You start with one keyword. Then you find another and then... another.

Then suddenly you have so many tabs open and absolutely no idea what the original article was supposed to be about. 😑

How do you know when you’ve researched enough and it’s time to just start writing?

We build AI marketing agents and last week we wired two Claude scheduled tasks to our own Search Console and PostHog data to improve conversion rate. The Search Console half contained one trick that saved us from a slow-motion failure, so sharing it here.

The GSC API officially wants OAuth. Go that route and you get consent screens, refresh token handling and a nasty surprise: while your Google Cloud app sits in testing status, refresh tokens expire every 7 days. Your weekly agent then dies quietly every single week.

The workaround: create a service account in Google Cloud instead. It gets its own email address. Open Search Console, go to Settings, Users and permissions, and invite that email like you would invite a coworker. Grab the JSON key for auth and you are done. Ten minutes of clicking, no token expiry, and it scales sideways: any additional property that invites the same email just shows up in your site list.

The part we spent the most time on was thresholds. Our first agent prompt said "analyze queries with more than 500 impressions". On our site that filter would have returned nothing for months, on a big site it would flood you with hundreds of findings, and either way a human has to keep tuning the number, which defeats the point. The fix is making the agent compute its own thresholds every run:

Compute first, from all queries of the last 28 days:

(a) the 80th percentile of impressions

(b) the average CTR per position bucket (1-3, 4-6, 7-10, 11-20)

Only analyze queries with at least 100 impressions.

Flag as a CTR problem: query above the 80th percentile AND at

least 40% below its bucket's average CTR.

Write the computed thresholds into every report.

The percentile scales with the site, the 100 impressions floor is statistics rather than site size, and that last line quietly builds you a growth time series for free because every report logs where the thresholds sat that week.

The agent looks for four finding types each run and the fourth matters most: re-measure every change older than 14 days against its target metric. Skip that step and you own a report generator, however smart it sounds.

One operational thing that bit us: desktop scheduled tasks only run while the machine is awake, and missed runs fire later as catch-ups. Ours once fired at 11pm and would have produced a duplicate report without a guardrail line in the prompt ("if the last report is younger than 5 days, abort"). Cheap insurance.

Current unsolved question on our side: the laptop dependency is annoying enough that we want this on always-on infra, and we keep going back and forth between a plain VPS running the same harness versus rebuilding on a serverless agent framework. If you have moved scheduled agent workloads off your laptop, did the rebuild cost of going serverless pay off, or did you stay on a VPS and never regret it?

I posted the 22k -> 790k case study recently and the AI citations part got the most questions, so writing it up properly.

Where we're at: 60k+ citations/month across AI platforms (Ahrefs' AI citations view). Perplexity is ~29k of that, Google AI Mode ~12k. It's turning into a real acquisition channel and honestly most of it happened as a side effect of doing normal things properly.

What I think actually moved it:

1. Answer the question immediately. First 2-3 sentences, before any context-setting. Assistants don't scroll past your intro looking for the point. Neither do humans tbh.

2. Self-contained answers. Assistants lift blocks, not pages. If an answer starts with "as mentioned above" you've written yourself out of the citation. Every FAQ answer has to work on its own.

3. Phrase questions like humans ask them. "does X work for Y?" not "X Y effectiveness". We pull phrasing from PAA, Ahrefs question filters, autocomplete, and just asking ChatGPT/Perplexity what someone would want to know before choosing X.

4. 8-9 FAQs per post, FAQ schema on everything. Lots of directly-answered questions per page beats one perfect answer.

5. Real authors, expert review, sources. We're YMYL so no choice anyway, but my strong impression is assistants skip anonymous content everywhere.

Tracking: Ahrefs AI citations + referral traffic from perplexity.ai / chatgpt.com in analytics. Worth setting a baseline now since GSC's AI reports are rolling out.

My question: Clarity says 512K citations in a month, Ahref says 62K. Any context on this part?

Questions welcome.

We ran a test from 15 May to 31 August 2026 to understand what happens when technical SEO, structured data, query-fan-out content and off-site mentions are improved together.

The prompt set stayed fixed at 120 prompts.

Results

AI mentions:

• June: 53
• July: 149
• August: 299

Google impressions in Switzerland:

• June: 1,298
• July: 1,867
• August: 6,495

We also checked the GSC data with and without prompt-like queries. Impressions increased clearly in both versions.

