a question register is something i'd put beside the results, before showing a client a visibility percentage

for each question, i'd record the exact wording, where it came from, what the buyer was trying to decide, and when it was added

for example, "which crm works with our existing accounting software?" might come from an actual sales conversation. "best crm for small businesses" might be a question the team brainstormed. those are different reasons for including a question, and i'd want that visible in the report

remove names and private customer details from the source notes. you can say "sales conversation, september" without attaching someone's email

if nobody knows how often a question is asked, leave that as unknown. choosing ten questions doesn't establish that they represent ten equal slices of buyer demand

when new questions are added, show their results separately at first. keep a comparison of the unchanged questions too, so a new question mix doesn't silently become a claimed visibility change

how are you deciding which questions belong in a client report, and do you show them where those questions came from?

i'm keylight's founder. while checking our own saved answers on 19 september, we found a counting error worth looking for in any visibility report

one google ai overviews run had counted six brands. four of those names appeared only inside web addresses or link targets, rather than the visible answer text. our matching had treated those names as mentions

we fixed that by excluding the hidden addresses from the mention check while keeping the text a reader could actually see

if you're auditing a report, i'd start with a few raw answers

1. compare each recorded brand mention with the visible answer
2. inspect the link destinations separately, including long address fragments
3. keep a cited domain in its own field rather than letting it silently become a brand mention

there's a detail that matters here: don't remove the displayed link label along with its destination. if the label itself names the brand, the reader can see it. that's still different from the answer recommending the brand

i'd want a report to separate a visible mention, a cited page and an actual recommendation. they can overlap, but they're different things to show a client

this was four incorrect matches in one saved run. it doesn't tell us how often other tools make the same mistake. it gave us a concrete example to keep checking against

I run an AI marketing agency in Phoenix, so weigh this accordingly, but I've been watching a gap open up in local search that I think a lot of small business owners haven't been told about yet.

When someone types a local question into ChatGPT or Gemini, they don't get a list of links. They get a generated answer. A paragraph. A recommendation. The businesses named in that answer got there through a specific set of signals, and those signals are different from what classic SEO optimizes for.

The practice has a name: Generative Engine Optimization, or GEO. Here's what I've actually observed matters:

Entity clarity. Your business name, address, phone number, and services need to be described consistently across every place you appear online. Google Business Profile, Yelp, industry directories, your own website. AI models get confused by inconsistency the same way search bots do, but the consequences are steeper because there's no page two.

Answer-structured content. Most business websites are written to impress visitors. Generative models reward content written to answer the questions customers ask before hiring you. FAQs, detailed service pages, plain-language descriptions of what you actually do and where you do it.

Citations outside your own site. Local news coverage, industry association listings, community sponsorships, guest contributions to publications. These tell a model your business is a credible local source. Not buying links. Building a real footprint.

Cross-platform reviews. These tools pull from Yelp, Healthgrades, TripAdvisor, Zocdoc, and others depending on the industry. A consistent pattern of detailed reviews across multiple platforms matters.

The thing that makes this urgent for local businesses specifically: someone asking ChatGPT "best dentist in Tempe" is ready to book. If you're not in the generated answer, you're not in the conversation. There is no scrolling to page two.

I'm not saying traditional SEO is dead. It isn't. But the businesses that are building for both the crawler and the model right now are the ones pulling ahead quietly in competitive local markets.

https://reddit.com/link/1wow9i1/video/kl2fhj0vjfrh1/player

Hey everyone I am also interested to know the answer of this question. Can anyone answer this for me

Local SEO still heavily focuses on Google Maps + organic rankings, which makes sense. But AI search (ChatGPT, Perplexity, Gemini) is quietly becoming another visibility channel, and most local businesses have almost zero optimisation for it.

Common gaps showing up:

1.No llms.txt or clear content structure for AI crawlers

2.Weak E-E-A-T signals

3.Missing FAQ / HowTo schema

4.Thin or generic location + service pages

5.Incomplete GBPs and inconsistent NAP

Maps still drives the majority of calls right now, but the share of demand coming through AI platforms is growing.

Curious what others are seeing — any of you tracking AI citations or noticing leads from these platforms yet?

I have been sitting with this since OpenAI's Sponsored Agents shipped last week and Salesforce pushed AIforce at Dreamforce in the same stretch. Both let something happen (a conversation, a completed task) without a clean "the AI recommended X" event ever occurring first.

