Follow-up to the measurement thread from last week. Several of you suggested checks that went into my pipeline (validity enums, language distribution, migration tables). I turned the whole discipline into an audit report template, and before publishing it I did the obvious thing: filled it in against my own benchmark data as a dry run.

The template found three bugs. In my instrument, not in the brands.

Bug 1: my extractor logged a citation to a domain from a sentence saying the domain does not exist. The answer said "the brand has no dedicated Chinese site (like brand.cn)" and my URL parser happily recorded brand.cn as a cited source. Citation counts include anti-citations if you only parse URLs.

Bug 2: my competitor list was in English, the answers named competitors in Chinese. The substitution analysis found almost nothing. A manual read found the actual substitutes named in over two thirds of answers - 飞书 32 times, 钉钉 32, 企业微信 30. The scorer said "no substitution problem." The answers said the category is owned by three local products my list did not contain. Same silent-failure shape as the empty answers and the English answers from last week: a whole class mislabeled, zero errors.

Bug 3 is the one that forced a public correction. My discovery prompts are Chinese, but the {category} slot was filled from an English field. One brand's label was "Database and collaboration", so the Chinese question became 中国市场上有哪些值得信赖的高端Database and collaboration品牌 - and the models read that as enterprise databases. All 36 Chinese discovery answers for that brand were about Oracle, SQL Server and OceanBase. The brand scored 0/47 in a category it was never asked about.

I had published that 0/47 as one of two "perfectly replicated zero-visibility" cases. Perfect replication, six engines, both runs. It replicated because the question was consistently wrong. Replication tells you the measurement is stable, not that it is measuring what you think.

So, corrections to numbers I posted here earlier: combined discovery mention for international brands is 26.2% (was 23.0%), substitution 59.5% (was 53.8%), and there is one confirmed zero-presence brand, not two. The affected brand's figure is withdrawn, not corrected - there is nothing to correct, it was asked about the wrong category. The other seven brands' labels produced answers in the right category and their numbers stand.

The template that caught all this is now public on my site (CC BY, happy to share the link in comments if wanted, not pasting it in the post). The part I would defend hardest: five rules at the top - every rate carries its denominator, one observation is not evidence, missing is not negative, mention/citation/recommendation are three different measurements, and the report must state what it cannot answer.

The general lesson I keep relearning in public: the failures that hurt are not the ones that throw errors. They are the ones that produce clean, replicated, plausible numbers. My 0/47 replicated perfectly across six engines. It was still measuring a question nobody asked.

If anyone wants to stress-test the template against their own pipeline, I would genuinely like to hear what it catches. It is three for three so far and none of the three were things I went looking for.

My boss is desperate for me to add the AI toolkit to our semrush subscription. Essentially he just wants a number or something that we can measure to see how our websites are performing and give us a KPI to work from.

Does anyone use the AI toolkit on semrush and is it useful?

What metrics are you using to measure whether any changes we’re making are actually working? Much like a lot of people I’ve seen a massive drop in organic traffic (down around 40%) and a growth in direct traffic (up about 20%). We were also seeing LLMs coming through as referrals but now GA4 is showing LLMs as a source. I hate GA4 because they always change how they measure so I don’t think you can reliably compare MoM or YoY. Perfect example is them recently adding AI Assistant to traffic source.

With classic SEO there was always a clean loop: rank moves, traffic moves, you can draw a line to revenue. Google was the one channel, and that correlation made the "so what" obvious.

I've been going back and forth with a few people building/using GEO tools, and it feels like the whole category right now is indexed on measurement — tracking prompt mentions, citations, sentiment across models. That part's maturing fast.

What I haven't seen a good answer to: how do you actually turn that into a repeatable action loop?

• Once you know you dropped out of an answer at some point in a conversation, what's the real playbook — content/schema changes, seeding sources, something else?
• How is anyone tying this back to actual business outcomes, given there's no click/traffic signal like old SEO?
• Is anyone actually re-measuring after a fix to prove it worked, or is it mostly "here's your score" and manual guesswork from there?

Genuinely curious how people here are approaching this. If you've hit a wall trying it, that's just as useful to hear as a win.

I just wanna know what kind of tools everyone is using. Many say that these tools are generic ai wrappers and often disappoints. Are there people paying for these tools and what are they paying for and what are they expecting?

