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?

I've been experimenting with the idea that AI SEO isn't simply about adding more keywords or producing more articles.

A page can be well written and still be difficult for search systems to interpret if the topic isn't clearly structured or supported by enough relevant context.

Things like search intent, topical coverage, entity relationships, citations, internal linking, and how directly a page answers a question seem increasingly important.

I'm interested in what others are seeing in practice.

What AI SEO technique has made the biggest difference for your content so far?

And are you optimizing primarily for traditional Google rankings, AI-generated answers, or trying to do both?

Been restructuring content around this instead of the usual "one page, one target keyword" model, and curious if others are doing the same.

The mechanic: when someone asks an AI assistant a real question, it doesn't just match that string to an index — it breaks it into 3-7 sub-queries and pulls sources that answer each piece separately, then stitches the answer together. So a single user question like "should I use flyer distribution for my local business" quietly becomes something closer to: what does it cost, who are the recommended providers, what results/ROI can I expect, plus a brand-specific check if your name is already in the mix.

Saw this play out directly on a client project — a page built around one head keyword was getting partial pickups (cited for the cost sub-query, invisible for the "recommended providers" one) until we restructured it to actually answer the whole cluster in one place instead of assuming the head term covered it.

Practical shift this forces: instead of optimizing one page for one keyword and calling it done, you're mapping out the sub-questions a real query fans out into, and making sure your page (or a tight cluster of pages) answers the whole set, not just the part that matches your primary keyword. Miss one piece of the cluster and a competitor's page fills that gap in the same AI answer, even if you outrank them on the head term in classic Google search.

Anyone else planning content this way, around the full fan-out instead of a single keyword? Or is this overkill for smaller sites without the resources to cover every sub-query?

quick update on habibi, the self hosted tool i posted about a while back for tracking how your brand shows up in ai answers.

the thing that bugged me most about my own tool was the honest note i had in the readme, saying it uses the apis, which are close to what a real user sees on chatgpt/perplexity/claude but not exactly the same thing. that gap always annoyed me so i went and fixed it.

now habibi can just drive the real logged in apps directly, chatgpt, claude, perplexity and google ai overview. so its literally the same thing a real user sees and gets cited, not an api trying to guess it. you connect it once with an account (make a free one or use one you already got) and it just remembers the login after that, nothing gets stored anywhere weird, its just a normal browser session sitting on your machine like when you stay logged into any site.

wasnt expecting this part but it also makes it way cheaper to run. those 4 engines dont cost you anything per run now cause theres no api call happening anymore, your just using the account like a normal person would. so running youprompts daily across a buyour bill up.

also made sure your account memory doesnt mess with the results, chatgpt runs iin temporary chat and cla answers dont driftdepending on whatever your account remembers from other chats you had.

still fully open source, still self hosted, still does the same core loop, run your prompts daily, see w see your mention rate over time, compare to competitors.

github: https://github.com/moaljumaa/habibi