u/Eason-SolCrys

i had a reddit exchange this week that finally put words to the thing i've been building around, so i'm writing it down.

when someone asks an AI which tool to use, the savvy buyers, the ones who ask the narrow question with their real constraints in it, find the right specialist and get a great outcome. but that whole exchange happens inside a private chat. the win, and the evidence of the win, both vanish into a chat log. it never becomes a public sentence the next model gets trained on, or the current one retrieves. so the specialist keeps quietly winning the people who already know how to dig, and stays invisible to everyone who doesn't.

that's the trap, and it's why "best product" and "what the AI recommends" are two different competitions. the model isn't judging quality, it's surfacing whatever the public record already said about you, most of which was written by people other than your happiest customers.

it's also, honestly, the whole reason i'm building solcrys. you can't see the private-chat wins, but you can measure what the models actually say about you across engines, and see exactly where the public version of your best pitch is missing. not faking a market opinion, just surfacing one that already exists in private and moving it somewhere the model can read.

so, building-in-public question for the specialists here: how are you turning your private wins into public evidence? or are you just watching the default get recommended and hoping it changes?

i had a reddit exchange this week that finally put words to the thing i've been building around, so i'm writing it down.

when someone asks an AI which tool to use, the savvy buyers, the ones who ask the narrow question with their real constraints in it, find the right specialist and get a great outcome. but that whole exchange happens inside a private chat. the win, and the evidence of the win, both vanish into a chat log. it never becomes a public sentence the next model gets trained on, or the current one retrieves. so the specialist keeps quietly winning the people who already know how to dig, and stays invisible to everyone who doesn't.

that's the trap, and it's why "best product" and "what the AI recommends" are two different competitions. the model isn't judging quality, it's surfacing whatever the public record already said about you, most of which was written by people other than your happiest customers.

it's also, honestly, the whole reason i'm building solcrys. you can't see the private-chat wins, but you can measure what the models actually say about you across engines, and see exactly where the public version of your best pitch is missing. not faking a market opinion, just surfacing one that already exists in private and moving it somewhere the model can read.

so, building-in-public question for the specialists here: how are you turning your private wins into public evidence? or are you just watching the default get recommended and hoping it changes?

i had a reddit exchange this week that finally put words to the thing i've been building around, so i'm writing it down.

when someone asks an AI which tool to use, the savvy buyers, the ones who ask the narrow question with their real constraints in it, find the right specialist and get a great outcome. but that whole exchange happens inside a private chat. the win, and the evidence of the win, both vanish into a chat log. it never becomes a public sentence the next model gets trained on, or the current one retrieves. so the specialist keeps quietly winning the people who already know how to dig, and stays invisible to everyone who doesn't.

that's the trap, and it's why "best product" and "what the AI recommends" are two different competitions. the model isn't judging quality, it's surfacing whatever the public record already said about you, most of which was written by people other than your happiest customers.

it's also, honestly, the whole reason i'm building solcrys. you can't see the private-chat wins, but you can measure what the models actually say about you across engines, and see exactly where the public version of your best pitch is missing. not faking a market opinion, just surfacing one that already exists in private and moving it somewhere the model can read.

so, building-in-public question for the specialists here: how are you turning your private wins into public evidence? or are you just watching the default get recommended and hoping it changes?

we all know by now that when an AI recommends a brand, it leans on third-party sources more than the brand's own site. the usual takeaway is "go get more third-party mentions." i think that's the wrong lesson.

the sources the model trusts aren't neutral, and the incentive behind each one tells you its bias before you even read it. a retailer page is accurate on price and spec but skews any comparison toward its own margin. an affiliate roundup skews toward whoever converts, not whoever's actually best. a forum like reddit is the hardest to buy, which makes it the highest-trust input the model has and also the one you can least control.

so "third-party" doesn't mean "independent." the AI isn't reading a neutral web, it's reading an incentive map, and it's weighting that map without knowing the incentives behind it.

what that changes in practice: the goal isn't to be on more sources, it's to figure out which of the model's trusted sources have an incentive that lines up with the truth about you, and win those. the ones whose incentive cuts against you, you don't try to capture, you monitor them and make sure enough aligned sources out-corroborate them.

the uncomfortable part is that the most-trusted sources, the unbuyable ones, are exactly the ones you can't just go place content on. you earn them or you don't.

when you look at the sources behind your own AI answers, do you map them by incentive, or just by whether you're mentioned?

half the GEO advice right now is some version of "add more schema and the AI will cite you." i've stopped believing it, and here's the distinction that made it click.

