After 3 months of building, ChatGPT now recommends my platform and every project inside it
Three months ago I started building a bridge — a noise-free channel between indie projects and the AI engines that millions of people ask for recommendations every day.
The idea was simple but nobody was doing it: structure every project listing so ChatGPT, Perplexity, and Gemini can actually read, understand, and cite it when someone asks "what's a good tool for X?"
Week after week, we shipped the pieces: schema.org markup on every profile, FAQPage structured data, llms.txt, an Intent Map where founders write the exact questions people ask AI about their niche. Each feature was one more layer that made the platform machine-readable a clean, structured feast for any LLM that crawls in.
Then it started happening.
First, Perplexity picked us up. Projects listed on the platform started appearing in AI-generated answers.
And then — the big one — ChatGPT.
We checked the admin panel and saw it: new signups tagged "via ChatGPT" as their discovery source. People were finding us because an AI recommended us to them. Not just the platform the projects inside it.
CitableHub is now in ChatGPT's memory. And every project that joins gets the same structured foundation to be cited next.
Here's what that means for you: when someone asks an AI assistant about your niche, your project can be the one it quotes if it's structured to be found. That's what we built. No noise, no tricks, just clean structured data that AI engines trust.
Where we are today:
174 users and growing every day
657 projects listed
Signups coming from Google Search, ChatGPT, Perplexity, YouTube, GitHub, Reddit
Revenue from an optional boost feature
A citability score (/100) that shows exactly how ready your project is to be cited
This took 3 months of relentless building. There were days I wasn't sure it would work. But it did and the proof is in the admin panel, not in promises.
Thank you, Indie Hackers, for being the community where projects like this get a chance to be seen. You gave us the stage. We built the bridge. And now AI engines are walking across it, carrying our projects to people who need them.
With passion and effort, it can be done. 🚀
Free to list your project → CitableHub
Interesting result, though I'd check how stable it is before treating it as a channel. LLM recommendations move around a lot between sessions, accounts and regions, so a logged-out run from a different country often gives a completely different answer to the same prompt. If you've only tested from your own account, that's the first thing I'd re-run.
The insight buried here is about measurement boundaries - you're not building a search tool, you're building a translation layer between human-written project descriptions and AI-readable structure. The measurement shift you made was from "does a human find this?" to "does an AI system understand this enough to cite it?"
That's a completely different measurement dimension. A project could rank top on Google (measured by human traffic) and still be invisible to ChatGPT (measured by structural clarity). You found the boundary and built specifically for the side of it that was empty.
The citability score is the brilliant part because it measures what matters - not "how much traffic does this page get" but "how much of this project's story is actually machine-readable right now?" The score makes the invisible visible, which is how founders figure out which direction to move the needle.
Question: as more projects join and start optimizing for that score, will ChatGPT's citation behavior change? At some point you might move from "we solved the scarcity of structured project data" to "we created visibility for everyone equally" - and that distribution shift determines which types of projects actually win in the AI recommendation game.
You nailed the exact thing I couldn't articulate for months — it's a translation
layer, not a search tool. The measurement shift from "can a human find this?" to
"can an AI understand it enough to cite it?" is the whole thesis.
On your question about whether ChatGPT's citation behavior changes as more projects
optimize for the score: I think it does, but not toward "equal visibility for
everyone." Structure gets you eligible to be cited — it's table stakes. Once
everyone is structured, the tiebreaker shifts to signals a directory can't fake:
freshness, real proof/evidence, and specificity of the questions a project actually
answers. So I don't think we're creating equal distribution — we're raising the
floor and moving the competition to substance instead of SEO tricks. The projects
that win won't be the most optimized, they'll be the most genuinely useful and the
most clearly described. That's a healthier game than the keyword-stuffing one it
replaces.
Achieving organic recommendations from ChatGPT within three months is a massive distribution win that underscores the growing power of AI Engine Optimization (AEO) over traditional SEO. By structuring content so effectively that AI models index and cite every project natively, this approach demonstrates how early adopters can leverage AI platforms as powerful, low-cost discovery engines for modern products.
"AI Engine Optimization over traditional SEO" that's the cleanest way I've heard
it put. The wild part is how fast the two diverge: I have 700+ pages discovered by
Google, with the majority sitting in "discovered, not indexed" limbo for months,
while AI assistants already read and reference them. Google is conservative with
crawl budget until you earn authority; LLMs just read structured content and use it
immediately. Early movers get a real, low-cost head start here precisely because
most people are still fighting for page-one rankings that AI is quietly routing
around
The ChatGPT-driven signups are the most interesting signal here.
Curious whether users coming from AI recommendations behave differently from Google/Reddit traffic, especially around activation or paid boosts.
Great question. Early signal, but here's what I'm seeing:
AI-referred users tend to arrive with higher intent clarity. They already described their problem to ChatGPT/Perplexity, got a recommendation, and clicked through so they're not browsing, they're evaluating. That means:
Faster activation: they usually complete their profile the same day they sign up, because they already know what they need the platform for.
Lower bounce: they don't land and leave like ad traffic often does. They read, they explore other listed projects, some even submit their own.
Zero acquisition cost: no CPC, no retargeting pixel just structured data doing its job at retrieval time.
On the "paid boosts" side I offer an optional visibility boost inside the directory (not ads, just priority placement + AI-optimized copy). Interestingly, AI-referred users convert to that faster than organic/Google users. My hypothesis: they already trust the AI recommendation, so they trust the platform's tools more quickly.
Sample size is still small I'm tracking this cohort separately to see if retention holds. But the quality signal is unmistakable so far.
If you're building something and want to test this yourself, the listing is free would be curious to see if your numbers match mine.
That’s interesting. Happy to continue the conversation privately — what’s the best email to reach you on?