r/GenEngineOptimization

I’ve been running the same business through different AI search prompts, and I’m starting to think the idea of a single “AI ranking” is misleading.

Take a local company and test:

• “best company for X in Dallas”
• “reliable X company in Dallas”
• “who would you recommend for X in Dallas?”
• “X company for an emergency”
• “affordable X service near Dallas”

Same business. Same general service.

But ChatGPT, Gemini, and Perplexity can produce surprisingly different recommendations depending on the intent and wording.

What seems more useful to me is measuring appearance rate across a group of prompts.

That’s what I’ve been testing with Signal AEO: prompt-level visibility, platform differences, recommendation consistency, and which sources appear alongside the recommendation.

Instead of asking:

“What position do we rank?”

I’m starting to ask:

“Across 50 realistic ways a customer could ask for this service, how often are we actually part of the answer?”

That feels much closer to what AI visibility really means.

Curious if anyone else is measuring it this way. Have you found certain types of prompts to be significantly more stable than others?

We track AI brand visibility, so we have a lot of stored responses. Wanted to answer something basic: when an AI answers a question, how many brands does it actually name?

234,000+ responses, March 1 to July 13, 2026, five engines.

Brand mentions per response (last 14 days in July)

• ChatGPT: 4.5
• Google AI Overview: 3.5
• Gemini: 3.5
• Google AI Mode: 3.2
• Perplexity: 2.7

Count each brand only once per answer, and it tightens to 2.2–3.3. So engines repeat themselves 1.2x to 1.4x.

Two things stood out:

Every engine is down since March. True whether you count every mention or each brand once, so it's not a repetition artifact.

AI Mode is wildly unstable. Day-to-day swings are 31% of its own average, vs 9% for ChatGPT. It went from 8.8 mentions per response in early May to 2.6 six weeks later. If you spot-check visibility there weekly, you're mostly reading noise.

Method: a "mention" counts every occurrence (Nike named 3x in one answer = 3); the distinct count treats it as 1. Same responses for both. Worth noting these are prompts our customers chose to track rather than a random sample of AI queries, so I'd trust the relative comparisons and trend direction over the absolute numbers.

Charts and full methodology: https://vercite.io/research/engine-personalities

Happy to get into how anything was counted.

Hey all,

I'm a small business owner, I run a software company in the marketing space. Spent a long time building out our sites and the traffic still wasn't where I wanted it, so I started learning SEO myself.

Didn't take long before I decided I'd rather hand it off and go back to building. So I started talking to agencies.

Problem was the pricing (first one quoted me $1,800/month plus a $1,400 setup fee for like 4 articles a month) and then when I actually asked about their process, they were running the content through ChatGPT anyway. They just had a system built around it.

So I spent the last year building that system myself, tweaking & improving it as I went. Here's what's actually worked:

1. Keyword research is what steers everything else

Sounds obvious, I still got it wrong for months. Never prompt the AI to "pick a topic and write." Pull keywords that have real intent behind them, check what's already ranking, and look specifically for gaps where competitors have coverage and you have nothing. I use Semrush + DataForSEO for this and it's worked well. Writing for volume without matching intent is how you end up with 50 articles ranking for nothing, which I know because that's what I did first.

2. Deep research for every article

Scrape the top 10 pages for the keyword, pull out what they cover, what questions they answer, what entities they mention. That all becomes context for the writing step. Skip it and you get the generic AI article anyone can spot in two sentences.

3. RAG over your own site pages

Crawl your pages, embed them, give the writing agent retrieval access. Two payoffs: it stops repeating topics you've already covered (cannibalization), and it describes your product accurately instead of making up features. Helps a lot with internal linking too.

4. Internal linking needs its own dedicated pass

This was one of the biggest single lifts for me and almost nobody does it properly. After generating, semantically match the new article against everything you've already got and insert links in both directions. New article points to old ones, old ones get updated to point at the new one. Vary the anchor text. An orphan page is a page that doesn't rank.

5. Pillar / cluster structure

Pick 4 or 5 core topics as pillars. Everything you publish is a cluster page linking up to its pillar. Google reads that as topical authority. Without it you've just got 200 unrelated posts and no signal.