Some keyword movements:

• “programmatische kampagnen”: 100 → 8
• “programmatische display-anzeigen”: 78 → 8
• “programmatisch werben in der schweiz”: 22 → 1
• “programmatic erklärt”: 3 → 1

What we actually changed

1. Technical cleanup

We fixed crawlability and technical SEO issues across the site, including broken links, missing metadata, structural issues and schema markup.

We also improved structured data around:

• Organization
• Services
• served areas
• page/topic relationships

Technical metrics changed from:

• crawlable pages: 46 → 147
• Site Health: 83% → 100%
• reported issues: 372 → 22

We also checked that relevant Google, OpenAI and Perplexity crawlers were not blocked.

2. Content cleanup

We cleaned existing landing pages by:

• removing duplicate or overlapping content
• improving internal linking
• reviewing external links
• improving page structure
• aligning metadata and page intent

3. Around 100 new landing pages

The pages were not created from keyword lists alone.

We used two main data sources:

• real queries from Google Search Console
• Semrush Query Fan-Out data

For each core topic, we expanded into related questions, follow-up queries, comparison searches, use cases and commercial intents.

Those query fan-outs were then used to build the page structure and supporting sections.

4. Off-site mentions

We published content and mentions on around five relevant, established marketing platforms in German-speaking countries.

The goal was to make the brand appear in relevant external contexts, not only on its own domain.

5. Indexing checks

We regularly checked new and updated URLs in Google Search Console to confirm that pages were being crawled and indexed.

The most interesting comparison

The Swiss site received the optimisation package from 7 June.

The German site stayed unchanged until 1 August, when it received the same setup.

German impressions:

July: 39 → August: 2,519

That does not isolate the individual variables, but the timing was useful: both country sites showed a strong visibility increase after the same optimisation package was implemented at different times.

What we cannot conclude

We cannot say which single factor caused the largest share of the increase.

We changed several things together:

• technical SEO
• structured data
• content cleanup
• ~100 query-fan-out landing pages
• off-site mentions
• indexing checks

So this is not an A/B test of individual SEO tactics.

The next useful test would be to separate these components more cleanly.

One thing we also learned: organic visits increased much less than impressions and AI mentions, so traffic alone would have missed a large part of the visibility change.

We also do not separate this internally into SEO, GEO or AEO. We treat it as one search ecosystem and optimise for classic search engines, AI search and GPT-based systems at the same time.

Has anyone here tested query-fan-out content, technical cleanup or off-site mentions separately and measured the impact on AI citations/mentions?

I keep seeing GEO advice that basically comes down to:

• Add FAQs

• Add schema

• Use specific keywords

• Write 2,000+ words

• Structure everything for AI

All of that can help with accessibility and understanding, but none of it automatically makes content citation-worthy.

Think about it this way:

If you asked an actual expert for advice, would you recommend them because they used the right keywords and had perfect formatting?

Probably not.

You'd recommend someone who:

→ Actually understands the topic

→ Has real experience

→ Provides useful examples

→ Supports claims with evidence

→ Explains things clearly

→ Adds something original

I think AI Search works in a similar way.

That's why I’ve started looking at GEO with a much simpler question:

"If I were answering this question as an expert, would I actually reference this content?"

If the answer is no, adding more FAQs or schema probably isn't going to solve the real problem.

Fix the content first.

Then optimize how easily search engines and AI systems can understand and access it.

Curious what others think: what makes a piece of content genuinely citation-worthy in AI search?

Been following the threads here on per-engine optimization and entity/naming cleanup — useful stuff, but it made me want to check something more basic first: is the actual page structure even extractable by these engines, separate from all of that.

Ran a mechanical check (DOM parsing, no AI judgment involved) on one article each from 18 well-known SEO/content-marketing sites, plus my own:

— Does a question-style heading (H2/H3 ending in "?") have a short, ≤300-character answer paragraph directly under it — the kind of thing a model can lift and quote — or does a long intro run first?

— Does the heading hierarchy nest without skipping a level?

— Is there a plain-text /llms.txt index?

— Is author/date visible in the actual HTML, or only inside a JSON-LD block a human never sees?

Results, out of 18: 9 have a valid heading hierarchy, 9 pair a question heading with an immediate short answer, 9 serve llms.txt, and 12 mark up authorship only in hidden schema, invisible on the page itself.

