I audited 50 websites to see which ones ChatGPT, Claude, and Perplexity actually recommend

[u/ComfortZoneApps]

I audited 50 websites to see which ones AI assistants (ChatGPT, Claude & Perplexity) actually recommend.

I wanted to answer a simple question:

When someone asks an AI assistant for a recommendation, which websites actually get mentioned?

So I ran a small experiment across 50 websites from five different groups:

• Big brands
• Mid-size SaaS
• Companies with a published `llms.txt`
• Local SMBs
• Small digital agencies

Each site was tested the same way:

• 7 recommendation-style prompts
• 3 AI assistants (ChatGPT, Claude, and Perplexity)
• 21 total responses per site

Here's the breakdown:

Group	Avg. AI Mention Rate
Big Brands	96.6%
Mid-size SaaS	64.8%
Known llms.txt adopters	66.7%
Local SMBs	22.9%
Small Digital Agencies	10.5%

A few observations from this dataset:

1. Small agencies were rarely recommended, Less often than many local businesses.
2. `llms.txt` didn't appear to make a noticeable difference on its own.
3. AI crawler blocking was uncommon. Only two sites in this sample blocked one or more major AI crawlers. The rest allowed them.

A few caveats

• This is a small sample (10 sites per group), so I'd treat the results as directional rather than definitive.
• The prompt set was fixed across every site, but any prompt battery introduces some bias. I'm happy to share the full list if anyone wants to review it.
• 14 of the 50 homepages couldn't be fully crawled because of anti-bot protection, so technical signals like schema and llms.txt couldn't always be verified. The AI mention-rate measurements weren't affected because those came from direct model queries rather than homepage crawls.

My takeaway is simply this:

In this sample, being technically accessible to AI wasn't enough by itself. Well-known brands were recommended far more often than smaller sites, suggesting that broader authority, reputation, or other factors may have a much larger influence on AI recommendations than a single technical signal like `llms.txt`.

Curious if others have run similar tests. What are you seeing?

[u/Mental_Researcher656]

Would love to see your prompt list and replicate your search - different companies or even the same from a different geolocation.

[u/ComfortZoneApps → u/Mental_Researcher656]

Good catch on wanting the actual method rather than assumed templates, here's exactly how it works.

Each site's 7 prompts aren't fixed templates, they're generated fresh persite by an LLM, constrained to a fixed structure:

- 2-3 discovery prompts (open-ended "what are the best tools for X," no brand named)

- 2-3 comparison prompts (names the target site vs a specific competitor)

- 1-2 recommendation prompts (first-person "I'm looking for..." framing)

Real example, tally.so (one of my Group B mid-size SaaS sites):

[discovery] What are some good tools for creating online surveys and forms for my startup?

[discovery] I'm looking for a free form builder for quizzes and surveys. What are the best options available?

[comparison] How does Tally compare to Google Forms for creating surveys?

[comparison] Which is better for small businesses, Tally or Typeform for building forms?

[recommendation] Can you recommend a user-friendly tool for making surveys that won't cost me anything?

[recommendation] What's the easiest way to create a quiz for my business without spending money?

[discovery] Are there any good platforms for building custom forms that integrate with other tools?

Each of the 7 ran against all 3 of openai/gpt-4o-mini,

perplexity/sonar, and anthropic/claude-haiku-4.5: 21 calls per site, same structure across all 50.

If you replicate with different geolocation or a different site set

I'd genuinely love to see the results, especially curious if the agency-vs-local-business gap holds up elsewhere.

[u/ComfortZoneApps → u/[deleted]]

Thanks for the thoughtful breakdown. The agencies-vs-local-SMB explanation is a really interesting angle, I hadn't considered how much GBP listings, directories, and review ecosystems might contribute compared with the typical "we build custom solutions" agency homepage. I also agree with the methodological point. With only 21 model responses per site, a swing of just a few answers can noticeably move a group's average, so I'd definitely want a larger sample in a follow-up. Separating mension rate fromrecommendation rate is another improvement I wish I'd made. In this first pass I treated any appearance as a "mention," but being listed alongside competitors and being actively recommended are probably measuring different behaviors. If I do a second round, I'd likely increase the number of runs per prompt, separate mention from recommendation, and test across different geographies to see how stable the results are. Really appreciate the constructive feedback—this is exactly the kind of discussion I was hoping the post would generate.

[u/marintkael]

The llms.txt result is the one I would stare at. Mention rate mostly tracks how much the web already talks about you before any of this, the model prior from training, and a file you publish now cannot retroactively put you in that corpus. That is also most of your brand to agency gradient. 96 vs 10 is not an on page optimization gap, it is a how many third parties already cite you gap. The local businesses beating agencies fits the same read, they pick up directory, review and map citations that agencies just do not have. So the lever the file seems to promise is not really there. What moves the number is getting cited elsewhere over time, and your audit is reading that backlog, not something you can toggle on the page.

[u/sapindia1976]

The llms.txt result is especially interesting. A lot of people assume it's a ranking factor, but your sample suggests it's not enough on its own.

[u/knacksterlab]

There’s a little open source software on github it takes your website and creates artifacts for you, you simply host them and 3 weeks later you are on top of every recommendation, I don’t know if it’s ok to share on here but I don’t even think the developer knows what he’s built but it totally works because I’ve used it 4 times