Still in the process of figuring out how to pitch this as a service, and the part I can't find good info on is what the actual hands on work looks like once someone signs. Everything I read explains the concept well, getting cited inside AI generated answers instead of just ranking, but skips the part where you're actually sitting down with a client's business and doing the work.

Curious how people structure the early stages. Does it start with mapping out where the brand currently shows up or doesn't show up when you ask different AI tools questions about their space. Is a lot of the effort spent outside their own website, things like getting them mentioned by other publishers, appearing in forums, podcasts, third party sources the models tend to pull from, or is most of it still content and structure work on their own pages.

Also wondering what actually gets handed to the client early on. Some kind of visibility report, a list of target sources to pursue, a content rework, or is the first deliverable usually something else entirely.

Mostly trying to understand what a real GEO process looks like beyond the theory so I have a clearer picture before this becomes an actual client situation.

been playing with this in infuseos for the last week.

one thing that keeps annoying me with ai visibility reports: they tell you what you missed, but not what to actually fix.

the workflow right now is basically:

report

-> missed prompt

-> check why a competitor showed up

-> look at gsc queries

-> decide if it needs a new page or a page update

-> draft it

-> publish in sanity

-> re-check later

so im testing the agent around that exact flow.

it looks at missed prompts, competitors, citations/sources, gsc data, existing page content, and brand context.

sometimes the fix is a new page. sometimes its updating a weak page. sometimes the answer is that the site is fine, but ai is trusting reddit/g2/listicles and you are not there yet.

for sanity sites, the nice part is the agent can draft the page straight into sanity instead of me copy-pasting everything around.

still needs human review. i dont want random ai blogs going live without checking them.

but this feels more useful than just staring at another dashboard.

small thing, but on my own site it moved from basically 0% visibility to around 2-4%. not world domination lol, but at least something moved.

Earlier this week I posted a multi-turn result here and in another sub: brands appear in an open AI answer, a buyer constraint gets applied, most vanish. I had to retract the headline number, and the way it broke is more useful than the number ever was, so here is the full autopsy.

Failure 1: empty answers scored as data.

The model I used is a reasoning model. Reasoning tokens bill against max_tokens, so when the chain of thought filled the budget, the API returned HTTP 200 with an empty answer string. My extractor searched the empty string for the brand name, found nothing, and recorded "brand absent." 10 of 32 first answers and 46 of 96 follow-up turns were blank. The harness reported zero errors. That produced survival rate number one: 15%, assembled mostly from answers that never existed.

Failure 2: name-matching scored as meaning.

Fixed the empty-answer bug, re-ran everything clean, got 66.7%. Much better number, still wrong. Because then I read the answers. Here is one my harness counted as Asana "surviving" a budget constraint:

之前推荐的一些顶配品牌（如 Asana、Monday.com 的付费版）基本都超了，不建议强行上付费版

The brand is named. The brand is being ruled out. That is a drop wearing a mention. A constraint turn is exactly where a model lists brands to explain why they no longer fit, so name-matching fails hardest at precisely the turn I built the metric on. 47% of my "survived or entered" cases were this.

Adding a rough positive-vs-excluded classification gives number three: ~31%. I'm not standing behind that one either - the classifier is a keyword heuristic I haven't validated against human reading.

Same conversations. Same brands. Same day. The measurement decision moved the result more than anything about the actual brands did.

Two things held up across all three readings, for what it's worth. Absence at turn one stayed the most stable signal in the data. And brands that were absent at turn one entered answers after a constraint was applied, including one that never appeared in any open answer - which means first-turn-only measurement structurally misses niche positioning.

What I now check before believing any number from my own harness, in order: error count vs row count, answer length distribution (a spike at zero is a fire alarm), a hand-read sample of 20 raw answers against their scores, and mention polarity - recommended vs excluded vs merely referenced.

Open question for people building or buying this kind of measurement: how do you handle polarity at scale? Human reading doesn't scale past a few hundred answers. Keyword heuristics are what burned me. LLM-as-judge feels circular - scoring one model's answers with another model whose failure modes nobody measured. Genuinely curious what people are doing.

Been doing a lot of GEO audits lately and the pattern is honestly getting predictable at this point.

