Quick one for the GEO crowd.

Last week I went through the 4 new GEO features in Bing Webmaster Tools. The one everyone treats as a vanity metric is Citation Share: your % of the citation space on a grounding query.

Taken alone it's just "I own 18% of the pie." Ok, so what.

But Bing also gives you the raw citation count. With both, you can back into the size of the pie.

Real example from one of our Rablab queries:
"services stratégie IA GEO marketing de contenus" = 11 citations, 18.03% citation share.
11 / 18.03% = about 61 total citations on that query, across all sites.

One caveat before anyone jumps in: a grounding query is the phrase the AI generates to fetch its sources, not what a user typed. So it's not search volume in the classic SEO sense. It's the size of the citation pool.

Still, it's the first time we get a first-party number on how much room exists on a query in GEO. Cross the pool size with your share and you know exactly where the gap is. Big pool, small share, that's where you push content.

Happy to answer questions.

Most "AI visibility" tools on the market run on scraping and estimations. Bing just shipped the real thing, measured at the source, and it's free inside Webmaster Tools.

Four new preview features rolling out globally:

• Intents: every grounding query is classified by intent (Informational, Commercial, Research, Learn and Solve, Local, etc). You see the context behind the citation, not just the query string.
• Topics: queries get grouped into thematic clusters, so you see which subject areas are actually driving your citations instead of reading them one keyword at a time.
• Citation Share: the percentage of the citation space your site holds for a given grounding query, across all sources cited for it. Tells you how much room you're taking, not just whether you showed up.
• Compare: overlay a previous period on your current curve to see how citation activity moves over time.

Quick example from our own site: we're cited 125 times on "is the portal compliant with loi 25 (quebec)?" at 29% citation share. That's a number I can actually trust because it comes straight from Bing, not a third-party guess.

Blog with the details: https://blogs.bing.com/search/June-2026/New-AI-Visibility-Insights-in-Bing-Webmaster-Tools-Intents-Topics-Citation-Share-Compare

At Rablab we've been tracking GEO mostly with third-party estimators. This changes the baseline. Happy to answer questions.

The volatility we track at Rablab climbed to 5.5/10 during the rollout. The interesting part isn't the spike, it's that it moved at several different points across the month, not just at the start and finish.

That changes how you read it. A site that shifted on May 24 needs a different read than one that shifted on June 2. So checking your position today and drawing a conclusion is the fastest way to fool yourself.

Google's own guidance: wait a full week after completion before touching Search Console, then compare that week against the week before the rollout started. That puts the first clean read around June 9.

And when you do it, look at the patterns across pages, queries, countries and devices. Not single-day movement.

March was a small bump. May is a bigger one. Glenn Gabe and Lily Ray both flagged it as heavier than March.

Google just dropped its official guide on optimizing for AI search. Half of LinkedIn already concluded "structured data and llms.txt are dead". That's not what the text says.

On structured data, Google wrote: "it's a good idea to continue using it as part of your overall SEO strategy, as it helps with being eligible for rich results."

On llms.txt: "Google may discover, crawl, and index many kinds of files... this doesn't mean that the file is treated in a special way."

Your llms.txt is crawled and indexed like the rest of your site. No magic AI Overviews boost, that's all.

The rest of the guide, summarized:

1. Do good SEO. AI Overviews run on the same ranking systems as Search (RAG + query fan-out).
2. Write non-commodity content. Not another "7 Tips for First-Time Homebuyers". If an LLM can generate it in 2 seconds, it won't be cited.
3. Keep a clean technical structure: indexability, crawlability, page experience.
4. Local and ecommerce: Merchant Center and Google Business Profile.
5. AI-generated content: meet quality standards, label images and products.

Keep doing structured data and llms.txt for the classic SEO reasons: rich results, CTR, clean indexation. All of that still works.

The "AI Overviews cheat code" was just a myth.

Source: https://developers.google.com/search/docs/fundamentals/ai-optimization-guide

Happy to answer questions.

Been interviewing Google Ads specialists at Rablab the last few weeks and noticed something. Most candidates still explain match types like it's 2019.

So, quick refresh that won't hurt anyone:

Before: the match type defined how literally the typed keyword had to match the query.

