Find out if AI search recommends you — or your competitor.

What an AI search visibility check actually measures

"Are we visible in AI?" is too vague to act on. A real AI search visibility check breaks it into measurable pieces — per engine, because ChatGPT, Perplexity, Gemini and Claude each source their answers from different places. Being visible in one is not being visible in all. Four signals decide whether AI puts you in the answer:

• **Mention rate** — across the real questions buyers ask, how often does each engine name your brand at all? This is your baseline presence, and usually the first number that surprises people.
• **Share of voice vs competitors** — when the AI does answer, who gets named: you, or the competitors it lists instead? Share of voice turns "we're invisible" into "we win 2 of 10 prompts and a rival wins 6."
• **The real questions being asked** — visibility is per-question. The actual prompts in your category show exactly which questions you win, which you lose, and which you've never shown up for.
• **What the AI actually cited** — the signal that matters most: the specific sources the model pulled to build its answer — your own pages, a directory, a competitor's blog, a Reddit thread. You can't earn a citation you can't see.

Why checking AI search visibility matters now

Search used to end in a click. Increasingly it ends in an answer — and the answer names a few brands and moves on. The shift isn't hypothetical; it's measured. ChatGPT alone reached roughly 900 million weekly active users in early 2026, and when an AI summary is present the clicks that used to flow to websites are staying inside the answer.

In Pew Research's 2025 study of 900 U.S. adults across nearly 69,000 searches, users clicked a traditional result in just 8% of visits when a Google AI summary was shown, versus 15% with no summary — roughly half. Clicks on links inside the summary happened in only about 1% of visits. So the question stops being "where do I rank?" and becomes "am I one of the few brands the AI names — and what did it cite to choose them?" If you're not in the answer, you're not in the consideration set.

What SourceWatch surfaces — and the two things rivals structurally can't

The free audit reads a single page the way AI engines do. A SourceWatch trial turns that into ongoing, cross-engine intelligence — plus two things almost no competitor has, because both require being on your own analytics, not just prompting the models.

Moat 1 — Real AI traffic, verified, not just synthetic prompts

Most checkers infer your visibility by firing test prompts at the models and seeing if your name comes back. SourceWatch does that and captures the real thing: actual AI-crawler hits and AI-referral clicks on your own site, verified against each vendor's published IP ranges so a spoofed user-agent pretending to be GPTBot doesn't pollute your numbers. So you see not just "the model said your name in a test," but who actually arrived from ChatGPT, Perplexity, Gemini and Claude — first-party data a prompt-sampling tool can't reach.

Moat 2 — Act on it inside Claude Code (MCP-native)

SourceWatch ships an MCP server for Claude Code, so your visibility data is readable and actionable right inside the agent you already work in — pull the gaps, generate target prompts, draft fixes, all in the loop. Among self-serve tools this is effectively unique; the only comparable agent stack is enterprise-only and gated behind a separate subscription. (A documented REST API is coming soon; today the programmatic surface is MCP.)

On top of the two moats, a trial tracks the competitive core: mention rate, self-citation rate and share of voice vs named competitors; the actual search queries the models ran; most-cited domains and your citation gaps; daily tracking with drop and competitor-movement alerts; and content briefs aimed at the questions you're losing — across ChatGPT, Perplexity, Gemini and Claude.

Why you have to check every engine, not just one

Checking one engine is not checking all of them. Engines source their answers from wildly different places, so the same brand can dominate one and be invisible on another. A Profound analysis of 680 million citations makes the divergence concrete: ChatGPT leaned heavily on Wikipedia (47.9% of its top-10 sources) while Perplexity leaned on Reddit (46.7%), and Google AI Overviews was more balanced. If your brand has a strong encyclopedic presence but no community footprint, you may post a healthy ChatGPT visibility and near-zero Perplexity visibility from the same questions.

So a real check tracks the major engines separately and reports a per-engine read, not a blended headline that hides where you're losing. The per-engine split is what tells you which source to earn next — a Wikipedia entry, a stronger community presence, fresher pages, or schema-marked content. SourceWatch runs your category's real questions against each engine and layers in the first-party traffic actually hitting your pages.

Checked it and you're invisible? How to improve

A low score isn't a verdict — it's a to-do list. There's a lot of GEO superstition online, so SourceWatch's recommendations lean on the peer-reviewed foundational GEO study (Aggarwal et al., KDD 2024) rather than folklore. The fixes that move AI visibility most are concrete and mostly within your control:

• **Let the crawlers in.** Confirm GPTBot, ClaudeBot, PerplexityBot and Google-Extended aren't blocked in robots.txt. If they can't read you, you can't be cited — full stop. See AI crawlers.
• **Become a recognized entity.** Add Organization schema and consistent references so Google's Knowledge Graph knows you're a real business. If Google doesn't recognize you, AI engines have nothing solid to cite.
• **Write answer-first, with citable facts.** Lead each key page with a direct one-sentence answer, add a statistic, quote a source, and structure it for extraction. Adding statistics, quotations and citations is exactly what the GEO research found lifts generative-engine visibility most.
• **Publish an llms.txt file.** The proposed open standard (Answer.AI, Sept 2024) maps your best content for LLMs. Treat it as forward-looking hygiene, not a guaranteed lever — it isn't yet confirmed to be read by ChatGPT or Gemini for live answers — but it's cheap and directionally right.

For the full playbook, see how to show up in AI search and the AI SEO guide. To compare tools, the best AI SEO tools roundup lays out the landscape.

Free check vs full tracking — the honest contrast

The free audit and a SourceWatch trial do different jobs, and we'd rather be clear than overclaim. The free check reads one page point-in-time; the trial tracks your whole category across all four engines, daily — including the two moats. Here's the honest split: