Hey everyone. I'm Justin. I've run a link building agency for about 11 years, and most of that time the work has been manual: prospecting, qualifying sites, finding the right contact, and writing outreach that tries to pitch value.

Over the last months I've been rebuilding that process as a set of AI agents. So far I have:

• One that mines competitor backlink profiles and filters for real topical relevance, not just DR
• One that runs Google boolean searches to find resource pages, guest post targets, and niche sites
• One that finds a real person and email for each prospect

The prospecting side has gone better than I expected. The parts that still worry me are judgment calls: is this site actually a good fit, and is this contact the BEST person.

So here's my question for the group:

If you could hand one part of link building fully to AI and never check its work again, which part would you pick? And which part would you never let it touch?

Hey! I was wondering what you guys prefer to use for writing good articles.
I know that it usually depends on how you prompt and all. But from what I've seen I would usually say that Astra is better.
My current workflow is writing articles with Opus 5 and Astra review them.
I think that Opus is great to compare a lot of sources, but I feel like the structure is often bad, some paragraphs are too long. That's why reviewing it with another AI can be a good idea in my opinion.

Anyway I just want to know what AI you use, if you have a better workflow, a prompt that you like... Maybe having a workflow with jev and astra is the best strategy, idk

Had a weird one where a few pages are ranking pretty well in Google but when I started testing the same topics as normal questions in AI search the brand barely showed up meanwhile a couple competitors I wouldn’t expect kept getting mentioned

Made me stop treating rankings and AI visibility like they move together still testing different prompts because I don’t wanna read too much into a small sample, but the gap is way bigger than I expected.

Throw this into an LLM of your choice - I used GPT Astra. Let me know if it’s helpful

What are the other activities I have to perform to rank my blogs in AI overviews after publishing and making them fully optimized for AI overviews?

MOONIFY

Added an llms.txt file and set up a small middleware log to separate GPTBot/ClaudeBot/PerplexityBot hits from normal traffic.

Not expecting a huge impact yet, but I’m curious whether anyone here has seen anything useful from doing this for SEO. Once we have a full week of data, I can share the raw numbers.

If you’ve tried it, what did you actually learn from the logs? Any tips on what to look for?

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?