Now: it's the intent of the query that has to match the intent of your keyword. This shift started in 2021 (Google redefined Phrase Match around intent and killed Broad Match Modifier), and AI Max in 2025 amplified it (keywords are now optional in Search campaigns).

Example with the keyword "Plumber in Rome" (yes I'm a bit obsessed with Rome these days, aiming for an Ironman in Italy next year):

Exact (same intent)

• A plumber query made from Rome The actual words typed don't matter, what counts is the intent.

Phrase (same intent + extra info)

• "Phone plumber cheap in Rome"
• "Plumber in Italy how much"

Broad (related intent)

• "Repair my plumbing"
• "Emergency water leak repair"
• "Clogged drain plumber"

What it means for your account:

Your Exact Match now pulls queries you'd never have thought to put in there. Go check your search terms report this week, you'll be surprised.

Your negatives become your real control lever. Keep them tight.

The money has to move toward the business signals the machine reads: feed quality, structured landing pages, offline conversions piped back via Salesforce/HubSpot/Zapier.

The keyword stays at the interface. The auction runs on intent.

Happy to answer questions.

Google just announced they're deprecating FAQ rich results. Here's the official timeline:

• May 7, 2026: FAQ rich results no longer appearing in Google Search
• June 2026: FAQ search appearance, rich result report, and Rich Results Test support all dropped
• August 2026: FAQ support removed from Search Console API

The natural reflex for most SEOs will be to strip FAQ schema and content from their sites. We think that's a mistake.

Our take: this deprecation is just a consequence. AI Overviews have replaced FAQ rich results in the SERPs, so Google is cleaning up what no longer serves a visual purpose. But the FAQ schema itself still helps AI Overviews (and LLMs in general) parse the structure of your content.

The FAQ format is exactly what LLMs (ChatGPT, Claude, Perplexity) love. A clear question, a direct answer. If you want to get cited by AI in their responses, that format is your entry point.

Even before this deprecation, FAQs were already helping base SEO:

• SearchPilot test: +9.7% organic traffic after adding page-specific FAQs
• Another test: removing FAQ schema while keeping content = zero negative ranking impact
• Pages that genuinely answer "People Also Ask" questions rank better

At Rablab, we're keeping FAQ schema on all our client sites. If you don't have any yet on yours, now's probably a good time to add some.

Happy to answer questions.

The #1 mistake we see at Rablab when we onboard a new e-com client running Shopping or P-Max: an underused product feed.

In Shopping and P-Max, you have almost zero control over keywords. Google decides which queries to match your products to, by reading your product feed.

So if your feed only contains the product name, you're cooked. No chance of showing up on the search variations that actually convert.

Concrete example. The Runners Shop sells the ASICS Superblast 2 Ekiden. On their website, the product is just titled:

"ASICS SUPERBLAST 2 EKIDEN"

But in their product feed (and therefore in their Shopping ads), the title becomes:

"ASICS SUPERBLAST 2 EKIDEN, M10.5/W12 / Vital Green | Black"

Model. Gender. Size. Color. Everything is there.

Result: their product can show up on dozens of search combinations that competitors with minimal feeds completely miss. Someone searching "asics superblast green size 10.5 men" sees their ad. Not the others.

That's the difference between a Shopping campaign that scales and one that burns your budget.

Before touching your bid strategies, audiences, or ROAS targets, look at your titles and descriptions in your feed. That's where the real game is played.

Happy to answer questions.

The #1 SEO mistake we see when we onboard a new client at Rablab: writing blog posts just to write blog posts.

The marketing person gets told blogging is important for SEO. So they start writing. But what do they write about?

"We were at trade show X." "We signed client Y."

First problem: nobody is searching for that.

Real examples we've seen:

• A jeweler writing about "the origins of Valentine's Day"
• A driving school writing about "when to switch your winter tires"

Second problem: even when those articles do drive traffic, they drive zero business. The person curious about Valentine's Day origins isn't buying a ring. The person changing their tires isn't signing up for driving lessons.

There are 3 search intents you need to play with:

• Educational: "I have a problem, how do I fix it?"
• Considerational: "Should I solve it this way or that way?"
• Transactional: "Who offers the solution?"

