain with no authority history getting indexed fast is not optional. Without IndexerHub we would have been waiting weeks for Google to discover new pages. With it content was indexed the same day and started contributing to search visibility immediately.

The revenue spikes you can see from Apr 26 onwards are directly tied to content batches that were indexed fast and structured right. That is not coincidence. That is the workflow producing results the way it is supposed to.

We tracked all of this through Faurya which is completely free for startups, no card needed. It connects directly to Stripe and shows revenue per visitor and page-level attribution. Without that visibility we would have been guessing which content was working. With it we could see exactly what was driving the 817% revenue growth and double down on it.

Month one of dogfooding our own product. The tool works. The numbers say so.

I want to break this down properly because the number sounds dramatic but the reason behind it is actually straightforward and replicable.

April 5 to May 4. 1,714 visitors. $566.76 in revenue. Revenue up 617% from the previous period. Session time up 23.7% to 1 minute 13 seconds. The green revenue bars on the graph are not random. They are tied directly to specific content that was structured the right way and indexed at the right time.

The core of what changed was how I was writing for AI search specifically.

Most people still think of SEO as a Google-only game. Write something, get it ranked, get traffic. That model is increasingly incomplete because a growing percentage of discovery happens through AI tools now. Someone opens ChatGPT, asks a question about their problem, and the answer either mentions your product or it does not. Getting into that answer is not about domain authority or backlink counts. It is about content structure.

The format that gets cited in AI-generated responses is specific. One clear question, one direct answer in the opening lines, plain language throughout, no filler. AI tools scan for content that serves the reader immediately and pulls it into the response. Content that buries the answer, pads the word count, or writes for keyword density gets skipped entirely. I rebuilt every piece of content I produce through this SEO tool around this format. The shift was not just about AI search either. The same format keeps human readers on the page longer because they get value immediately. Session time going up 23.7% is a direct reflection of that.

IndexerHub made sure new content entered Google's index the same day it was published. For AI citations specifically this matters because AI tools pull from indexed content. Fast indexing means new articles become citable sources quickly rather than sitting in a crawl queue for weeks. The revenue spikes you can see in the graph align closely with content publish dates because of how quickly that content became discoverable.

Faurya is what made all of this visible and it is completely free for startups, no card needed. It connects directly to Stripe and shows revenue per visitor, conversion rate, and which pages are driving paid users. Without that connection I would be looking at traffic numbers and guessing whether the strategy was working. With it I can see the 617% revenue growth broken down by page and understand exactly what caused it.

AI search is a real acquisition channel right now. The barrier to getting into it is just writing content that is structured to be cited.

There is a real difference between ranking on Google and getting cited in AI search responses and I think it is worth talking about because the two require different content strategies.

Google rankings are about authority, keyword relevance, technical signals, and backlink profiles. AI citations are about content structure. ChatGPT and Perplexity are scanning the web for the clearest, most direct answer to a specific question. They do not care about domain authority the way Google does. They care about whether your content actually answers the question well in a format that is easy to pull from.

The format that gets cited is simple. One question per article. Answer it in the first paragraph. Write the rest in plain language that builds on that answer without padding. No keyword stuffing, no long introductions, no "in this article we will cover." Just the answer, explained clearly.

I rebuilt my content pipeline through EarlySEO around this principle and the results were visible within weeks. Articles started appearing in ChatGPT and Perplexity responses for relevant queries. The traffic that came through those citations converted at a meaningfully higher rate than standard organic because those visitors had already been through a conversation with an AI, already understood the problem space, and were clicking through with clear intent.

The supporting piece was IndexerHub which automated indexing submissions to Google's API and Bing's IndexNow so new content was indexed within hours. AI tools pull from indexed content. Fast indexing means new articles enter the pool of citable content quickly instead of waiting weeks.

Faurya gave me revenue visibility at the page level by connecting to Stripe. That data showed clearly which AI-cited articles were driving paid conversions and which were pulling in curious visitors who never bought. The difference in conversion rate between AI citation traffic and broad organic traffic was significant enough to restructure the entire content calendar around it.

AI search is a different game from Google SEO. The good news is the format that wins it is also just better content.