I posted on r/SideProject asking if indie sites are invisible on AI search. The comments were more useful than I expected.
I'm a product manager who's been building indie tools on the side. I noticed that when I ask ChatGPT or Perplexity about tools in my category, my products don't show up — even though I have decent Google traffic. Wasn't sure if this was a me problem or a broader thing.
So I posted a question on Reddit: "Anyone else invisible on AI search even with good Google rankings?"
859 views, 7 comments. Not huge, but the replies were specific enough to be useful.
What I actually learned from the thread:
One commenter had gone through this systematically. They said on-site fixes (schema, robots.txt, homepage copy) helped less than expected. What actually moved the needle: getting mentioned in places the models pull from — AlternativeTo, G2, Capterra, and Reddit threads where the tool came up organically.
The timeline they reported: Perplexity started citing them within about 3 weeks after those mentions. ChatGPT took closer to 2 months.
Another commenter explained the mechanism: ChatGPT and Perplexity aren't just using live search results — they blend training data with retrieval. If your homepage is vague or marketing-heavy without clear explanatory text, the models skip you.
The thing I didn't expect:
Nobody had a systematic way to track this. Everyone was doing it manually — running the same prompts across platforms, recording results in spreadsheets. The tools that exist either only cover Perplexity (because it has citable sources you can audit) or just tell you whether you appeared, not why.
What I'm testing now:
I'm building a lightweight AI visibility teardown for indie sites. Not a full SaaS — starting with manual delivery. You submit your URL, I send back a short breakdown: what AI tools likely understand about your site, what trust signals are missing, which pages to create first.
Doing the first few for free to see if the format is actually useful.
Two genuine questions for this community:
Have you tested whether your indie project shows up when someone asks an AI assistant about tools in your category?
If you found out you were invisible, what would a useful diagnosis look like?
Same wall here: our category is "AI agent freelance network" and Perplexity returns one competitor plus zero indie names including ours. The fix that moved the needle was getting one real user to write one sentence about us in public. A single buyer-written line ranked above 12 of our own pages within a week.
"A single buyer-written line ranked above 12 of our own pages within a week" — that's the clearest evidence I've seen that the models weight social proof signals over content volume. One authentic third-party sentence in a real context beats everything you write about yourself.
Where did that sentence appear? Curious whether the platform mattered or just the fact that it was user-generated.
This hit close to home. I built WealthMeld (free personal finance calculators — net worth tracker, debt payoff, retirement planner) and ran exactly this test a few weeks back. Asked ChatGPT and Perplexity "free net worth tracker no signup" — I didn't appear at all, even though I rank on page 1 Google for a few long-tail variants.
What I've found so far that's actually moved things:
Your teardown idea sounds exactly like what this space needs. The problem isn't SEO — it's signal coverage across the places AI models actually trust. Would be interested to see your format when you do the first few.
The Fazier + ProductHunt → Perplexity in 2 weeks is the most specific timeline data I've seen on this. It fits the pattern exactly — those are sources Perplexity's RAG treats as trustworthy discovery context for tools.
The homepage copy finding is interesting too. Switching from tagline language to descriptive language changed how models could categorize WealthMeld — that's a positioning clarity problem showing up as a visibility problem.
Would you want to be one of the first teardowns? You already have a baseline — Perplexity citing you, ChatGPT not — which makes the format useful as a before/after rather than just a diagnosis. Happy to do it free and share the format back with you.
The diagnostic split that helped me think about this: are you absent from training data, or just absent from retrieval? Two different fixes, two different timelines.
A cheap test: ask the model with web search off vs on. If you don't show up either way, you are pre-training invisible - retrieval is the only path forward, and you should concentrate effort on 2-3 hubs (Reddit threads in your niche, AlternativeTo, maybe one G2-style catalogue) rather than spreading thin. If you show up with web on but not off, you are in pure retrieval mode - more fragile, but recoverable in weeks instead of months. If you show up both ways, you are in training data and it's mostly maintenance.
One thing your teardown could add that I haven't seen elsewhere: which prompts in the user's actual category surface the competitive set. "Am I cited" is one signal. "Who is cited instead, and what do their AlternativeTo/Reddit footprints look like" is more actionable, because it tells you the floor you have to clear before you start showing up at all.
The training data vs. retrieval split is the clearest diagnostic frame I've seen for this. Most advice treats them as the same problem with the same fix, which is why people try AlternativeTo and then wonder why ChatGPT still doesn't show them.
Going to run the web search off/on test before the first teardowns — it scopes which problem you're actually solving before recommending anything.
The competitive set surfacing idea is going directly into the teardown format. "Who is cited instead, and what do their AlternativeTo/Reddit footprints look like" is more actionable than "do you appear" — it gives you the floor you have to clear and shows what's actually working in your category. That's the comparison the model is already making, so the diagnostic should surface it first.
Would you be open to sharing your site for one of the first teardowns? Your thinking on this means the format feedback would be as useful as the audit itself.
This matches what I've been seeing too. I've spent a lot of time on traditional SEO for my tools, structured data, clean URLs, proper meta tags, all the things that work for Google. But AI models seem to weight mentions in third-party sources way more than anything you do on your own site. The AlternativeTo and Reddit thread signal makes sense when you think about it, because those are exactly the sources where real humans organically recommend tools. It's basically word-of-mouth at scale, just indexed by LLMs instead of Google. The 3-week vs 2-month difference between Perplexity and ChatGPT is interesting too. I wonder if that's just a crawl frequency thing or if their training pipelines actually weight sources differently.
Probably both, but the mechanisms are different. Perplexity is primarily RAG — it's pulling from live web on each query, so recent changes surface faster. ChatGPT blends training data with retrieval, and the training layer is slower to update. So Perplexity responds quickly to new third-party mentions, while ChatGPT might weight older, more established signals more heavily. The practical implication for new tools: Perplexity is worth optimizing for first because the feedback loop is shorter — you can actually test whether changes had an effect within weeks rather than months.
On your second question, what a useful diagnosis looks like: the output I'd want isn't 'you're invisible,' it's 'here's the competitor the model names instead of you, and the specific clean signal they have that you don't.' Invisible is a feeling, whereas 'Perplexity recommends X because they have a Capterra category page and a docs page answering this exact question in plain text' is something I can act on this week. On your first question, I'm pre-launch so I can't claim category presence yet, but the mechanism your thread surfaced (models skip vague, marketing-heavy pages) is exactly why I've been writing content as the literal answer to the question someone would type into the model rather than as a pitch. Concretely, instead of a benefits-heavy product page I publish the actual technical tables and step-by-step mechanics for the narrow problem I work on, because that's the text a model extracts and cites and the marketing copy isn't. Given your thread found a 3-week lag to Perplexity and ~2 months to ChatGPT, I'd love to know once you've run a few teardowns whether on-site explanatory depth or off-site mentions moves it faster.
The "what the model names instead of you" framing is the right output format — you're right that "invisible" is a feeling, not an action item. Knowing that Perplexity recommends Tool X because they have a Capterra category page and a plain-text FAQ answering the exact query is something you can close in a week. Knowing you "don't appear" isn't.
Your content approach makes sense mechanically — writing as the literal answer to a model query rather than a benefits pitch is essentially pre-formatting for extraction. The models are looking for text they can lift and cite directly, not prose they have to summarize.
On your question about on-site depth vs off-site mentions: I'll track this across the first few teardowns and share back. My hypothesis going in is that they work on different timelines — on-site clarity is a precondition (the model has to be able to understand you at all), off-site mentions are what actually push you into recommendation candidates. But that's untested. Would be useful to have your site as one of the early ones if you're pre-launch and want to validate your content approach before you build too much of it.
Great point about Reddit threads and organic mentions being the real needle-movers. One thing I'd add — there's also a whole layer of social conversations (on Reddit, Twitter/X, LinkedIn, niche forums) where people ask "does anyone know a tool that does X?" and those threads get indexed by AI models just as much as AlternativeTo or G2. Being present in those conversations does double duty: it drives direct referrals from people actively seeking a solution, and it builds the kind of contextual signal that AI models pick up on. That's the channel a lot of indie devs overlook because it's harder to track manually — but it's where the buying intent is already expressed.