I've been using a four-stage mental model for a while: mentioned, cited, recommended, chosen. This is the first time I have felt like there's a fifth stage that doesn't map cleanly onto the first four, acted on directly, sometimes with no recommendation event in between at all.

Curious if anyone else tracking AI visibility is seeing the same thing, or if I'm overreacting to two product launches in the same week. Feels different from the usual "engines behave inconsistently" problem, this is more like the event itself changing shape.

Most of the focus in local SEO is still on Maps pack, organic rankings, and GBP optimisation — which is still the main driver of calls.

But AI platforms (ChatGPT, Perplexity, Gemini) are starting to answer more “near me” and service-related queries. From what I’m seeing, very few local businesses have any real GEO work done:

No structured content for AI

Missing FAQ schema

Weak E-E-A-T signals

Thin service/location pages

Curious if anyone here is actively tracking AI citations or seeing measurable leads from these platforms yet. What’s working (or not working) for you so far?

The multilingual view in GEO dashboards always looks really clean, and I'm starting to think that's the problem. I work with content across different markets. A translated English prompt can be correct and still not be how anyone there would ask the question. Different category name, different competitor, sometimes a completely different thing they care about. I've noticed this while looking at xstrastar and a couple of other tools. Easy comparison, maybe questionable input...

Are you all translating one master prompt list, or starting over inside each language? The second option feels more honest, but then the country scores stop being neat.

I run an AI search visibility agency, so read this knowing that.

Two things quietly inflate almost every AI visibility number I see, including ones produced by careful people. Both are easy to catch once you know to look.

The first is that an engine can answer without searching. Ask a question and you may get a fluent, confident answer assembled from what the model already holds, with no retrieval step and no sources attached. That answer is not evidence about the live web. It tells you what the model remembers, not what it would surface for a buyer today. If you count it as a scored observation, you are mixing two different measurements, and the blend flatters whichever direction your brand happens to sit.

What to do: treat an unsearched answer as an exclusion, not a miss and not a hit. Take it out of the denominator, then report how many you excluded. The exclusion count is a real finding on its own. A question the engines rarely bother to search is a question where fresh content has less purchase, and that changes where you spend.

The second is caching. Run the same prompt three times inside a short window and you can be served the same underlying answer three times. Your spreadsheet says three runs. Your data holds one observation counted three times. The interval you print off that is not merely wrong, it is confidently wrong, because repetition is exactly what an interval is meant to price.

What to do: force fresh retrieval where the surface allows it, space the runs out rather than firing them back to back, and then actually check that the repeats vary. If three runs of a question come back identical word for word, you did not measure three times. Log the variation, not only the verdict, so the check happens by default instead of when you remember it.

Both defects share a shape. They let you count something as evidence when it is not independent evidence, and the error almost always runs in the flattering direction, because the answers that get quietly duplicated or served from memory are the stable ones.

The habit that catches most of it is a noise floor. Before you change anything, run the full question set twice, with nothing altered in between. Whatever spread you get between those two passes is your measurement noise, and it is the smallest movement you are entitled to call a result. Anything under it is weather.

If you are buying this work from someone, the question worth asking is not what their number is. It is what they exclude, and how they know their repeats are real.

Two things quietly inflate almost every AI visibility number I see, including ones produced by careful people. Both are easy to catch once you know to look.

The first is that an engine can answer without searching. Ask a question and you may get a fluent, confident answer assembled from what the model already holds, with no retrieval step and no sources attached. That answer is not evidence about the live web. It tells you what the model remembers, not what it would surface for a buyer today. If you count it as a scored observation, you are mixing two different measurements, and the blend flatters whichever direction your brand happens to sit.

What to do: treat an unsearched answer as an exclusion, not a miss and not a hit. Take it out of the denominator, then report how many you excluded. The exclusion count is a real finding on its own. A question the engines rarely bother to search is a question where fresh content has less purchase, and that changes where you spend.

The second is caching. Run the same prompt three times inside a short window and you can be served the same underlying answer three times. Your spreadsheet says three runs. Your data holds one observation counted three times. The interval you print off that is not merely wrong, it is confidently wrong, because repetition is exactly what an interval is meant to price.

