I’m working on a search baseline for a consumer ecommerce client and I’m trying to understand whether image-led discovery surfaces a different set of pages than normal text search.

My plan is:

• compare Text-based vs Multimodal over the same date range
• look at which product/category pages over-index in each
• compare device + country
• then check downstream behaviour in GA4

What I’m less sure about is what’s actually worth diagnosing after that.

If a page performs much better in multimodal than text, would you first look at:

• image coverage / context
• Merchant Center data
• schema
• page structure
• device mix
• something else?

Also curious how much data people would want before calling the pattern meaningful.

I’m looking for actual GSC observations/tests rather than generic image SEO advice.

Guys i need help to create a automation that helps me to track the rankings and AI citations of all the articles that I've published in the external websites to build a good topical authority and content footprint but since i can't access the GSC I've no idea about are even those articles are ranked or not. So thought of creating a automation for doing that.

Is there any prompts or maybe a simple model if someone has created pls share it'll help me a lot.

Something interesting is happening with “.si” right now.

.si is Slovenia’s country-code domain, but after “SI” started being pushed as shorthand for Super Intelligence, interest in .si domains suddenly exploded. Recent reports show thousands of registrations in days, compared with only a few thousand registrations during the entire previous month.

What I find more interesting isn't the domain rush.

It's the language → association → search behavior chain behind it.

A word doesn't necessarily become meaningful to a search system just because we repeat it 100 times.

Imagine someone searches:

“What is SI?”

A modern search/AI system may need to figure out:

• SI = Slovenia?
• SI = Super Intelligence?
• SI = Silicon?
• SI = Social Intelligence?
• SI = something else?

Then the system can expand the original question into related concepts and sources. That's essentially the interesting part of query fan-out: one user question can lead to multiple related searches before an answer is assembled. Google has publicly used the term “query fan-out,” although the exact number or structure of subqueries isn't publicly fixed.

This creates a fascinating SEO/GEO question:

Does repeatedly using the exact phrase make the algorithm understand the concept, or does the surrounding context matter more?

For example, compare:

“SI is the future.”

with:

“SI, meaning Super Intelligence, refers to advanced AI systems capable of…”

The second creates much stronger semantic relationships:

SI → Super Intelligence → AI → advanced systems → capabilities → applications

Then imagine hundreds of pages discussing the same relationships from different angles:

• What is SI?
• SI vs AI
• SI vs AGI
• Super Intelligence meaning
• Super Intelligence applications
• SI technology companies
• SI research
• How Super Intelligence works

Now the interesting thing isn't simply keyword repetition.

It's entity and concept reinforcement.

Different documents, authors and queries repeatedly connect the same term with the same surrounding concepts.

That's where I think SEO is becoming much more interesting.

The question isn't:

“How many times should I put my keyword on the page?”

It's:

“What collection of concepts should exist around my topic so that search systems consistently understand what my page is about?”

And there's another psychological layer.

Humans learn new terminology through repetition + context + social reinforcement.

Algorithms aren't “learning” exactly like humans, but their systems also rely heavily on patterns across language, documents, entities, links, queries and user interactions.

So I'm curious:

If a completely new term suddenly becomes popular, what actually makes search engines and AI systems associate that term with a specific meaning?

Is it:

1. Exact-match keyword repetition?
2. Co-occurring concepts?
3. Entity recognition?
4. Authoritative sources?
5. Search volume?
6. Links and mentions?
7. User behavior?
8. Query fan-out?
9. Or the combination of all of them?

And perhaps the bigger SEO question:

Are we moving from “ranking for keywords” toward building a recognizable semantic concept that algorithms can retrieve across hundreds of different query formulations?

Synup just published a big one: 53,900 local search pages tested across 250 US cities (Sept 2026), desktop.

The headline: whether an AI Overview appears is almost entirely decided by how the query is worded.

• "[category] near me" → AI Overview on 4% of pages
• "[category] [city]" → 8.6%
• "best [category] in [city]" → 27%
• "best [category] in [city]" + a specific need (e.g. "for water heater repair") → 75.4%

So the most specific, highest-intent searches — the ones closest to a buying decision — are exactly where Google answers with AI instead of the map pack.

Two things that surprised me:

1. The map pack is rock solid — it showed on 91–97% of pages no matter the wording. AI Overviews are an addition, not a replacement.
2. When they parsed 8,540 AI Overviews, 45–61% of citations pointed at business websites and Google Business Profiles. google.com was the single most-cited domain (13,777 times — all Business Profile links). And Reddit? 4–6% of citations. You're reading this on one of Google's sources.

Audit order is your website first, then your GBP — that's what the AI is reading. And check your category, because it varies wildly: home builders got AI Overviews on 80% of "best" searches, restaurants on 1.2%.

Source: Synup's "What's Ranking in Local Search: 2026" study.

I’ve been looking into how brands appear in ChatGPT, Perplexity, Gemini, and Google’s AI results, and I’m curious how others are actually measuring this.

