Check whether AI actually recommends your brand.

What an AI visibility checker actually checks

"AI visibility" means whether AI answer engines surface your brand when a user asks a relevant question. It is not the same as a Google ranking — a #1 blue-link page can be completely absent from the AI answer above it, and a page on result two can be the one the model cites. So AI visibility has to be measured on its own terms. There are two distinct things worth measuring, and confusing them is the most common mistake:

• **Mention** — the AI names your brand in the text of its answer. Good for awareness, but it doesn't necessarily send anyone to your site.
• **Citation** — the AI links to your domain as a source. This is the stronger authority signal: it means the engine trusted your page enough to footnote it, and it's the only thing that drives an actual referral click.

A complete checker reads both, then adds the context that makes them useful: where you place in the answer, whether the mention is positive or critical, which topics you're absent in, and — crucially — which competitors get named instead of you. A one-time checker gives you that as a snapshot; tracking it over time is what turns the snapshot into a trend.

Why checking AI visibility matters now

The shift isn't hypothetical — it's measured. ChatGPT alone reached roughly 900 million weekly active users in early 2026. And when an AI answer 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, and roughly 18% of searches produced an AI summary at all. The takeaway is simple: if AI is increasingly the surface buyers see first, the brand it names is the one that wins the moment — and the only way to know whether that's you is to check.

How an AI visibility check works (and how SourceWatch does it)

Under the hood, an automated AI visibility checker is honest and unglamorous: it fires a bank of real customer questions at the LLMs and parses each answer for your brand's mentions, citations, position and sentiment — then compares you against the competitors named instead. That's the mechanism, whether a tool dresses it up or not.

On this page, SourceWatch's free, working check is the **AI SEO audit** — a point-in-time read of a single page, not an inline widget and not live monitoring. Here's exactly what it returns:

1. 1

   Enter one URL

   Paste the page you want checked — your homepage or a key landing page. No login required to see your result.

2. 2

   We read it the way AI engines do

   In about 15 seconds SourceWatch checks whether Google's Knowledge Graph recognizes your brand as a real entity, whether AI crawlers (GPTBot, ClaudeBot, PerplexityBot, Google-Extended) are allowed to read the page, and how answer-ready its structure is.

3. 3

   Get your AI-readiness score + top fixes

   You get a 0–100 score, a letter grade, a clear Google-recognition verdict, and your highest-impact fixes written in plain English — not a 40-page PDF.

The honest contrast: free checkers vs the free audit

Plenty of free "AI visibility checkers" exist — Ahrefs and Semrush both run one. They're useful: typically a one-shot snapshot of where your brand is mentioned across a few engines. SourceWatch's free offering is shaped differently, and we'd rather be clear about that than overclaim. The table below is the honest comparison — including what the free audit does *not* do.

In short: a typical free checker is a brand-level snapshot of mentions; the free AI SEO audit is a page-level readiness check that also tells you what to fix. Different jobs — and you can run both. For a wider field guide, see the best AI SEO tools.

Two things a snapshot checker structurally can't do

Almost every AI visibility tool works the same way: fire synthetic prompts at the LLMs and parse the answers. That's genuinely useful, and SourceWatch does it too on a plan. But it's an *inference* — a sample of what the model might say, not a record of what actually happened on your site. Two things SourceWatch adds require being on your own analytics, which a prompt-only checker can't reach.

Moat 1 — First-party AI traffic, verified, not inferred

When an AI engine reads or cites your site, its crawler hits your pages and its answers send real referral clicks. SourceWatch captures both from your own first-party data via a one-line snippet or a Cloudflare Worker — and verifies each hit against published vendor IP ranges, so a spoofed user-agent pretending to be GPTBot doesn't pollute your numbers. That's ground truth: the real bots that crawled you and the real visitors who arrived from AI, separated from the fakes. A prompt-scraping checker can't show this, because it doesn't live in the model — it lives in your traffic logs.

Moat 2 — It works inside Claude Code (MCP-native)

SourceWatch ships an MCP server, so your AI visibility data is readable and actionable from inside Claude Code — your assistant can pull your citation gaps and the real queries the models ran, and help you act on them in the same loop, not just stare at a dashboard. Among self-serve tools this is effectively unique; the only comparable agent stack is enterprise-only and gated behind a separate subscription.

Checked it and you're invisible? How to improve

A low score isn't a verdict — it's a to-do list. 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.
• **Become a recognized entity.** Add Organization schema and consistent references (LinkedIn, Crunchbase, Wikipedia where earned) 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.** Lead each key page with a direct one-sentence answer, use headings that mirror real questions, and back claims with citable facts. This is the core of answer-engine optimization and the single tactic the GEO research found lifts generative-engine visibility most.
• **Publish an llms.txt file.** The /llms.txt standard (proposed by Jeremy Howard, Sept 2024) gives LLMs a curated, inference-time map of your best content — a concrete, low-effort signal you can ship this week.