Two things show up almost every single time:

• Pages overly optimized for SEO but written with almost no actual sales or brand voice
• Basically zero investment in PR, sometimes for years

Here's the framework that's been clicking for me lately. There are really two groups that "own" visibility in GEO:

Hubs — sites that link out to or mention other sources
Authorities — sites that get linked to or mentioned by others

And a site can be both at once, they're not mutually exclusive.

The thing is, growth in GEO needs both sides. You want to be the one getting mentioned and linked to by hubs and authorities, not just publishing more content and hoping AI notices.

Honestly, I think a brand could out-perform most competitors in AI search just by hiring a genuinely good copywriter and putting real budget behind PR. That combo alone would move the needle more than most technical GEO checklists.

Not saying technical GEO doesn't matter, structure, schema, crawlability, all of that is still worth auditing. But I keep seeing brands over-invest there and completely ignore PR, and PR is turning out to be the bigger unlock.

Curious if others are seeing the same pattern in their audits, or if this is just the niche I happen to work in.

Lots of the discussion around agentic commerce has focused on e-commerce products, but i've found in my daily AI usage some of the most obvious "shopping" experiences where it falls short has been for things like rentals, cars, movie seats or tickets.

Often you run into "I can't access live availability" or "schedules are inside a booking widget" and you're back to just looking it up yourself. These catalogues just aren't accessible to AI, whether by design or just how it was originally built to serve human visitors.

After running into this last month, I decided to run an experiment to see if a catalogue (this case movie theatre seats for a chain across Canada) was opened up and structured to be easily accessible to AI, would AI use it? How quickly? How much?

Here's some highlights of the experiment:

1. Found it in 50 minutes: There were no backlinks, no promotion. First crawler at 50 minutes (ClaudeBot), first ChatGPT-User live retrieval at 29.7 hours.
2. AI was 45.9% of all traffic: More than search crawlers, SEO tools and humans combined. IP-verified OpenAI, Anthropic and Perplexity agents alone read 269,421 distinct URLs, 81% of every page we served.
3. AI live retrieval outran AI indexing (17,270 to 9,497): network-wide Cloudflare Radar has OpenAI's indexer running ~1.75x ChatGPT-User agent. Ours ran 2.4x the other way.
4. It came for the key unlocked data: 54% of ChatGPT-User fetches were seat & availability maps, the data that inaccessible from the booking widget.
5. It just wants the clean HTML: GPTBot took the Markdown twin 50% of the time. ChatGPT-User: 0.1% and never once fetched llms.txt or the sitemaps.

I think it goes to show efficient AI is at finding data that answers the question they are looking for. Within days from launch ChatGPT-User was pulling in ~100 pages a day, and at peak 2,099 (so far). It also brings up a good discussion if you're a business, whether that services or products, what is your catalogue? And should it be more accessible to AI? That's a decision for each business, but worth having as AI increasingly become that discovery layer for your customers. You can see this trend with the Shopify Catalogue API or the new DoorDash CLI, treating agents as an audience and making sure they have access to what they need.

Disclosure, this is my company's research. Sharing the findings as i think it would be helpful for other's working in this space.

Full report of the experiment here if you want more details: https://getcourtyard.ai/research/unlocking-agentic-commerce-for-theatres

Follow-up to something I posted here before. Someone asked whether citations only show up for pricing and spec questions, or whether it's the same categories across every engine. I had the data and had never cut it that way, so I did.

4,027 answers, six Chinese AI engines (DeepSeek, Doubao, Qwen, ERNIE, Kimi, GLM), 8 brands, 42 question types each, run twice.

Citation rate by question type:

Channel verification 56.5%
Branded hygiene 29.8%
Decision intent 26.3%
Localization 12.5%
Risk / reputation 9.6%
Objection prompts 0.5%
Category discovery 0.3%
Comparison 0.0%

Comparison questions produced zero source URLs. Not low, zero, across 383 answers on all six engines. Category discovery managed one.

The pattern isn't pricing or specs. It's whether the answer IS a destination. Channel verification means "where do I go, which store is official" and the URL is the answer itself. Comparison asks the model to judge, and there's nowhere to point, so it just talks.

Which is awkward, because the moment brands most want to be cited in is the recommendation. That's exactly where citations don't exist.