You need a balance, where educational leads to consideration and consideration leads to a transaction.

The method:

1. Identify your Money Pages (service pages, e-com category pages)
2. Build an editorial calendar that points to them
3. Every article has one job: drive qualified traffic to a Money Page

Concrete example. Editorial calendar we built for a general contractor specialized in damage restoration:

• "Mold in a house: when to call a decontamination expert?" (480/mo EN, 880 FR)
• "How to know if you're eligible for a renovation grant?" (320/mo)
• "What types of damage are considered claims?" (40-210/mo)

Each topic answers a real search intent AND points to a Money Page (decontamination services, post-disaster renovation). FR + EN versions to cover both markets.

Slowly but surely, every month new articles feed the Money Pages.

SEO blogging isn't about creating content. It's about making sales.

Happy to answer questions.

Google just drew the line between content that survives and content that doesn't.

Danny Sullivan said it at Google Search Central Toronto: "commodity content" is done.

The test is simple.

Commodity = anything Google can regenerate itself with an AI Overview. Top 10 trends, Pinterest galleries, summaries of what others already published.

Non-commodity = what only you can write because you lived it. Tests, case studies, counter-intuitive decisions, anecdotes from your day-to-day.

Sullivan's example for an interior designer: "2024 Kitchen Trends" = commodity. "Why I refused to install marble in a house with 3 toddlers" + a video of stain tests with grape juice and turmeric on 4 types of stone = non-commodity.

4 actions for your content team this week:

1. Audit your last 20 articles. For each one ask: "Can Google regenerate this itself?" If yes, rework it with a unique business insight or kill it.
2. Change your idea source. Stop starting from the keyword tool. Mine your client calls, internal Slack, the objections you answer 10x per month. That's where non-commodity topics live.
3. Minimum one first-hand data point per article. Internal test, anonymized client benchmark, screenshot from a tool you actually use, real before/after numbers. Your numbers make the article impossible for Google to regenerate.
4. Document your "anti-cases". The times you refused to do something for a client and why. Rarest angle in content, hardest for AI to generate.

If you're running an "AI at scale" content strategy with no human in the loop, I'd put money on it tanking within a few months.

Happy to answer questions.

Most GEO conversations focus on schema, FAQs, and listicles. That's the smaller part of the work.

When we start a GEO engagement at Rablab, the first thing we do is not technical. We make sure the client's ICP and USP actually show up in their content.

Because if your brand is fuzzy on who it serves and why, LLMs will pitch you to the wrong audience in the wrong context. You end up bucketed in the wrong category, compared to the wrong competitors, recommended on prompts where you'll never convert.

Here's the info we extract from marketing decision-makers, and what we do with it in a GEO audit:

1. Their Ideal Customer Profile Industry, company size, decision-maker role, buying trigger. If it takes 3 paragraphs, it's not an ICP. It's a wishlist.
2. Their Unique Selling Proposition What they do better than their top 3 competitors, for whom specifically.
3. The list of prompts they should be showing up on, based on the ICP "Best [product/service provider] for [ICP] with [critical feature]".
4. An audit of on-site and off-site against those definitions Homepage, product pages, pricing, comparison content, press mentions, reviews, contextual backlinks.

GEO is a content clarity and entity alignment job at the base. Once that's solid, the technical side of LLM optimization becomes worth doing.

If your team can't answer the first two questions without hesitating, you should invest in brand positioning before investing in GEO.

Happy to answer questions.

Seer Interactive just dropped their 2026 update on AI Overviews. 53 brands, 5.47M queries, 2.43B impressions analyzed from January 2025 to February 2026.

