The LLM Visibility Tools Cost $79/Month. Mine is Open Source.

Tom Capper at a Search Engine Journal webinar last week:

"There's no Search Console equivalent for LLMs."

Google Search Console tells you where you rank, how many people saw your result, your CTR, your average position. It tells you nothing about whether Claude mentions your domain when someone asks a question you should own.

That gap is now a product category. I checked most of them. Cheapest entry point: $39/month. The ones worth taking seriously: $79 and up.

At the same webinar, someone asked Capper directly: what are accessible ways to measure AEO/GEO visibility, since there's no equivalent of Search Console for LLMs? His answer was three manual approaches. None of them were a tool you could run.

I built mine for free. Open source. And it found something I'd missed for months on my own sites.

What the paid tools actually do

AIclicks, LLMrefs, Cairrot, Slate — the mechanics are the same across all of them. They query AI models with your target keywords, check whether your brand appears in the response, track it over time.

That's the whole product. The variance is in how many LLMs they cover, how the reports look, and whether the UI justifies the monthly fee.

None of them are doing anything technically unusual. They're calling APIs and parsing text.

So I added it to seo-agent as a standalone module: llm-visibility.

How it works

Point it at your domain and a query list — or just export from Google Search Console:

python main.py llm-visibility --domain dannwaneri.com --queries gsc-export.csv --project dannwaneri-com

It takes your top 20 queries by impressions. Sends each one to Claude Haiku. Checks whether your domain appears in the response. Writes everything to llm-visibility.md — visibility score, per-query results, gaps to address.

Cost: negligible. A 20-query run costs less than a cent.

What I found

I ran it on two of my own domains.

One scored 0%. Every query. Claude answered correctly — sometimes well — and never mentioned my site once.

The other scored 15%. Three out of twenty queries returned a mention. The other seventeen? Nothing.

Both domains have content on these exact topics. Both rank in Google for these queries. Neither is getting cited.

The same run also caught something else — a query sitting at position 9.5 with 29 impressions and 0% CTR: does twitch pay nigerians. The page was ranking. The title was answering the wrong question. That's not an LLM problem, that's a GSC problem. But llm-visibility sits on the same audit surface as gsc-insights, not separate from it. You find both in one pass.

Your Google rank tells you nothing about your LLM presence. They're different surfaces with different citation logic entirely.

The limitation I won't bury

Claude has a training data cutoff. Content published after that cutoff won't appear regardless of quality.

A score of 0% on a site less than a year old is expected — it's a data availability problem, not a content quality problem.

Run it quarterly. Watch the number move as training snapshots update.

The paid tools have this same limitation. They just don't always put it where you can see it.

What the pixel data adds

While I was building this, the same SEJ webinar surfaced pixel data from Tom Capper at STAT Search Analytics worth flagging: position 1 now sits 635 pixels down the page on desktop. On mobile, the top organic result is below the fold nearly two thirds of the time. AI Overviews consume roughly a third of above-the-fold space on informational queries. Paid and shopping units take over 60% on commercial ones. Organic gets what's left. (Source: Search Engine Journal, May 2026)

seo-agent covers both surfaces. The serp-features module hits SerpApi for each target query and maps which features are present — AI Overview, featured snippet, PAA, image pack, local pack. llm-visibility handles the other surface.

Neither module needs a paid subscription. SerpApi has a free tier: 100 searches/month, no credit card.

What's still missing

This only tests Claude. Not ChatGPT, not Perplexity, not Gemini.

The paid tools cover 6–10 models. That's a real gap.

I'm one person. Multi-model support is on the list.

If your Claude visibility score is 0%, adding Perplexity to the test won't fix the underlying problem. The content isn't strong enough to get cited anywhere. Fix that first. Then track across more models.

The tool

Everything is open source: github.com/dannwaneri/seo-agent

Full breakdown of what it found on a real site — a Nigerian creator site that went from 0.4% to 44% pass rate in one afternoon — at dannwaneri.com/seo-automation.

The full module list: