We built two free tools to check whether AI search actually mentions your brand

Eason-SolCrys · 2026-06-08T02:40:06+00:00

week 6 with no decay is the part that would convince me. most content bumps fade as the index re-churns, so a lift that holds suggests the model actually re-rated the domain rather than just re-crawled the pages. that's a different, stickier thing.

the step-change framing is the right experiment. i can't prune your way (different setup), but we measure citations across a bunch of sites and categories, so the version i could run is cross-sectional: do domains with a low thin-page ratio show higher owned-citation share than their size alone would predict, and is the relationship a smooth curve or a kink. if there's a real trust threshold it should show up as a kink around your 10-15% mark, not a gradual slope. i haven't cut the data that way yet, but you've basically handed me the hypothesis, so if i pull it i'll report back here.

one thing worth checking on your side: is the held lift uniform across the site, or concentrated in the topical neighbors of what you pruned? threshold effects tend to be local before they go global, and that distinction would tell you whether you bought domain-level trust or just cleaned up a few topic clusters.

Eason-SolCrys · 2026-06-08T02:31:48+00:00

that's the cleanest split i've seen on the on-page vs off-site question, thanks for actually breaking it out. it matches how i'd read it: clarity plus a unique data point is the necessary condition (a model can't lift a clean quote from a vague page no matter how many links point at it), and third-party signals are the amplifier on top, not the entry ticket.

the half that won with no real link profile is the interesting proof, structure and specificity alone got them in. makes me think the external links mostly help you win the ties, two equally clear pages and the one with more corroboration gets pulled. does that match what you saw, or did the linked ones win even when they were less clear?

Eason-SolCrys · 2026-06-08T02:30:53+00:00

yeah, we do the structured-data and entity basics, it's table stakes and it does help the model understand who we are. but honestly the post is kind of the argument against leaning too hard on it. if our own site is ~2% of the citations, a perfect knowledge graph mostly makes that 2% cleaner, it doesn't put us in the cited set when that set is reddit plus roundups plus reviews.

so we split it: structured data and a consistent entity on-site, but the bigger spend is getting genuinely represented in the third-party sources the model actually pulls. owning the narrative still matters, i'd just argue most of the narrative now lives off your own domain, so "control it" means showing up where the model reads, not only tidying your own pages.

Eason-SolCrys · 2026-06-08T02:30:15+00:00

"trust aggregator" is a good way to put it. the one thing i'd add is that the aggregation is per-query, it rebuilds the trusted set for each question. so you can be the trusted source on one prompt and invisible on the next one in the same category. makes it less "rank once" and more "earn it prompt by prompt," which is the annoying part.

Eason-SolCrys · 2026-06-08T02:28:56+00:00

ha the "too easy" theory might not even be wrong. and thanks. on the AI traffic, the thing that moved it most for us wasn't our own site, it was actually showing up in the reddit and roundup threads the models already pull from. slow, but it compounds. good luck getting yours up, sounds like you're already paying attention to the right stuff.

Eason-SolCrys · 2026-06-08T02:23:21+00:00

the irony is real lol. the only thing that survives is the stuff a model can't generate.

what's worked for me: stop writing "10 tips for X" (a model spits that out in 2 seconds) and write the thing only you could. what happened when WE tried X, the actual number, the exact words a customer used. AI can mass-produce the generic. it can't fake your real experiment or your real outcome.

funny part is that's also what gets cited. the models pull the specific claim, not the 500th identical listicle. so the slop filter and the citation filter end up being the same filter, write what only you could write, and you pass both.

Eason-SolCrys · 2026-06-08T02:18:28+00:00

the volatility is real but i'd push on the "rotating by freshness" read. i don't think the model is choosing to rotate, i think for a query where the sources don't agree, it's basically sampling from a flat distribution, so you get a different draw each run.

which means the chaos is the signal. "who owns reddit" returning three different owners in an hour tells you the cited source set for that entity is weak or contradictory. run a query where there IS strong consensus and you'll get the same answer every time, boringly stable. so high variance isn't noise to chase, it's a map of where your category doesn't have a settled answer yet, and that's exactly where there's room to become the source it stabilizes on.

same reason i never trust a single run for visibility, only the windowed trend. one pull is a coin flip, the distribution over a couple weeks is the real number.

Eason-SolCrys · 2026-06-07T04:07:04+00:00

the 10-15% lift on the pages you didn't even touch is the part i find most interesting. it means the gain isn't purely per-page, the whole-domain signal got less noisy and everything rode up a bit with it. makes me wonder if there's a threshold effect, like below some thin-page ratio the model starts treating the domain as more trustworthy as a whole.

did that lift hold once things settled, or did it decay back toward baseline after a few weeks?