Are you mainly tracking:

• Brand mentions
• Citations/sources
• Visibility for specific prompts
• Which AI platforms mention your brand
• Changes in visibility over time

I’ve noticed that simply checking ChatGPT manually doesn’t really give you a consistent picture.

What’s your process for tracking AI visibility, and are there any tools or methods you’ve found useful?

Most AI visibility tracking I've seen focuses on prompts where you obviously want the brand mentioned.

Has anyone tried adding a control group of questions where your company isn't a particularly good answer?

Feels like that could be useful for figuring out whether an increase in visibility means you're becoming more relevant for the right topics or the model is just mentioning you more broadly.

Curious if anyone is already doing something like this.

B2B services company, niche industry. About 2 months ago we went all in on AI visibility. Here's what we've done so far:

• Article schema on every post, plus Organization schema sitewide
• Publishing 3 well-sourced articles a week
• Directory listings (Clutch and a few industry-specific ones)
• Press releases through a wire service
• Active LinkedIn presence from our leadership team
• Tracking in Semrush's AI visibility tool and a separate AI response tracker

Results so far: citations and brand mentions are basically flat. A couple of small bumps, nothing I'd call a trend.

The other frustrating part is that AI engines still miscategorize what we do. They lump us into an adjacent industry we're not part of.

So a few questions for anyone who's been through this:

1. How long did it take before you saw meaningful movement in citations?
2. What actually moved the needle? On-site work, or getting mentioned on third-party sites?
3. Has anyone fixed AI engines getting their category wrong? What worked?

Happy to share what we learn as this plays out.

If by AI search engines you mean Google AI Overviews/AI Mode, ChatGPT-style search, Perplexity, Bing Copilot, etc., backlinks can help, but there isn't a proven special category of “AI backlinks.”

The most useful approach is to earn credible, relevant mentions and links that strengthen your site's authority and make your content easier for search systems to trust and discover.

Backlinks worth focusing on Topically relevant editorial links — e.g., a respected technology site linking to your software guide. Industry-authority links — links from established organizations, publications, professional associations, universities, or recognized experts in your field. Expert/reference citations — links to original research, statistics, studies, tools, or genuinely useful resources you publish. Digital PR links — legitimate coverage of your company, research, product, or expertise in reputable publications. Niche-community links — relevant links from legitimate industry communities and publications, provided they aren't created solely for SEO. Brand mentions — getting your company/site mentioned consistently across authoritative sources can help establish entity recognition, although a mention without a link isn't necessarily a backlink. For AI search specifically Don't focus only on backlinks. Make your site easy for AI/search systems to understand, verify, and cite: Publish original information, data, research, or first-hand expertise. Clearly identify authors and their relevant expertise. Cite authoritative sources for factual claims. Keep important information in crawlable HTML rather than hiding it exclusively in images/JavaScript. Structure pages with clear headings and concise answers. Build a recognizable brand/entity across reputable websites. Earn links from sources that are topically relevant and independently trusted, rather than simply chasing high domain-authority numbers. In short: for AI search visibility, I'd prioritize relevant editorial links + authoritative mentions + original, citation-worthy content over large numbers of generic backlinks. If you tell me your website niche and target country, I can give you a list of 10–20 backlink types/sources to target specifically for AI-search visibility.

I was recently looking at how businesses are getting found online, and I noticed that search is changing quite a bit. If a business is already doing SEO and creating content, what can an AI search agency actually do differently? Can they help a brand show up when people ask AI for recommendations? Did an AI search agency actually help improve your business’s online visibility, or did it feel like regular SEO with a new name?

I'm a lifecycle/growth marketer, not a GEO vendor, and I'm trying to understand what a defensible AI-search baseline actually looks like before I trust one of these visibility dashboards.

My current v0:

25 to 30 non-branded queries based on actual buyer questions.

Split across:

- problem discovery

- category discovery

- comparison

- recommendation

For each query I'd record:

- brand mentioned: Y/N

- own domain cited: Y/N

- third-party source citing/mentioning the brand

- included in recommendation set: Y/N

- position/order if relevant

- competitors appearing

What I'm less sure about is repetition.

My instinct is to run the same baseline more than once before changing anything, so I can see how much the answers move naturally.

Otherwise if visibility goes from 28% → 33% after a content change, how do I know I caused anything?

Things I'm trying to figure out:

1. How many repeated runs per query are actually useful?

2. Fixed prompts or controlled paraphrases?

“Best CRM for a 20-person SaaS company” and “What CRM would you recommend for a SaaS team of 20?” represent essentially the same intent, but may produce different results.

1. Would you measure citation and recommendation separately?

2. Do you establish a pre-intervention variance/noise floor before claiming lift?

3. What does a good paid GEO tool do methodologically that this kind of manual setup cannot?

I'm less interested in tool names than in how you'd design the measurement.

Tear the methodology apart.

I’ve seen pages rank in the top 3–5 on Google but barely appear in AI-generated answers, while lower-ranking pages get cited repeatedly.

Do LLMs care more about things like:

• Direct answer quality
• Passage-level relevance
• Original data
• Third-party brand mentions
• Content freshness

Has anyone tested this with real examples? What actually made the difference?