6. Refresh your content on a schedule

Content decays. Every 2 to 3 months I re-run the research step on the top 20% of pages, update the stats, add sections for subtopics that have come up since, resubmit. Refreshed pages usually outperform brand new ones for a fraction of the effort. That one surprised me.

7. Put a real author on each post

Same articles, but with a real byline linked to an actual LinkedIn or X profile. It moved the needle. E-E-A-T isn't a myth and faceless content underperforms.

8. Image quality matters more than you'd think

If your images look obviously AI generated, bounce rate goes up. I can't prove what Google does with Chrome data and I know that one gets argued about, but the correlation between bad images and bad dwell time was consistent enough on my sites that I stopped fighting it.

9. Backlinks

When we started adding backlinks to the system, that's when we saw the biggest jump in the number of keywords we were ranking for. Content on its own plateaued after 6 months.

What didn't work

10. "JSON schema so AI can read your page." Structured markup still helps for rich snippets in classic search, that part's fine. But LLMs are built to process natural language, that's the whole point of them. Clear prose with real numbers in it does far more.

11. More than about 5 new pages a day. Crawl budget is finite and it gets allocated based on how much authority Google thinks your site has. Dump 100 pages at once and most of them just sit there undiscovered. Spreading it out sends a sustained freshness signal and that compounds. One a day for a year beats 365 in a month, and it isn't close.

12. llms.txt. Doesn't do anything useful. Not risky, just a waste of time. Same for making markdown versions of your pages, for the same reason as the schema thing. You're structuring data for a model that was specifically built to handle unstructured data.

Though it sucks that bing only measures AI citations from its own set of AI engines, not chatgpt, claude, or perplexity.

I've had several people come to me and ask about Cloudflare's Agent Markdown product and whether it is a good solution for GEO/AEO.

On the surface, it seems to solve retrieval token cost issues, which may be enough to move the needle. However, I don't answer questions like this with "on the surface" information.

According to CF's documentation, the crawler must present "Accept: test/markdown" in order to be served MD.

I enabled the markdown agent on one of our domains, then sent crawlers to it 20,000 times. I wanted to see if the agent would serve MD to a wildcard accept header.

In particular, GPTbot sends a complex header: "Accept: text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8" and I wanted to know if that would trigger CF MD.

The answer is no. CF only served MD when the accept header showed "text/markdown."

So the obvious next question is "do the frontier models ask for markdown?"

To measure this, I ran an experiment across 5 domains that we observe and logged the accept headers.

Over about 900k recorded crawls, the frontier models requested markdown ZERO times.

Thus, CF Agent Markdown has ZERO impact on frontier crawler behavior because it NEVER serves MD to them.

You can find the logs and scripts at https://answershare.com/research/markdown-accept-study

I launched ModelSaid for founders and small businesses who want to know how they show up when buyers ask AI systems for recommendations.

The basic idea is simple: instead of only tracking rankings or traffic, you test buyer-intent prompts such as "best option for X", "alternatives to Y", or "who should I use for Z", then look at:

1. whether your brand is mentioned
2. whether it is actually recommended
3. what language is used around trust, pricing, category fit, and proof
4. which third-party sources seem to influence the answer
5. what gaps you can fix with positioning, content, reviews, or comparison pages

I am looking for feedback from builders: is this something you already check manually, and what would make the report useful enough to act on?

Link: https://modelsaid.com

I audit distributor product data and recently found a search-enrichment rule that treated any number as a possible wire gauge.

A screw listed as #8-32 was tagged as “8 gauge wire.” A reference such as REF#259286 became “259286 AWG.” Once the rule ran across the catalog, thousands of unrelated products started appearing in electrical searches.

The search engine wasn’t really the source of the problem. Raw identifiers and inferred attributes had been mixed together without recording where the inference came from or checking whether it made sense for that product family.

I wrote up the failure and how distributors can prevent it:

https://subramanya.ai/2026/08/06/fixing-b2b-commerce-search-in-the-age-of-ai/

For people working with distributor or manufacturer catalogs: where does search quality usually break for you supplier feeds, taxonomy, cross-references, or ranking?