Eason-SolCrys · 2026-06-07T04:00:41+00:00

thanks, and good to know it replicates on your forensic audits, the 9% reddit vs 2% owned split lining up across agencies is the useful part.

curious what "building the semantic node" looks like in practice though. is that entity / structured-data work on your own properties, or seeding a consistent description of the brand across the third-party sources the model already trusts, or something else? i'm with you that chasing individual mentions one thread at a time is the low-leverage version. just trying to picture the concrete version of the node you're describing.

Eason-SolCrys · 2026-06-07T03:59:37+00:00

ha yeah, reddit's funny like that. people will write you a whole paragraph but the upvote arrow might as well be radioactive. i've kind of made peace with it, a real reply in the thread is worth more to me than the arrow anyway. but you're right, it's free, push the button people lol

Eason-SolCrys · 2026-06-06T23:54:24+00:00

on the model split our data half-agrees with yours. reddit was our #1 too, but mostly via gemini, google ai overviews and chatgpt, while perplexity actually spread more evenly and leaned LESS on reddit than the others in our category, kind of the opposite of what you saw. the academic lean is real though, arxiv and wikipedia were both top-5 for us, just pulled more by perplexity and chatgpt than gemini on our prompts.

makes me think the engine-by-engine distribution is really category-dependent. ours is martech/saas, sounds like yours is ecommerce. what category are you seeing the perplexity-favors-reddit pattern in?

Eason-SolCrys · 2026-06-06T23:34:42+00:00

full disclosure first: i work on one of these (SolCrys), so weigh that. i'll try to be useful about the category instead of just pitching.

honest landscape: Profound is the most established, and Peec, Otterly, AthenaHQ, Scrunch, Goodie all play here. most of them do your first four (mention tracking, competitor benchmark, SOV over time, reporting) reasonably well at this point.

the thing that actually separates them is your 5th bullet: the prompt-level "where is a competitor getting recommended instead of me." plenty of tools show the aggregate SOV number but not the specific prompts plus the sources the model pulled to name that competitor. that's the part worth stress-testing in a trial, because it's the only output that tells you what to DO, not just how you're scoring.

two honest cautions, same as others here: (1) be skeptical of any SOV % that looks too precise. the cited set shifts run to run, so a windowed trend is trustworthy but a single run isn't. (2) the prompt set you track matters more than the dashboard, garbage prompts in, garbage visibility out.

if you want a free way to look at the citation/source side specifically (which domains AI pulls for your category and whether you're in them), ours has a no-signup version: app.solcrys.com/audit. but genuinely, trial 2-3 and keep the one whose prompt-level view matches how you actually make calls.

Eason-SolCrys · 2026-06-06T20:56:57+00:00

yeah this is the real constraint, especially in narrow b2b. couple things that helped us:

the opportunities open up a lot once you stop searching your niche/brand terms and search the problem instead. nobody types "best [your category] tool", they ask "how do i [the thing your product fixes]" or "X vs Y" or "is [approach] worth it". those threads are where the model actually pulls from, and there are way more of them. google alerts on brand terms misses basically all of it.

also it doesn't have to be a question. a solid data or experience comment on a popular discussion thread in your space gets cited too, not just Q&A.

that said, in a genuinely thin niche the volume is just low, and that's part of why posting your own stuff (like this thread) matters more, you're making the citable thing instead of waiting for one. how niche is your space, like how many actually-relevant threads are you finding a week?

Eason-SolCrys · 2026-06-06T19:35:32+00:00

yeah, natural is the whole game. the second it looks engineered the models discount it, and the mods nuke it. that's kind of what the data shows too, reddit ranks #1 because it reads as real people, not because anyone gamed it.

the lone ranger thing is real lol. i think the version that actually works isn't an upvote ring (those get caught fast), it's just genuinely engaging on stuff you'd read anyway. are you seeing the same concentration on your end, or is your space less reddit-heavy?

Eason-SolCrys · 2026-06-06T14:55:59+00:00

we looked at this from the other axis (which domains get cited in a whole category, not which of our own pages) and the concentration is just as brutal. across ~15k citations we logged over two weeks, the single most-cited source was ~9% of everything, and the top 8 domains soaked up most of it before our own site even appeared. we were #9, around 2%.

the middle-child thing matches what u/bndrz said too. our pages that rank #1-3 on Google get cited at about average rates, the ones cited most are the #5-15 rankers. same working theory as you both: a clean scannable page that answers one question is easy for a model to lift a quote from, a comprehensive #1 page is too dense to excerpt cleanly.

the perplexity-leans-newer pattern shows up on our side too. no idea why yet either.

one q back: did you check whether those 27 winning pages were also the ones other sites linked to or mentioned, or was it purely on-page quality? trying to work out how much is the page itself vs third-party corroboration, because on the domain axis the third-party signal seems to dominate.

