Show IH: I noticed ChatGPT never mentioned my product, so I built a tool to find out why
Six months ago, I searched "best tools for quality management" on ChatGPT.
My product wasn't there. My competitor was cited three times.
I didn't have a content problem. I had a visibility problem. AI was pulling its recommendations from Reddit threads and Hacker News discussions I had never touched.
So I started manually tracking which community threads were shaping AI answers in my space.
It was 3 hours of work. Every. Single. Week.
I built AIRankCite to do it in under 2 minutes.
You paste your URL. It analyzes your category, generates the recommendation-style prompts AI engines actually use, then finds the exact Reddit and Hacker News threads that are shaping those answers right now.
The output isn't a report, it's a ranked hitlist. Each thread gets a confidence score, citation evidence, and a tailored seeding kit: what angle to take, what to say, and an opening draft.
No spam. No automation. Just knowing exactly where to show up.
476+ founders have run a scan since launch. The most common reaction: "I had no idea this thread existed."
One user went from zero AI citations to being mentioned in 3 out of 5 recommendation queries for their niche, in under a month.
Another told me the seeding kit saved them hours of research they were doing manually on Perplexity.
First scan is completely free, no credit card, and results in under 2 minutes.
Happy to answer questions below. Also curious: has anyone here been doing this kind of AI citation tracking manually? Would love to know your process.
One angle worth testing is whether the model even has a clear category hook for your product, not just whether it "knows" the brand. In my own tools, vague positioning tends to lose to competitors with boring but explicit comparison pages, use-case pages, and docs. Your tool could be really useful if it shows which source gaps or prompt patterns cause the omission.
You're onto something. We're actually working on that layer - showing not just which threads are cited, but WHY certain products get picked over others in the same thread. Early patterns we're seeing: products with explicit comparison pages, structured FAQ content, and community-generated 'vs' threads get cited way more than products with just a landing page. The source gap is often a content gap.
The product is useful.
What you’ve built is less “AI SEO tooling” and more visibility infrastructure for the AI recommendation layer.
That distinction matters, because AIRankCite sounds like a feature.
It explains what the tool does, but it still reads like internal growth tooling instead of the system teams rely on once AI search becomes a real acquisition channel.
That category will get crowded fast.
The products that hold position usually sound more like infrastructure than tactics.
Exirra.com fits best here.
It feels sharper, more durable, and much easier to grow into as the product expands beyond citation tracking.
Xevoa.com is the other strong fit.
Cleaner, broader, and better suited if this becomes the operating layer for AI visibility rather than just prompt citation discovery.
Fair distinction, and you're right that AIRankCite reads like a feature name. That's exactly the positioning I'm pressure-testing right now.
The infrastructure framing resonates more as the product expands beyond citation tracking into full AI visibility ops. Exirra and Xevoa are both on the list. Leaning toward testing the narrative shift first before committing to a rebrand, since the domain is the last thing to change, not the first.
Curious what made Exirra feel sharper to you over Xevoa. Platform ambition or just the sound of it?
Exirra feels stronger because it carries more weight.
Xevoa is cleaner and broader.
Exirra sounds more like infrastructure with judgment behind it.
For what you’re building, that matters.
If the product stays closer to:
AI visibility tooling
Xevoa works
If it becomes:
the system teams rely on to understand, monitor, and defend visibility across AI surfaces
Exirra carries that weight better
Xevoa feels lighter.
Exirra feels more like something teams trust to make decisions from.
Yes, manually and badly. Every couple weeks I check ChatGPT for "free Statuspage.io alternatives" , "best free status page" and just eyeball which 4-5 names show up. Same names every time. StatusPageBuddy (mine) is never one of them.
The Reddit/HN identification piece is the part I'd actually pay for my Reddit account is shadowbanned so I can't even reverse-engineer which threads are influencing the answers. Quick question: does the "seeding guidance" output point to specific threads to engage in, or is it more about content angles to pitch?
Will run a scan and report back.
It points to specific threads, the actual Reddit and HN URLs that LLMs are pulling from when generating recommendations in your category.
So you can see exactly which conversations are influencing the answers and decide whether to engage, create a counter-thread, or get mentioned in a similar one.
The seeding guidance layer then tells you what to say and where. Not just angles, but word-for-word comment copy you can drop in.
Given your Reddit account is shadowbanned, the thread identification piece alone is valuable. You can use a secondary account or post on HN instead.
Run the scan, would love your feedback.
That's exactly the layer I was hoping was there. The "which Reddit/HN threads LLMs pull from" piece is the leverage point , once you have the URLs, a counter-thread
or strategic comment is a 30-min job, not a 30-day SEO campaign.
Running the scan today, will report back.
One question while I prep: SPB is in a sparse category (free indie status pages competitors are mostly self-hosted upptime forks, not active Reddit/HN discussions). When there's basically nothing to identify, does the seeding guidance still produce output, or does the empty set itself become the signal ("go create the first mention here")? Curious whether your customer profile skews "crowded category, defend share" or "empty category, seed first."
The empty set is the signal, and that's actually the more actionable output for a sparse category.
When the scan surfaces little to nothing, the seeding guidance shifts from "engage here" to "create the canonical thread." You're not competing for position in an existing conversation, you're writing the conversation that will get cited first. That's a different brief but the tool still produces it.
For SPB specifically, a well-constructed HN "Ask HN: best lightweight status page for indie projects" thread that you seed early becomes the reference point LLMs pull from for that query. Empty category, first-mover advantage, lower effort than fighting for share in a crowded one.
To your question on customer profile: both segments are real but the empty category user needs a different frame. Less "here's where your competitors are winning" and more "here's the gap you can own." The scan output reads differently but the value is higher in sparse categories if you move fast.
Curious what the scan returns for SPB. Would love to see whether it surfaces anything or comes back thin.