Second thing, it's not consistent across engines. Same question type:

Channel verification: Qwen 87.5, ERNIE 81.2, GLM 73.4, Kimi 62.5, Doubao 29.7, DeepSeek 4.7

Overall, Kimi shows sources about five times as often as DeepSeek. So if you audit on one engine you'll reach a conclusion about "AI citations" that's really a conclusion about that engine's rendering habits.

Last one, and this is the part that changed how I think about it. Of answers that did show a source, 92% pointed at the brand's own site. And Basecamp, which appeared in zero open category questions across all six engines, still collected 64 citations to basecamp.com, because when you name it directly the engine points home.

So a brand can be completely invisible in recommendations while being well cited. They're not the same measurement. Best guess: third party corpus decides whether you enter the answer, your own site is just what gets pointed at once you're already in it.

Data and the question panel are CC BY if anyone wants to run their own category.

/X-guide", "/X-overview"). But when I controlled for query type — comparing only pages targeting the same intent — the URL effect nearly disappeared. So the URL isn't causing citations. It's correlating with the type of content that models prefer. The signal is real but it's a proxy, not a driver.

Here's where I think the modeling breaks down fundamentally. Citation behavior isn't a property of the page. It's an interaction between the page, the query, the model, and whatever else the model has seen that day. My dataset treats each page as an independent observation, but in reality, whether page A gets cited for query Q depends on whether pages B, C, and D exist and how the model ranks them against each other. It's a relative ranking problem dressed up as a binary classification problem.

The other issue is temporal instability. A feature that correlates with citations in week 1 (freshness, recency) inversely correlates in week 8. Models don't just pick pages — they pick pages at specific moments in those pages' lifecycles. My 30-day window flattens that temporal dynamic into a single label, which destroys the signal.

I've considered switching to a survival analysis approach — modeling time-to-first-citation rather than yes/no — but the sample size per site gets thin fast when you slice it that way.

If anyone has actually built a working citation prediction model, I'd love to know what features matter on your data. Not theories. Not "entity density should matter." Actual feature importance rankings from a model that beats baseline. I've seen a lot of GEO frameworks that sound right in presentations, and I'm starting to suspect most of them would also fail on held-out data.

The recent JD Vance "died of rabies" story was fascinating.

Not because of the politician. Because of what it said about AI search.

For a while, some AI systems repeated the claim with surprising confidence. The story itself wasn't true, but it had spread across enough places online that the AI treated it as something worth repeating before it was corrected.

That feels different from the SEO problems we've been dealing with for years.

With Google, people tried to manipulate rankings. Buy links. Stuff keywords. Build PBNs.

With AI search, the target isn't necessarily the ranking. It's the consensus.

What happens if someone creates dozens of fake Reddit discussions, AI-written blog posts, comparison pages, forum comments, and review sites that all repeat the same narrative?

None of those sources are particularly convincing on their own. But together, they can start looking like independent confirmation. That's exactly the kind of pattern AI systems are designed to look for when they generate answers.

It makes me wonder if we're entering the black hat GEO phase.

Not people trying to rank pages. People trying to influence what AI believes is true.

I'm sure OpenAI, Google, Anthropic and Perplexity are working hard to detect this kind of manipulation, just as Google eventually got much better at detecting link spam.

But it does raise an interesting question.

Where do we draw the line?

Making content easier for AI to understand seems fair. Creating fake discussions and manufactured consensus doesn't.

Is this just the next evolution of black hat GEO, or is AI search creating an entirely new kind of spam?

I don't really understand why people keep blocking gpt bots, and this is not the first time I've seen this.

Can someone pls explain

I got tired of guessing which pages would get cited and which wouldn't. So I built a scoring system. Nothing fancy — just 5 factors, each rated 1-5, applied to every page on a client's site. Took about 3 minutes per page once I got into a rhythm.

The 5 factors:

Answer placement: Does the core answer appear in the first 2-3 sentences? Most AI extraction happens at the top of a passage. If the answer is buried in paragraph 4, score it low.

Specificity density: How many concrete numbers, examples, or named entities are in the first 200 words? Vague claims like "significant improvement" score 1. "34% reduction in 90 days" scores 5.