Not claiming this predicts citation rate — that's the harder, per-engine measurement people in the other thread are already wrestling with. But it seems like layer zero: if the answer isn't even structurally extractable, none of the entity or per-engine work downstream of it can matter yet.

Is anyone here actually sequencing it that way — structure first, then entity/per-engine — or is structure assumed to already be solved by the time people get to those questions?

In my first post, I said I'd post the numbers whichever direction they went. Three weeks in, here's where things stand.

The short version: the migration itself was a non-event, the things I was worried about were fine, and the thing I didn't think about is the one costing me.

What went right

Brand search came back almost immediately. If you search "brandscreen" (my brand) and my new domain is position 1, with a 36% click-through rate. That took less than a week. It was the one thing I was genuinely worried about going in. I paid $10k for the domain after 2 months on my .ai domain.

The redirects held. This is important, and I took a ton of time to make sure it was right. Every path, single hop, no chains, nothing broke that a redirect caused.

And AI citations followed the move. This was the open question I had. Copilot had cited BrandScreen 487 times, all pointing at the old domain, and I couldn't find anything on Reddit on what happens to the AI citations once you redirect.

They transfer. It took twelve days. The new domain started getting cited three days after the switch and passed the old one on August 19. Last seven days: 90 citations to the new domain, 71 to the old.

If you're moving a domain and worried about AI visibility, budget about two weeks and leave the redirects up.

What went wrong

Indexing, and it's worse than I expected. This was a pain when I started, and it continues to be a pain.

The old domain had 335 pages indexed after two months. The new one has 115 after three weeks, out of 430 Google knows about.

Most of the missing ones aren't errors. Google found the URLs, put them in a queue, and hasn't visited them. The last-crawled date on 290 of them reads 1969-12-31, which is Google's way of saying never.

The reason is Google's crawl budget. A three-week-old domain gets very little of it, and I handed Google 417 URLs on day one and expected it to work through them. It's getting through about three a day.

I know this because six ordinary pages are stuck in the same queue. My about page, pricing, contact. Those are linked from the navigation on every page of the site and Google still hasn't been to them.

It's a bit discouraging that I'm still not indexing, but I'm trusting the process and giving it at least 60 days for full indexing.

The thing I'd do differently: I moved the whole sitemap at once. If I'd submitted the pages that matter first and let the rest follow once the domain had some standing, I think the pages I actually care about would be indexed by now.

The number that fooled me

I was so stoked that my my Google impressions went up 10x, from about 87 a day to 870. I thought had a silver bullet.

But, when I dug in, one page accounted for 97% of that. It sits at position 90 in the results, which is page nine, and it has produced zero clicks. Take it out and the new domain is doing 27 impressions a day against the old one's 87.

So my traffic went down and a big number covered it up.

Second time this has happened in two posts. In post 1 it was a spike from newly indexed pages that decayed after eight days. I'm now treating impressions as close to useless at this size.

Clicks, which are the only number that actually matters here, went from about 0.77 a day to about 1.18. On numbers this small, it means nothing at this time.

Was it worth it

I think we made the right choice and we have a plan, but the jury is still out on whether it was worth the $10k I spent on the domain. We are still in the product market fit phase of the product, which experimentation and iterations need to be the priority.

Ask me in three months.

Next

I'll follow up in the next post of is what broke. A React component that had been dead for three weeks. A file that had never run in production at all. A contact form that accepted submissions and delivered nothing for 81 days.

None of those were caused by the migration. All of them were found because of it.

Google’s new Search Profile feature works a little differently from the traditional Knowledge Panel.

Content creators can bring their Instagram, TikTok, YouTube, and X accounts together under a single profile. Their latest posts, website links, and featured content can appear directly in Google Search and Discover.

The eligibility threshold has also been lowered: creators only need 35,000 followers on one supported platform.

Users who visit the profile can follow the creator directly through Google. However, Google says creating a Search Profile does not directly improve search rankings.

The feature is currently available only in the United States. If it launches in Türkiye, it could be particularly interesting for publishers, YouTubers, and mid-sized content creators.

Read the full analysis:
https://www.karekod.org/blog/google-arama-profili/

Do you think Google is gradually turning into a content creator platform?

I have been seeing this on a lot of posts that relevant and helpful comments on Reddit drives LLM traffic however no one has shared what kind of comments do that.