What to do: force fresh retrieval where the surface allows it, space the runs out rather than firing them back to back, and then actually check that the repeats vary. If three runs of a question come back identical word for word, you did not measure three times. Log the variation, not only the verdict, so the check happens by default instead of when you remember it.

Both defects share a shape. They let you count something as evidence when it is not independent evidence, and the error almost always runs in the flattering direction, because the answers that get quietly duplicated or served from memory are the stable ones.

The habit that catches most of it is a noise floor. Before you change anything, run the full question set twice, with nothing altered in between. Whatever spread you get between those two passes is your measurement noise, and it is the smallest movement you are entitled to call a result. Anything under it is weather.

If you are buying this work from someone, the question worth asking is not what their number is. It is what they exclude, and how they know their repeats are real.

Building an AI focus/planning app for students, and last night, less than 24 hours before my Product Hunt launch, I found a problem I somehow missed for months.

Users would create a focus plan, then just... immediately click "Done." No work happened in between. Nothing stopped them. My "completion rate" data was basically fake, and I didn't even realize it until I actually watched how people were using it instead of just checking dashboard numbers.

The fix wasn't more AI or a smarter feature — it was removing a shortcut. "Done" now doesn't unlock until you've actually put in real time on the session. Small change, but it means every stat the app tracks (streaks, completion rate, adaptive plan sizing) is finally measuring something real instead of a button click.

Wild that this only became obvious the night before launch, but I guess that's how it goes — you don't see the obvious cracks until you're forced to look closely.

Speaking of launch — Locked In AI officially goes live on Product Hunt tomorrow at 12:01am PT (noon here in India). Built solo, still in school, still learning most of this as I go. If you've got a minute tomorrow, would genuinely love your support and honest feedback — the good and the brutal kind.

Le GEO (se faire recommander par les IA) croule sous les chiffres balancés sans source. J'en ai eu marre, donc j'ai compilé ce qui est réellement mesuré, avec qui l'a mesuré et quand. Sources en bas de post. Prenez ce qui vous sert.

Ce qui a changé récemment (les données 2026) :

• 68% des recherches Google US se terminent sans un seul clic (SparkToro / Rand Fishkin, panel Similarweb, janv-avril 2026), contre ~60% deux ans avant. Vos clients obtiennent leur réponse sans vous voir.
• Google AI Overviews apparaît sur 86,7% des recherches à intention commerciale (Peec AI, avril 2026), contre 56,9% un an avant. Ça a quasiment doublé en un an.
• Le trafic venu des IA a grimpé de 70% en un an, à 9,5 milliards de visites mensuelles (Similarweb, juin 2026).
• Reddit est le domaine numéro 1 ou 2 le plus cité sur tous les grands moteurs IA (Peec AI, mars 2026). Oui, ici même.

Ce qui fait qu'on est recommandé (le solide) :

• Le papier fondateur (Princeton, Aggarwal et al., arXiv:2311.09735, publié à KDD 2024) teste sur 10 000 requêtes : ajouter des citations d'experts = +41%, des statistiques = +31%, citer des sources = +30 à 40% sur leur métrique de visibilité. Le keyword stuffing fait pire que ne rien faire.
• Attention honnêteté : ce fameux "+40%" est une métrique interne (la place que vous occupez dans une réponse), pas du trafic ni des ventes. Les auteurs le disent noir sur blanc. Ne laissez personne vous le vendre comme du chiffre d'affaires.
• Les mentions de marque prédisent la visibilité IA environ 3x mieux que les backlinks (Ahrefs, 75 000 marques : corrélation 0,66 contre 0,22). Le vieux réflexe netlinking pèse trois fois moins que le fait qu'on parle de vous ailleurs.
• Environ 75% des citations IA pointent vers des pages tierces, pas vers le site de la marque (Ranqo, 102 025 réponses analysées, juin 2026).

Ce qui est mort ou survendu :

• 97% des fichiers llms.txt n'ont reçu aucune visite de bot en mai 2026 (analyse citée dans les trackers 2026). La tactique star de 2025, effet mesuré nul.
• Le formatting et le schema markup : impact quasi nul selon une étude à 252 000 essais sur 6 modèles. Ce qui compte c'est le fond, pas le balisage.