schema does one thing genuinely well: it makes you machine-readable. instead of the model flattening your page into tokens and guessing which feature attaches to which product, a clean JSON-LD block hands it a deterministic statement of the facts. that's real, and worth doing. it's a comprehension win.

but here's the part the hype skips. the model has no way to know the markup is actually yours and true. a competitor can put the same @ type on their page. a parody site can fill in the same fields. nothing in the markup proves whose claim is real. so your schema is a claim you make about yourself, not a verdict the model has to accept. a claim, not a passport.

which means schema makes your claim legible, it doesn't make it win. the model still decides what's true about you by corroborating across the sources it trusts, and most of those are third-party, not your own marked-up page. clean schema raises your odds of being read correctly, it does almost nothing to get you recommended if the surrounding sources don't back you up.

the practical version: if you're being described wrong or confused with another company, schema (a clean Organization node, facts that mirror your visible page) is often part of the fix, because that's a comprehension problem. if you're just absent from the answers, schema won't move it, because that's a corroboration problem, and no amount of markup outvotes the sources.

has anyone actually seen schema move whether you get recommended, separate from whether you get read correctly? that's the line i keep trying to draw.

we spend all our time on visibility, am i cited, what's my share of voice, which sources does the model pull. fair, that's half the game. but i keep hitting the other half nobody tracks: when the AI does mention you, does it get you right?

i've watched models confidently quote a price that changed months ago, list a feature that was deprecated, and "compare" two products on a spec that's just wrong. no hedging, full confidence. the buyer reads it, believes it, and decides on it. and you never see it happen, there's no 404, no bounce, no analytics line for "lost the deal because chatgpt quoted our old pricing."

here's why mentioned-but-wrong is worse than absent. if you're not mentioned, you're invisible, which is at least neutral and fixable. if you're mentioned wrong, you're actively mispositioned by the most trusted-sounding voice in the buyer's journey, and it compounds, because models lean on each other and the wrong fact propagates.

the cause is mechanical, not malicious. the model isn't storing curated facts about you, it's pulling whatever some source said and parroting it. so a stale listicle or an old review quietly becomes "the truth" about your current product.

genuinely curious how many people here even check this. do you track how accurately you're described, or just whether you're described? and if you've caught a wrong fact, did you trace which source the model pulled it from?

i've spent about a year building SolCrys, a tool that measures whether AI engines (chatgpt, perplexity, google's AI answers) actually recommend a brand when someone asks for one. the whole pitch is "stop guessing, measure who AI names in your category."

so a few weeks ago we did the obvious uncomfortable thing and pointed it at ourselves.

the results were humbling. across about 24,000 citations in our category, our own site shows up around rank 8 or 9, under 2% of all citations. reddit is the single most-cited source. wikipedia is second. the engines barely even agree with each other on what to pull.

now, 2% sounds tiny, but it's actually top-10 out of ~2,000 domains, so it's not nothing. the gut-punch is realizing that everything we could ever publish on our own site is competing with reddit threads and wikipedia and third-party roundups that, added up, dwarf it. you cannot out-blog your way into AI answers. the citations live off your property.

that one chart rewired how we spend our time. less "publish more pages," more "be a real, credible source where the conversation already happens." which is uncomfortable for a content-heavy plan and is exactly why i'm in threads like this one.

genuine question for the builders here: have you actually checked whether AI engines recommend you, or are you assuming your content is doing that job? because when we checked, the answer was not what we'd assumed.

we track AI citations for our own category (the AI-search / AEO tooling space) and i finally stopped looking at the numbers pooled and broke them down by engine. about 19k citations, 14 days, five engines (chatgpt, gemini, google ai overviews, perplexity, claude), the same 22 buyer prompts run against each one.

the thing i wasn't ready for: the engines barely cite the same sources.

wikipedia is the #2 most-cited domain in the whole dataset, something like 950 citations. it's cited by exactly one of the five engines. chatgpt by itself drags it to #2. the other four basically never pull it. same shape for techradar at #3, chatgpt-only again.

reddit is #1 overall (~1,600) and even it's missing from two of the five, perplexity and claude don't pull reddit in our set at all. youtube ranks high too and chatgpt ignores it.

the only domains all five engines agree on are the vendor and tool sites in the category, the semrushes and hubspots and a couple of the AEO tools. and i think that's the tell. the cross-engine overlap is mostly brand-name lookups, where every engine just fetches the same homepage. for the actual discovery prompts ("best tool for x", "how do i do y") each engine is off in its own world of sources.

what it changed for me: "get cited by AI" isn't one goal, it's five weakly-overlapping ones. a page that earns you chatgpt citations (encyclopedic, editorial, wikipedia-shaped) can do nothing for perplexity, which is reaching for a different set entirely. if you're optimizing against a single blended "AI visibility" score you're averaging across five systems that don't agree, and the average ends up describing none of them.

honest caveat: this is one category, and a weird one (AI-search tooling is incestuous, the tools all cite each other). claude had a smaller response count this window so some of its misses are partly just volume, not a real snub. the two i'd actually stake something on are chatgpt basically owning wikipedia, and perplexity skipping reddit.

so the real question, has anyone here split their citation data by engine instead of pooling it? does the near-zero overlap on the top sources hold in your category too, or is mine just an artifact of a niche vertical. genuinely curious whether per-engine source diets are a general thing or just my data being strange.