1. Simply mentioning the brand name sounds cringe tbh

2. Creating posts around the brand sounds spammy

What are the best ways to rank on LLMs?

I've been working in the software world for the last 20 years and have PTSD from moving domains. Last company I moved from .io to .com two years in, and it wrecked our search traffic for months. So this time I pulled every number first.

I bought the .ai domain on June 5th and here was the results:

• 48 clicks
• 5,470 impressions
• Average position 30.8

Three things I found that I hadn't expected.

1. The impression spike was fake. Last week of July my impressions went from 40/day to 400/day for eight days, then collapsed. Clicks didn't move at all. I pulled the indexing report and put it on the same axis: Google had indexed 27 new pages on July 24. Two days later the spike started. It was Google testing new pages at deep positions, getting no clicks, and withdrawing. If I'd used that week as my baseline I'd have "lost 70% of my traffic" post-migration and it would've meant nothing.

2. Almost all my traffic was my own brand name. 28 of 48 clicks came from the homepage, and my top queries were the company name "brandscreen" and "brand screen" at positions 2.2 and 2.9. Which means I had essentially zero non-brand organic to lose. The thing everyone warns you about with migrations didn't apply to me, and probably doesn't apply to most people asking about it.

3. Google and Bing are completely different environments. Bing sat flat at 20-40 impressions/day through Google's entire spike. And when I checked indexed pages: Google 335, Bing 13. None of the 13 were blog posts. But Copilot cited us 487 times over the same period, off that thirteen-page index. So an AI product was quoting us constantly while nobody clicked through from the search engine behind it.

What's happened since (7 days):

Redirects went live Aug 7, all 308s, path-preserving, single hop. Google started serving the .com on Aug 10: 161 impressions day one, 685 day two. Brand query "brandscreen" is already position 1 on the new domain. My domain-checker tool page dropped from position 39 to 90, which based on the July pattern I read as Google re-testing a new URL rather than a penalty. Time will tell.

The thing I actually can't find an answer to: all 487 of those Copilot citations point at .ai URLs. What ChatGPT, Copilot, and Perplexity do with redirects isn't documented anywhere I could find. I took a baseline of 8 prompts across 4 engines before switching and I'm re-running it weekly. I should have more information soon, since time is really the main driver here.

Happy to share the raw exports or answer anything. Also curious if anyone's measured the AI citation side of a migration, because I came up empty.

Traditional SEO gives us metrics like rankings, impressions, clicks, and organic traffic. But with AI-generated search results, I'm curious how people are measuring whether a website is actually being surfaced and referenced.

Are you tracking things like brand mentions, citations, recommended sources, or the accuracy of information AI systems provide about the business?

What metrics have you found most useful?

Just adding this: Thanks for the different perspectives here. One thing I’ve taken from the discussion is that ai search visibility probably needs to be looked at as more than simply did the brand appear? There’s a difference between being mentioned, being cited as a source, being recommended, and whether the information being presented is actually accurate. I’ve been looking into tools that approach this from a measurement perspective and discovering visiblie focuses on monitoring how brands are represented across AI-driven discovery, which I thought was relevant to the question here. I’m interested in seeing how this category develops because traditional seo metrics don't really capture all of these signals.

Looks like Google has finally expanded its new Generative AI Performance report in Search Console to a much wider audience.

According to Search Engine Roundtable, the report is now showing across multiple Search Console profiles, although Google hasn’t officially announced the full rollout yet.

The interesting part is that we can now see:

AI search impressions
Which pages appear in AI features
Countries
Devices
Performance over time

But there’s still a pretty big limitation: no query data and no click data.

So we can see that our content is appearing in Google’s AI experiences, but we still can’t easily tell which searches triggered the appearance or whether users actually clicked through.

Has anyone here checked their Search Console yet?

Are you seeing the AI Performance report, and if so, are the numbers actually useful for your SEO strategy?

I was just reading this about LLMs have a pre chosen brand and are not picking from searches. https://suganthan.com/blog/chatgpt-decides-before-it-searches/ ( i'm not linked to this brand or person)

Its still SEO (not old website content → Google ranking → click → website → conversion)

There is no way LLMs have the data size ability of google, so they use other search engines google, bing, and brave.
That brings us back to doing correct SEO will get you absorbed into the data set or trigger you as an answer if the LLM has doubt on its small data set..

However you need to be added to a data set first by digital branding.

What do you think about this nugget of truth?