The question lands in every marketing team's budget conversation: Do we really need a dedicated AI visibility tool, or does our existing SEO platform already cover this? Semrush has been winning that argument — not because the evidence supports it, but because no one has published the evidence against it. Until now.
We ran 50 brand queries across categories including SaaS, fintech, e-commerce, and professional services through both Semrush's AI tracking add-on and AIsubtext. What we found isn't a close race. It's a structural gap that matters enormously if AI recommendations are already influencing your buyers — and in most B2B and high-consideration B2C categories, they are.
Semrush is an exceptional tool for what it was designed to do: track keyword rankings, backlink profiles, and organic search visibility across Google and Bing. Its AI tracking feature is a genuine attempt to extend that value into the LLM era. But extending a rank-tracking architecture into AI recommendation monitoring is like using a thermometer to measure humidity. You get a number. It's just not the number that matters.
AI recommendation visibility is not a ranking. It's a probabilistic, context-dependent, engine-specific output that changes based on query phrasing, conversation history, and the engine's own retrieval and synthesis logic. Measuring it requires a fundamentally different methodology — one built from the ground up for how LLMs actually work.
The most immediate gap is which AI engines are actually monitored. This matters because your buyers are not using a single AI engine. They're distributed across ChatGPT, Claude, Gemini, Perplexity, Microsoft Copilot, Grok, and others — and each engine has meaningfully different recommendation patterns for the same query.
AIsubtext measures Recommendation Share across ChatGPT, Claude, Gemini, and Perplexity — the four engines that collectively represent the overwhelming majority of AI-assisted purchase research. Semrush's AI tracking, by contrast, focuses primarily on AI Overviews within Google Search, with limited visibility into standalone LLM engines where conversational recommendation behavior actually occurs.
| AI Engine | Semrush AI Tracking | AIsubtext | Why It Matters |
|---|---|---|---|
| Google AI Overviews | ✅ Yes | ⚠️ Partial | High volume, but not conversational recommendation context |
| ChatGPT | ❌ No | ✅ Yes | #1 engine for B2B vendor research queries |
| Claude (Anthropic) | ❌ No | ✅ Yes | Rapidly growing share among professional and enterprise users |
| Gemini (Google DeepMind) | ❌ No | ✅ Yes | Default AI for Google Workspace users — massive installed base |
| Perplexity | ❌ No | ✅ Yes | Dominant for research-intent queries; cites sources explicitly |
| Microsoft Copilot | ⚠️ Limited | 🔄 Roadmap | Default AI for Microsoft 365 users |
| Grok (xAI) | ❌ No | 🔄 Roadmap | Growing among X/Twitter-native audiences |
If your buyers are asking ChatGPT "what's the best [your category] tool" — and they are — Semrush's AI tracking is not telling you whether you're being recommended. That's not a minor gap. That's the entire question.
Even where Semrush does surface AI-related data, the metric itself is structurally different from what AIsubtext measures. Semrush tracks mentions — whether your brand name appears somewhere in an AI-generated response. AIsubtext measures Recommendation Share — the percentage of relevant queries in your category where an AI engine actively recommends your brand as a solution.
These are not the same thing. A brand can be mentioned in an AI response as a cautionary example, a historical reference, or a secondary alternative while a competitor receives the primary recommendation. Mention count treats all three identically. Recommendation Share does not.
When a buyer asks "what project management tool should I use for a 10-person remote team," the AI engine names one or two tools with conviction. That's the signal that drives purchase consideration. AIsubtext is built to measure that signal specifically — across 50+ query variations per category, not a single branded query.
A third structural difference is how queries are constructed and how often they're refreshed. SEO platforms are optimized for keyword tracking — they monitor specific, exact-match queries at scheduled intervals. AI recommendation behavior requires a different approach: testing multiple phrasings of the same buyer intent, because LLMs respond differently to "best CRM for startups" versus "what CRM should a 20-person startup use" versus "recommend a CRM for a Series A company."
AIsubtext runs queries across this kind of variation to produce a statistically meaningful Recommendation Share score — not a snapshot of one query on one day. The methodology is designed to reflect how real buyers actually phrase questions to AI engines, which is conversational and varied, not keyword-exact.
The "one platform for everything" argument is compelling in theory. In practice, it works when the underlying data requirements are similar enough that a single architecture can serve both. SEO rank tracking and AI recommendation monitoring do not meet that bar. The data sources are different (search index vs. LLM inference), the query methodology is different (keyword vs. conversational), the metrics are different (rank vs. share), and the engines are different (Google vs. ChatGPT/Claude/Gemini/Perplexity).
Using Semrush to monitor AI recommendation visibility is not consolidation. It's substitution — and what gets substituted is the actual insight you need to compete in AI-mediated buying journeys.
AIsubtext was built specifically to answer the question your SEO platform cannot: When buyers ask AI engines for a recommendation in your category, how often do they name you? That number — your Recommendation Share — is measured across ChatGPT, Claude, Gemini, and Perplexity, across dozens of query variations, and delivered with a diagnostic that shows you why the number is what it is and what content or positioning changes would move it.
The free audit returns results in approximately 10 seconds. No credit card required. It's the fastest way to find out whether the AI engines your buyers are using are recommending you — or your competitors.
Run your free AI visibility audit →
No. As of 2025, Semrush's AI tracking features are focused on Google AI Overviews — the AI-generated summaries that appear within Google Search results. They do not monitor standalone LLM engines like ChatGPT, Claude, Gemini (outside of Google Search), or Perplexity. If your buyers are using those engines to research vendors or products in your category, Semrush's AI tracking will not tell you whether you're being recommended.
A mention means your brand name appeared somewhere in an AI-generated response. A recommendation means the AI engine actively suggested your brand as a solution to a buyer's specific problem. These are meaningfully different outcomes. A brand can be mentioned as a competitor, a cautionary example, or a secondary option while a different brand receives the primary recommendation. AIsubtext measures Recommendation Share — the rate at which AI engines recommend you as a primary solution — not raw mention frequency.
AIsubtext tests 50+ query variations per category to produce a statistically meaningful Recommendation Share score. This matters because LLMs respond differently to different phrasings of the same buyer intent. A single query snapshot can be misleading; a distribution of query variations reflects how real buyers actually interact with AI engines — conversationally and with natural language variation.
That depends on how much of your buyer journey now runs through AI engines. For categories where buyers routinely ask ChatGPT, Claude, Gemini, or Perplexity for vendor recommendations — which includes most B2B SaaS, professional services, fintech, and high-consideration B2C categories — the answer is yes. The cost of not knowing your Recommendation Share is measured in deals that go to competitors who are being recommended while you're invisible. AIsubtext offers a free audit with no credit card required, so you can see your current score before making any budget decision.