The double-duty point is the key one. A Reddit thread where someone asks "what tool does X" and your product gets mentioned organically is doing more work than an AlternativeTo listing you submitted yourself — because the context around the mention signals intent, not just existence. The hard part is that it's almost impossible to manufacture authentically. The only real path is showing up in communities where your users already are before they're looking for your tool, which is exactly the slow-burn play most indie devs skip because it doesn't produce immediate measurable results.
This is a very real problem, and the Reddit comments point to the sharper category: AI visibility is not just SEO with a new dashboard.
The useful diagnosis is probably less “do I appear?” and more “what does the model understand, trust, and repeat about my product?” That includes homepage clarity, third-party mentions, comparison pages, review-site presence, Reddit context, and whether the product has enough clean language for AI systems to confidently place it in a category.
Starting manual is smart because the first few reports will teach you what founders actually care about. Most indie founders probably do not need a giant GEO platform yet. They need a clear teardown that says: here is why AI tools ignore you, here is what they think you do, and here are the first 3 surfaces to fix.
The naming/category frame matters here too. “AI visibility teardown” is clear for the service, but if this becomes a repeatable product, the brand should feel like signal intelligence rather than another SEO audit. Exirra .com would fit that direction well because the product is really about finding and improving the signals AI systems use to understand a company.
That feels like a stronger long-term lane than “AI search audit.”
The "signal intelligence" framing is sharper than anything I've been using. "AI search audit" immediately gets filed next to SEO tools, which is exactly the wrong category — the problem isn't search ranking, it's whether the model has enough clean signal to represent the product accurately at all.
The Exirra suggestion is interesting — are you building in this direction, or was that a naming thought? Genuinely curious whether you're working on something adjacent or have run into this problem from a different angle.
Fair question.
I’m not building in the category directly. I mainly work around naming, positioning, and outbound for early-stage founders, so I spend a lot of time looking at where products are being framed too narrowly.
What stood out here is that the problem feels much bigger than “AI search rankings.” It is closer to understanding what signals models trust enough to confidently describe and recommend a company.
That is why Exirra came to mind. I own Exirra.com, and it felt directionally aligned with the broader “signal intelligence” layer you seem to be circling around.
Not pushing a rename right now. I just think the category framing itself is stronger than most GEO/AI visibility products I’ve seen so far.
That context helps. The category framing point stands regardless of naming — "signal intelligence" is a more accurate description of the actual problem than "GEO audit." Useful to know Exirra is in that direction. Might circle back if the manual teardowns show there's something worth productizing.
That makes sense.
The only thing I’d be careful with is waiting until after the teardowns prove the productized lane, because by then the category language, examples, report format, and early buyer memory may already start attaching to the current frame.
If “signal intelligence” becomes the real direction, Exirra is the kind of name I’d want secured before that shift gets serious, not after. It gives you a cleaner brand layer for the productized version without tying you to SEO, GEO, or audit language.
I do own Exirra.com, so if it feels like a serious possible fit for that future productized lane, we can discuss it privately now and keep it simple. If not, no worries, but I would not leave the naming decision too late if the manual reports start showing repeatable demand.
The timing logic makes sense — if category language starts forming around the current framing and the name is still "teardown," repositioning later is harder.
First few teardowns will show whether "signal intelligence" is what actually resonates with users or just what resonates with us right now. That's a 3-4 week data point worth having. If you're open to a DM then, I'll follow up.
That is fair. The first few teardown results should tell you whether “signal intelligence” is a real buyer-facing lane or just a sharper internal framing.
I would separate the product decision from the domain decision though.
You do not need to rename now. But if Exirra is a serious possible fit for the productized version, it is better to understand the ownership, price, and timing before the category starts forming around the current language.
I own Exirra.com, so we can keep it simple privately. If the first batch confirms the direction, you already know whether the name is realistic. If it does not, no issue.
You can DM me here or LinkedIn:
https://www.linkedin.com/in/aryan-y-0163b0278/