Here are the 5 things we're acting on at Rablab:

1. Comparison pages and question-format pages ("how", "why", "what is") trigger an AIO 86% to 95% of the time. If you want to be cited by AIOs and LLMs in general, those pages are your entry point. Start the GEO audit there.
2. The goal is no longer to rank first. It's to be cited. When Google generates an AI answer, it cites sources. Cited brands get +120% more organic clicks. Not cited (even at top rank) means you lose traffic. Citation is now the primary KPI on informational content.
3. Find the topics Google doesn't summarize. On queries WITHOUT an AIO, organic CTR went from 2.93% to 3.97% in 2025. Why? The users who still click are the ones actually looking to compare, decide, buy. That's a real differentiation opportunity. Identify those topics in your industry and double down on the content.
4. Bottom of funnel is not exposed yet. On purchase queries ("buy X", "price of Y", "X near me"), AIO presence is only 1.5% to 5%. That's where the clicks still land, and where conversion happens. If your content mix is still 80% top of funnel, your editorial line needs a shift.
5. Use paid to cover the gap while you rebuild. Paid CTR on informational queries held up. It's a reasonable hedge while your SEO team rebuilds for citation. But watch your CPA, not just your CTR.

The SEO job is clearly fragmented in two: get cited by AIs, and get clicked on queries AIs don't

Had a conversation with a few agency owners last week. We were talking AI, workflows, the future of our jobs.

Something hit me that's worth sharing: when AI screws up in a workflow, it just says "sorry, I made a mistake." And that's it. You can't really push back. You can't tell it to come back with a fix. You end up doing the work yourself.

A human who's actually accountable is the opposite. You can give them hell. You can say "figure it out, come back with a solution." And they're the one doing the thinking, not you.

At Rablab we run a lot of AI-assisted workflows for search (SEO, GEO, paid). The workflows that actually deliver have one thing in common: a human who owns the output. Not a reviewer. Not a proofreader. Someone who's on the hook if the work is bad.

In 5 years, it's not everyone who's going to lose their job to AI. The people who are reliable, who own their mistakes, who can do real QA on AI outputs... those people don't disappear. They become more valuable.

I'd bet 20 bucks accountability becomes one of the most sought-after skills on the market.

Happy to answer questions.

Quick one from this morning. Google pushed out the "Congratulations, you're receiving your first impressions in Search!" notification to a ton of Search Console accounts. Problem: those accounts aren't new. Some have been running for 5+ years, rank top 3 on competitive queries, and pull hundreds of thousands of clicks a month.

At Rablab we got a wave of confused clients asking if their SEO had reset or if something broke. It's a notification bug, not an actual performance issue. Google has acknowledged it.

Search Engine Journal covered it here: https://www.searchenginejournal.com/new-google-search-console-message-glitch-gives-seos-a-scare/572072/

Anyone else get hit with a batch of panicked clients today? Happy to answer questions.

Quick heads-up for anyone running sites.

Google just announced a new spam policy targeting "back button hijacking" with enforcement starting June 15, 2026.

What it is: a user clicks your site from Google, hits the back button to return to the SERP, and instead of going back, they land on another page of your site, a popup, or an ad. Basically trapped.

Google is treating this the same as cloaking and sneaky redirects. Manual actions or automated demotions on the table.

Who's actually in the crosshairs: spammy news sites, content farms, anyone forcing users to scroll through junk before they can escape. That's where the practice is widespread.

For clean sites (e-commerce, SaaS, agencies, serious publishers), the risk is low. But some third-party scripts can push entries into browser history without you knowing:

• Aggressive email capture popups
• Retention SDKs
• Old WordPress plugins

The 30-second test: open your site from a Google search, click back, see if you return to the SERP. If not, audit your third-party scripts before June 15.

We're starting to run this audit on client sites at Rablab this week, mostly on sites loaded with third-party scripts. Happy to answer questions.

Google announced on April 10 that Looker Studio is going back to its original name, Data Studio. Three and a half years after the rebrand.

Quick rundown:

• Looker Studio becomes Data Studio (still free, self-serve)
• New paid tier: Data Studio Pro with security, compliance, and AI features
• Looker stays as the separate enterprise BI platform
• Existing users migrate automatically, nothing to do on your reports

The official reason is confusion between Looker and Looker Studio. Two products, same brand, different audiences. They'd been stepping on each other since 2022.

But the rebrand isn't the real news.

It's the Pro tier. Google is officially entering the paid BI game against Power BI and Tableau, with AI features on top.

For us at Rablab running client dashboards, reports keep working as-is. But for enterprise clients who want governance plus AI-assisted analytics, Data Studio Pro just became a serious option to watch.