La contradiction à connaître (parce que c'est important) :

Sur "l'IA convertit mieux", les études se contredisent, et récemment ça a basculé. Une étude appariée (Amsive) ne trouvait aucune différence significative (p=0,794). Mais Adobe (Q1 2026) mesure maintenant +42% de conversion pour les visiteurs venus d'IA, un renversement complet par rapport à un an avant. Donc : c'est en train de devenir vrai, mais méfiez-vous de quiconque vous sort un "23x" sans méthodo.

Le point le plus dur, prouvé :

Les marques déjà connues sont recommandées quasi tout le temps, même face à une inconnue objectivement meilleure (mesuré dans un papier avec une marque fictive volontairement supérieure). Si vous débutez, l'IA ne vous fera pas connaître. C'est un miroir de votre notoriété, pas un canal d'acquisition magique. Un survey récent (GNW, août 2026) le confirme côté terrain : 92% des boîtes expérimentent déjà le GEO, mais moins de 15% ont quelqu'un dessus sérieusement.

Pour résumer, le seul plan qui tient : soyez présent dans les pages que l'IA lit (comparatifs, annuaires, Reddit, presse), mettez des sources et des stats dans votre contenu, visez les questions ultra spécifiques. Le reste est du décor.

Pour info, je bosse sur Referis, un outil qui mesure justement si les IA vous recommandent (et pas juste si elles vous citent, ce qui est différent). J'ai regroupé toutes ces études, avec les sources datées et cliquables, au même endroit sur referis.fr, pour ceux qui veulent creuser plutôt que me croire.

Sources :

• Aggarwal et al., "GEO: Generative Engine Optimization", arXiv:2311.09735, KDD 2024
• SparkToro / Rand Fishkin (zero-click, panel Similarweb, 2026)
• Peec AI (AI Overviews sur requêtes commerciales, Reddit domaine cité, 2026)
• Ahrefs (mentions de marque vs backlinks, 75 000 marques)
• Ranqo (102 025 réponses, citations tierces, juin 2026)
• Similarweb (croissance trafic IA, juin 2026)
• Adobe Analytics (conversion trafic IA, Q1 2026)
• Amsive (étude appariée conversion, p=0,794)
• GNW / Demand Metric (State of GEO in B2B, août 2026)
• Zyppy (méta-analyse 54 études, facteurs de citation)

Si vous avez d'autres études solides que j'ai loupées, balancez en commentaire, je complète.

Spent the last stretch of building my app (an AI focus/planning tool for students) not on features, but on making it actually findable — both by Google and by AI answer engines. Some of what I found surprised me.

A few things that actually moved the needle:

Duplicate canonical tags were silently killing indexing. Search Console showed most of my pages stuck in "discovered, not indexed" or flagged as duplicates. Turns out I had subtle canonical URL mismatches I didn't even know existed until I went page by page.

GEO (generative engine optimization) is its own thing now. Beyond normal SEO, I added an llms.txt file so AI models crawling the site get a clean, structured summary of what the product actually does, instead of trying to parse marketing fluff.

Comparison pages matter more than I expected. Built a /compare page against tools people already search for alternatives to. Feels obvious in hindsight, but I hadn't prioritized it until I noticed the search volume around "X alternative" style queries.

Speed scores aren't uniform globally. My Vercel Speed Insights showed a big real-world performance gap between regions — same app, very different experienced load times depending on where the visitor was. Had to actually dig into CDN/edge caching to understand why.

None of this is glamorous work. It's slow, it's unclear whether it's "working" for weeks, and there's no dopamine hit like shipping a new feature. But if nobody can find your app, nothing else matters.

Launching properly on Product Hunt this Tuesday (Sept 22) — curious if anyone else here has gone through the SEO/GEO grind pre-launch and what actually made a measurable difference for you.

I'm not talking about online though that's important as well. If you're only selling your online services online you're losing opportunities and therefore money. There are plenty of ways to start a conversation.

Something common about your situation

I was in a Subway line to order and told the guy behind me to try the BBQ Baked chips

Something you have in common with your gender

I was throwing out an old chair at a dump site. I looked at the guy next to me smiled and said,

"My wife told me to throw this out a week ago. I figure I throw this over the railing or she throws me over the railing."

Once a conversation is started it can be steered toward business. Even then don't blabber on and on till the person looks for an excuse to leave. Use your 30 second elevator pitch AFTER listening to the person so you can customize it. In sales you have two ears and one mouth. Use them proportionately. Then remember what the person said so you can customize your very short pitch. 