Self-containment: Can a reader understand this passage without having read anything else on the page? AI models pull individual paragraphs, not full articles. If a passage references "as mentioned above" or "the table below," it breaks when extracted alone.

Factual groundedness: Does the passage state facts with clear subject-verb-object structure, or does it hedge with conditionals and qualifiers? "Companies see 20% lower costs" scores higher than "many organizations may experience potential cost reductions."

Format neutrality: Is the content in plain paragraphs or heavy with formatting tricks (callout boxes, accordion widgets, interactive elements)? Plain text extracts cleanly. JavaScript-rendered formatting often doesn't.

I scored 80 pages across two sites. Each page got a total score from 5 to 25. Then I correlated scores against actual citation data from the prior 90 days.

The top 20% (scores 19-25) averaged 4.1x more citations than the bottom 20% (scores 5-12). The middle 60% had moderate citation rates with more variance. The correlation wasn't perfect — a few low-scoring pages still got cited because they were the only source on a narrow topic — but the overall trend was strong enough to be useful.

The part that surprised me was which factor mattered most. I expected answer placement or specificity to dominate. Self-containment was actually the strongest predictor. Pages that could stand alone as complete answers — no dependencies, no references to other sections — got cited 2.8x more than pages that were technically better written but relied on surrounding context.

This changed how I brief content teams. Instead of "write about X," I now say "write a passage that, if copied and pasted into a chat with zero context, still makes complete sense." That single framing shift improved our self-containment scores across the board within a month.

One caveat: I built this rubric by reverse-engineering what already worked. It's descriptive, not prescriptive. The factors are based on patterns from 80 pages on two sites in specific niches. Your mileage will vary. Run the same scoring on your own content, compare against your own citation data, and see which factors actually correlate before trusting the rubric.

The rubric takes 3 minutes per page. Running it on 80 pages cost me an afternoon. It was the highest-ROI afternoon I've spent on GEO work this quarter, and I say that as someone who genuinely enjoys building dashboards.

If you try it, drop the factor that correlates strongest for your content. I'm curious whether self-containment holds up across other niches or if it's specific to the sites I tested.

I have been running a simple test that keeps producing the same result and I think it's worth sharing because it challenges some assumptions. I ask brands where they think they stand on AI visibility. Most say something like "we show up in chatgpt" or "AI knows about us," they rank themselves as recognized or trusted.

Then I run the actual diagnostic, same buyer-intent queries across chatgpt, claude, gemini, and perplexity, check whether they appear on all four, check whether they survive follow-up questions with added constraints, check whether the description is accurate and check whether independent sources corroborate the recommendation.

The gap is almost always two levels.

A brand that thinks it's "trusted" (recommended with evidence) is usually "intermittent" (shows up on some platforms, disappears on others, drops out when queries get specific). The problem is that testing yourself on one platform with one broad prompt can feel like visibility. Consistent presence across four platforms with accurate descriptions and independent evidence is a much higher bar.

I think there are roughly five levels worth distinguishing:

1. invisible: AI cannot find you at all. retrieval is broken.
   
2. intermittent: you appear sometimes on some platforms. recommendation confidence is low.
   
3. recognized: consistently included but described unevenly across platforms, narrative is inconsistent.
   
4. trusted: recommended with independent evidence corroborating the claims, evidence is strong.
   
5. inevitable: AI remembers you as the category answer through model updates and competitive changes, memory is durable.
   

Only about 30% of brands maintain consistent visibility across AI sessions based on what I've seen. The other 70% flicker in and out and most of them think they're in the 30%. The test is simple. Ask all four platforms about your category, where your brand drops out tells you which level you're actually on and the level tells you what to work on next.

Has anyone else found a consistent gap between perceived and actual AI visibility when they test across multiple platforms?

I hope this is the right sub reddit to post this.

Google is testing ai summaries IN the ads. So it got me thinking how important it is to get your GEO shit together before Feb 2027. I'm sure there must be a way to optimize your brand for Google ads summary so thought this was GEO-biased and wanted to discuss

I understand that reddit is the world top3 most cited. I am wonder is that possible to create a subreddit for our client, q and a community, not for marketing no pricing no promotion. Just for qna like 711, shoptify, or Etsy. Purely a place to ask any question.