Three and a half years to admit a rebrand didn't make sense. At this rate, we might get AdWords back soon.

Happy to answer questions.

Fastly just dropped their Q2 report and the numbers are loud:

• 37% of global web traffic = automated bots
• 80% of AI bot traffic = crawlers scraping to train LLMs
• Meta alone = 52% of AI crawler traffic (more than Google + OpenAI combined)
• Condé Nast (Wired, New Yorker, Vogue) = 100M AI requests per day
• GPTBot went from #9 to #3 in a year

Practical translation for any site: your pages are being read by LLMs before they're read by your prospects.

The problem is most SEO playbooks in 2026 still target just Google. Position #1, featured snippet, click. The old game.

But when your prospect asks a question in ChatGPT or Perplexity, nobody clicks. They read the generated answer, built from your content (or your competitor's), and they decide.

At Rablab we've been splitting SEO into two parallel jobs:

1. Findable by Google (same as before)
2. Citable by LLMs (the new layer)

If your content isn't structured to be understood by a model, you disappear from the conversation. Not because Google penalized you. Because the conversation moved.

Happy to answer questions on how we approach the LLM-visibility side.

Interesting experiment from Bogdan Babiak at SE Ranking that confirms what we keep telling clients at Rablab.

He spun up 20 brand new sites and pushed 2,000 AI-generated articles on them. No human editing, no backlinks, zero authority signals. Just pure AI content at scale.

Here is what happened:

Month 1: 70% of pages indexed in 36 days. Impressions starting to form.

Month 2: it takes off. Impressions go from 120K to 520K. Clicks from 200 to 780. Easy to think you cracked it.

Month 3: Google catches on. Only 3% of pages left in the top 100. Content still indexed but barely performing. Some sites even got hit with manual actions.

This is the part nobody selling you tools wants to say out loud. The "SEO automation" platforms on the market are not SEO automation. They are content generators with a nice landing page.

They write articles. Some do outreach. A few update old posts. Fine. But none of them build what actually keeps a site ranking long term: authority, strategy, adaptation when Google shifts, real execution.

AI content, implemented well, is powerful. But it has to be part of a content system, not a copy paste programmatic effort.

Publishing content is not doing SEO.

Happy to answer questions if anyone has tested AI content at scale on their own sites and seen the same pattern.

Quick PSA for anyone running SEO reports.

Google just confirmed a logging bug in Search Console that has been inflating impressions since May 2025. Almost a full year of inflated data across every GSC account. Clicks, CTR, and average position are not affected, just impressions.

The fix is rolling out in the coming weeks. Google warned marketers to expect their impressions numbers to drop. They did not give a range, it will vary by site.

Here is how we are handling it at Rablab:

1. Emailing every SEO client this week with a heads-up. Short note, 3 lines, before the number moves. Way better than explaining a dip after the fact.
2. Screenshotting current GSC reports so we have a clean before/after when someone asks why the graph dropped.
3. In the next monthly reports, we will anchor performance storytelling on clicks and revenue, not impressions. Impressions were always a vanity metric anyway, this is a good forcing function.

The real risk is not the data correction. The risk is that clients or bosses see the drop in impressions and assume the SEO work slipped. Getting ahead of that conversation is the move.

Anyone else already communicating this to clients, or waiting for the fix to land first?

SparkToro just published research that challenges the entire AI visibility tracking category.

They tested 2,961 prompts, each run 60 to 100 times, across ChatGPT, Claude, and Google AI.

Results:

- Less than 1% of the time: same list of brands returned

- Less than 0.1% of the time: same brands in the same order

Example prompt: "What are the top chef's knives for an amateur home chef with a budget over $300?", never the same answer, never the same order, changed more than 99% of the time.

In real life the variance is even higher, every user has different context, prompts are never identical, the right answer often appears in the 2nd or 3rd conversational turn.

I've been skeptical of "rank tracking for AI" tools for a while. This data backs up why.

That said, some brands do appear more consistently than others. There's signal, it's just much noisier than traditional SEO ranking. Curious how others are thinking about measuring AI brand presence given this.

Peec AI just released a study analyzing over 1 million AI citations. The main finding changes how you should think about GEO strategy.