Spent a while actually using the free tiers of the AEO / AI-visibility tools, and they kept stopping at the same place. So i scored 16 of them on four steps: can it measure where you stand in AI answers, diagnose what's wrong, actually hand you the fix to ship, and re-test to prove the fix worked.

Almost everything clusters at step one or two. A few notes from doing it:

Free-forever tracking is basically one tool (Trakkr). one brand, ~8 engines, daily. measures fine, doesn't try to fix anything.

The big free "graders" (HubSpot, Ahrefs, Semrush, Mangools, plus Rankscale / Knowatoa / Goodie) give you a score and sometimes a gap list, then you're on your own.

The "free" ones from Peec, Otterly, Scrunch, Nightwatch, Am I on AI are really trials, so the clock's running and the fix layer is paid anyway.

Profound has no free tier at all.

The part that surprised me: on a free tier, basically nothing reaches "execute," meaning it generates the actual fix (the JSON-LD block, the heading rewrite) tied to the gap it just measured. most stop at a number. closest free thing i found was Writesonic's LLM Optimizer, but it's a standalone rewriter that doesn't know which prompt you're losing.

one thing most lists blur that's worth separating: "free trial" and "free forever" are completely different commitments.

full disclosure, i work on one of these (SolCrys), so discount my take accordingly. i scored everyone the same way though, and dumped the whole table plus per-tool sources into one writeup if it's useful: https://solcrys.com/free-aeo-tools-that-fix-not-just-score/

genuinely curious if i missed a free tool that measures AND hands you the fix, not just the score.

https://preview.redd.it/hcmuyt9h3s6h1.png?width=1200&format=png&auto=webp&s=3c19220a2b35d23ff212fa702b6d1f243c75fafe

last week i posted three "testable GEO claims" here, mostly to see if framing this stuff as small experiments beat the usual abstract advice. the comments added four more and made the originals sharper, and honestly the additions were better than what i started with. so here's the consolidated version, credited, because the people who said them did the work.

the original three:

1. extractability is a per-heading property, not a per-page one. one buried H2 drags the whole page down. ( u/Brave_Acanthaceae863, who's running the per-section test)
2. a wrong AI description doesn't flip when you fix your page, it heals on each engine's re-crawl clock, days to weeks. ( u/QuietMomentum89, who tracked the day-by-day timeline)
3. drop the single "AI visibility score", build a source map (which domains get cited per buyer prompt) and split branded vs discovery prompts. ( u/jasper_zerotouser and u/Upstairs_Control_611)

the four you added:

4. run every test about ten times before believing it. AI answers are non-deterministic, so a single run is a coin flip, you want an appearance rate, not a yes/no. this is the prerequisite for all the others. ( u/Tovi_AI)

5. test at the specificity level a buyer actually uses. a brand at 7/10 on "best tool for x" can drop to 1/10 once you add a constraint like budget, and that constrained prompt is where the buyer stands. ( u/ayzeo_com)

6. test per language. the source map changes completely in spanish or hindi, and a brand invisible in english can be the only clear answer in a lower-competition language. ( u/Formal-Brother-7831)

7. sort each cited source by the move, with a competitive-query test. survives a broader query, it's authority you earn into. vanishes, it was only there because nothing else covered the niche, so you out-publish it. ( u/Conscious_Ad_821)

the through-line for me: AI answers don't repeat, so every one of these is really "measure a rate, at the specificity and language your buyer actually uses." none of it needs a tool, you can run all seven by hand in an afternoon.

one bit of my own data for #4, since people asked: across a recent two-week window the same prompt set swung from about 3.5 to 8.4 percent run to run, with the windowed average near 6. single runs are noise.

so, what's #8?

most GEO content is vibes. this sub had an unusually good week, and three of the things that came up are actually testable, not just takes. crediting where each came from, because the people who said them did the work.