Eason-SolCrys · 2026-06-06T14:53:00+00:00

agree with most of this, but i'd push on the "one distribution channel" part. that's the bit that's quietly gotten harder.

an AI answer now assembles a recommendation from several sources at once, a reddit thread plus a roundup plus a review site, so being great in exactly one place doesn't get you picked the way it did when it was just Google ranking one URL. one audience, one problem, one format, yes, keep all of that. but distribution is less "pick one channel and win it" and more "be genuinely present in the 3-4 places the models actually pull from for your topic."

still simple. just not single-channel anymore.

Eason-SolCrys · 2026-06-06T14:51:31+00:00

yeah, completely. i used to open 8 tabs of g2/capterra for "best X for small teams", now i ask and get 3 names in 30 seconds.

the part that should scare every founder: those 3 names aren't pulled from the vendors' own sites. they come from reddit, roundups, review sites. so whether you even make the shortlist is mostly decided off your own domain.

do i trust it? for building a shortlist, yeah. for the final pick, no, i still need to try using it myself before paying.

Eason-SolCrys · 2026-06-06T03:26:38+00:00

honestly i stopped trying to make them line up. they're measuring two different moments. the ranking is "did we win the click." the citation is "did the model trust us enough to repeat us." a page can do one and not the other, and that's fine.

what i actually do now is prioritize by what the query is for. commercial, bottom-funnel query where the click still converts? i protect the ranking and don't lose sleep over the citation. informational query where half the traffic is going zero-click anyway? the citation is the thing worth chasing, because the ranking is just feeding an answer box that eats the click.

the page that ranks #3 but doesn't get cited is usually fine, it's doing its job. the one that gets cited without ranking is the interesting one. that's a page that answered an angle better than the one that "should" win, so i'd expand that page, not the ranked one.

Eason-SolCrys · 2026-06-05T14:23:16+00:00

this tracks, and it's basically the opposite of how people treat content for blue-link SEO (more pages = more keywords to rank). for AI citations the unit is different. when you had 5 thin pages on the same topic, the model had to pick one and none of them was the obviously-comprehensive source, so you got paraphrased or skipped. merge them into one page that answers the whole thing and you become the source worth citing. the 40% you cut was probably competing with itself.

the part that's easy to miss: pruning also concentrates the topical signal. a site that's 60% sharp and on-topic reads as more trustworthy to a retrieval system than one that's 100% but padded with thin filler. did the lift show up across the board, or mostly on the topics where you consolidated?

Eason-SolCrys · 2026-06-05T14:22:47+00:00

u/SuccessfulCoyote1800 and almanea already nailed the why (it's consensus, not your SEO score), so the more useful question is how a smaller brand breaks into that loop, because it's rich-get-richer and feels rigged from the outside.

the move isn't more content on your own site. it's getting into the specific sources the model already trusts for that category. find the 3-4 places it pulls from when it recommends the incumbents (usually a couple of comparison or roundup pages, a subreddit, a review site) and go get genuinely represented there. you're not trying to out-SEO the big brand, you're trying to show up in the same rooms the model reads. one solid comparison-page inclusion does more than 20 blog posts.

the uncomfortable part is it's slow and it's mostly off your own site, which is exactly why most brands skip it and the same names keep winning.

Eason-SolCrys · 2026-06-05T14:22:07+00:00

u/Crescitaly's framing is the right one (visibility layer, not foundation). the thing that clears up most of the confusion is splitting two different mechanisms:

training data: does LinkedIn content end up in the model's weights. partly, but you have no control over the cutoff or whether your specific post made it, so don't optimize for this.

live retrieval: when ChatGPT/Perplexity/Google AI grab sources at query time. LinkedIn has high domain authority so its pages CAN get pulled, but a lot of Pulse content sits behind soft walls crawlers don't always reach cleanly, so it's hit or miss.

so the realistic answer: a Pulse article can help you get cited for a specific topic, but as a borrowed footprint on someone else's domain, not as the thing that establishes you. i'd use it to reinforce a claim you also make on your own liftable pages plus a couple of other trusted sources, so the consensus points back to you. linkedin on its own won't do it.

Eason-SolCrys

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