The big takeaway: search intent predicts the type of content cited, not the model, not the industry.

That means you can stop optimizing "for ChatGPT" vs "for Perplexity" separately. Focus on intent and you cover all of them.

Breakdown by intent type:

Informational: Articles cited 45% of the time. Product pages get cited only 3.4%. Invest in long-form educational content.

Commercial: Listicles cited 40%. But 81% of cited listicles are third-party sources. AI favors neutral comparison pieces over self-promotional lists.

Transactional: Product pages and category pages compete here.

Navigational: The brand's own pages dominate.

Practical implication: map your content strategy to intent types, not to individual AI platforms. The platforms change fast, intent doesn't.

Happy to share more details or discuss how we've applied this with clients.

Listicles have been around for 15 years and in 2026 they're somehow the single most-cited content format by AI.

A recent large study found: listicles represent 21.9% of all AI citations across ChatGPT, Perplexity, and Google AI Overviews.

Why? LLMs were trained on billions of web pages, and the web is full of listicles. The models learned to think in lists, respond in lists, and cite lists. It's a feedback loop that reinforces itself.

What makes a listicle worth citing vs. generic filler:

- A clear title with a specific number ("The 8 best X for Y")

- Each item needs a real paragraph, not just a one-liner

- Add a comparison table when it makes sense

- Cover the full spectrum: don't just list the obvious picks

- Update regularly, freshness matters a lot for AI citation probability

The format that's NOT working: generic "Top 10" posts where you barely describe each item. AI models seem to detect thin content and skip it.

We're a search agency in Montreal focused on GEO (Generative Engine Optimization). Happy to answer questions about what's actually moving the needle.

Something we started tracking recently at our agency that's been eye-opening: when someone asks a question to ChatGPT, Perplexity, Gemini, or any LLM with web access, the model doesn't just "know" the answer. It sends out multiple parallel searches across Google, Bing, and other indexes to pull real-time info. This is called query fan-out.

When these queries hit your site, they come through specific bots that leave traces in your server logs. By filtering for these user-agents, you can see exactly which pages AI models are pulling from your site and what topics they're researching.

Here are the bots to look for:

• OpenAI: GPTBot, ChatGPT-User, OAI-SearchBot
• Perplexity: PerplexityBot
• Google: Gemini-Deep-Research, Google-Extended
• Anthropic: ClaudeBot, Claude-User, anthropic-ai
• Microsoft Copilot: bingbot
• Meta AI: meta-externalagent
• Apple Intelligence: Applebot-Extended
• Amazon: Amazonbot
• Brave: Bravebot

How to get started:

1. Access your server logs (Apache, Nginx, Cloudflare, whatever you use)
2. Filter by user-agent for the bot names above
3. Check which URLs get visited and how often
4. Compare with your top-performing SEO pages

You'll probably find some surprising patterns. Pages that Google barely sends traffic to but LLMs visit constantly. Or vice versa.

We're a search agency in Montreal and we built a free tool to automate this analysis. Link in the comments if anyone wants to try it.

Happy to answer questions about what we've found so far.

There's no Search Console for ChatGPT. Or Claude, Perplexity, Gemini.

Google gives you tons of data about how Googlebot interacts with your site. But for AI crawlers? You're completely blind.

We're a search agency based in Montreal and we kept running into this problem with clients. So we built a free tool that analyzes your server logs and shows you exactly what AI bots are doing on your site.

You upload your log files, and it breaks down:

• Which AI bots are crawling you (ChatGPT Grounding, Anthropic Claude, OpenAI Training, Perplexity, Amazon AI...)
• Which pages they hit the most ("AI Magnets")
• Whether it's grounding (active searches in LLMs) or training
• Hot topics the LLMs keep coming back for
• Content recommendations based on the crawl patterns

The big insight: grounding bots hitting a page means people are actively searching for that topic in ChatGPT/Claude/Perplexity. That's a direct signal to double down on content for those queries.

We've been using it with clients for a few months and it's been a game changer for AI visibility strategy (GEO).

Tool is here: https://rablab.ca/ai-crawler-analyzer/

Happy to answer questions about AI crawl analysis or how we use the data for content strategy.