1. Extractability is a per-heading property, not a per-page one. (h/t u/Brave_Acanthaceae863)

he'd simplified a batch of pages and got inconsistent results, until he looked section by section. the pages that stayed flat had the answer buried under one bad H2, dragging the whole page's extractability down. so "make the page shorter" is the wrong frame. "make each section's answer reachable in the first couple lines" is the version you can test, heading by heading, with an inverted pyramid per section. he's running that test now. (related: when he pruned ~40% of thin pages, even untouched pages lifted ~10-15% and held at week 6, which hints at a domain-level trust threshold, not just per-page gains.)

2. A wrong AI description heals gradually, on a re-crawl clock. (h/t u/QuietMomentum89)

after fixing a canonical page, he watched Perplexity not flip but climb: the cited description went from a stale listing, to website chunks around day 4, to verbatim feature copy by day 12. each re-crawl pulled a richer chunk from the same fixed page. Claude flipped clean instead. the testable takeaway: a correct fix looks like it failed for the first week because nothing has re-crawled yet. don't kill the experiment early. (this lines up with what we see on our side, the description is per-engine and updates on each engine's own cadence.)

3. Drop the single "AI visibility score." build a source map and split branded vs discovery. (h/t u/jasper_zerotouser and u/Upstairs_Control_611)

two moves. one: for each buyer prompt, log the actual domains the model cited to build its answer, then tag them (your site / competitor / editorial / reddit-UGC). after a dozen prompts the same 3-4 sources keep showing up, and that's your target list instead of a guess. two: split branded prompts from discovery prompts ("best tool for X"). a brand looks highly visible on branded queries and can be almost invisible the moment the journey starts with a problem instead of a name, which is the only place a new buyer finds you. a blended score hides exactly that gap.

the common thread: all three are measurable in a week, by you, without a vendor. GEO has too many opinions and too little data. if you've got a fourth that's actually testable, drop it, i'll run it.

a couple of threads here this week (the StillMind "cited but described wrong" one, and the page-pruning one) got me looking at the description, not just the citation. so i pulled our own data on it. two things stood out.

1. the description is per-engine. over two weeks of our own tracking, gemini and google's AI describe us in positive terms, while chatgpt and perplexity describe us flat and neutral. same prompts, same window, same brand. so "are we described well" isn't one number, it's four different answers depending on the engine.

2. when you fix your canonical page, the description doesn't flip, it propagates. and the shape depends on how the engine retrieves. live-retrieval engines (perplexity, chatgpt search) climb, each re-crawl pulls a richer, more accurate chunk from your fixed page. snapshot-y ones (claude) stay wrong until they re-fetch, then flip clean. google updates on its normal index cadence. u/QuietMomentum89 described the perplexity climb in detail in the stillmind thread (stale listing → website chunks → verbatim feature copy by day 12), which matches what we see.

the practical part: a correct fix looks like it failed for the first week, because nothing has re-crawled yet. killing the experiment after one bad week is the same mistake as reading one noisy run as a trend.

has anyone measured the correction lag cleanly by engine? curious whether the perplexity enrichment plateaus, or keeps pulling richer chunks until it's basically quoting your whole page.

Most citation-concentration posts here look at which of your own pages get cited. We flipped it to the domain axis: across a whole category, which domains does AI actually pull from when it answers buyer questions?

What we did 

Ran a fixed set of category prompts through ChatGPT, Gemini, Perplexity, and Google AI Overviews for two weeks. Logged every cited domain, roughly 15,000 citations across about 1,800 domains, and tagged each as owned / competitor / editorial / UGC.

The finding 

Brutally concentrated, and not where you'd expect.

• #1 was reddit, at about 9% of all citations. That's more than wikipedia and techradar combined.
• The entire top tier was third parties: reddit, wikipedia, arxiv, techradar, semrush, profound. All of them ahead of us.
• We ran this on our own category, and our own site only showed up after that whole stack, at about 2%.

So the brand being measured is basically a rounding error in its own category's citations. The model isn't pulling from your domain, it's assembling the answer from a small set of sources it already trusts: UGC (reddit, youtube), reference (wikipedia, arxiv), and a handful of comparison/roundup pages.

What it means for GEO 

The "just publish more on our own site" instinct is optimizing a 2% surface. The leverage is getting genuinely represented in the few third-party sources the model actually pulls. And reddit sitting at #1 is not a coincidence, it's basically why everyone's suddenly showing up in these subs.

Honest caveats 

Exact counts drift run to run, so I'm giving ranks and rounded percentages, not false-precise numbers. And this is one category (ours), so I don't know how far it generalizes.

Genuinely curious: for those of you tracking this, is reddit #1 in your category too? Or does it flip to editorial / competitor domains in less community-driven niches?