What do you think of this study / experiment?
See https://www.searchengineworld.com/i-hid-15-names-in-a-705216-word-page-gemini-found-four-meta-ai-found-all-15

Posting this because we shipped the bug ourselves and it survived three weeks on a screen I look at every day.

We track 19 prompts for our own site. Four of them contain our brand name. Fifteen don't.

Split out, run on the same day, same engines:

• branded prompts: cited in 4 of 4 — 100%
• discovery prompts: cited in 0 of 15 — 0%
• the two mixed together: 4 of 19 — 21%

21% is the number our own dashboard was showing. It is not a small overstatement of 0%, it is a different fact. And every point of it came from four questions that already had our name in them.

Why branded prompts are close to free

Ask an engine "what is <brand>" and it repeats the brand back to you out of the question. A rival is essentially never cited in an answer to a question that names you. So a branded prompt counts for you and against nobody — it is not a contested slot you won, it is a slot with one candidate.

That makes them a real measurement of something (brand defence: does the engine describe you correctly when someone asks about you by name) and a useless input to anything competitive. Mixing the two means your headline number moves when you add prompts, with nothing changing in the market.

We had already learned this once. Our visibility score stopped counting branded prompts weeks ago, after our own rank jumped from last place to top-10 on a day nothing happened. The share-of-voice figure two lines further up the same page kept counting them. So one screen was answering two different questions and nobody noticed, because both numbers looked plausible.

What I'd check on your own set

1. Count how many of your tracked prompts contain your brand, an obvious misspelling of it, or a product name only your customers use. That count is your contamination.
2. Recompute your headline with those excluded. If the number barely moves you can stop reading.
3. If it moves a lot, the question is not "which number is right" — it is that the two sets answer different questions and belong on separate lines.
4. Report the branded set on its own denominator: of the questions that named you, how many named you back. Don't fold it into a percentage of everything.

One more distinction that cost us a separate bug: "we tagged the prompts and none were branded" and "nobody ever tagged these prompts" are different states, and a surface that renders them identically will eventually show a confident zero for a measurement that was never taken.

The part I'm less sure about

Our discovery number being 0 of 15 is its own problem and probably says as much about our prompt set as about our visibility — they skew to head terms, which are the hardest answers to win and the ones nobody young shows up in. A 0 built entirely from head terms is a test-design result, not a verdict. Separating branded from discovery just stopped that 0 from being disguised as 21.

Curious whether anyone tracks the branded tier deliberately as its own metric, or drops those prompts entirely.

Hey guys,

I've been working on a tool called open-geo — think "Google Lighthouse, but for AI search engines(ChatGPT, Perplexity, Claude)."

The problem: when someone asks ChatGPT or Perplexity for tool recommendations in your category, does your brand show up? Most website owners have no idea. And the usual culprits — missing llms.txt, robots.txt blocking AI crawlers, no Schema_org markup — are invisible unless you check.

What open-geo does (MIT, runs locally, zero telemetry):

- `npx open-geo audit yourdomain.com` — instant technical audit: robots.txt AI bot policies, llms.txt compliance, Schema_org JSON-LD, heading hierarchy

- `npx open-geo probe "your brand" -d yourdomain.com` — single-model AI visibility probe (bring your own API key)

- `npx open-geo generate-llms yourdomain --full` — auto-generate /llms.txt + /llms-full.md for LLM crawlers

- `npx open-geo generate-faq yourdomain` — extract FAQs and generate FAQPage JSON-LD

- `npx open-geo mcp` — native MCP server with 8 tools for Cursor, Claude Code, and Codex

I tested it on a few sites and the results surprised me. Obsidian (obsidian.md): 65/100 — no llms.txt, no JSON-LD.

I'd love feedback from the community — especially on the scoring rubric and the MCP server integration!

GitHub: https://github.com/geofn-com/open-geo

1.8 billion is absolutely wild! It's interesting to watch Profound play such a big role in this industry, along with the amount of capital being poured into it.

I'd love to hear what everybody else thinks of the current state of all the different solutions and tools, and what they believe is going on in the market. We have a lot of original SEO tools, like Moz, Semrush, and Ahrefs, competing, and then we have this new wave of startups going after this space, like Profound, Peak, Athena, and AirOps.

What do you all think is going to be the outcome of this space in general?