That question is now asked of an assistant before it is asked of a vendor, and the answer decides whether you are in a product conversation or a managed-service one. GrackerAI shows you which of those framings currently name you.
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EDR, XDR and MDR are three answers to one problem with very different commercial shapes. When an assistant picks a framing for a buyer, it has already narrowed the field — often before anyone has evaluated a detection claim.
Prompt Topics keeps the three framings separate, so you can see whether the engines put you in the product conversation, the managed one, or neither.
Brand Sentiment tracks the qualifiers attached to your name, which in this category usually means claims about noise, agent weight or operational burden.
CVE, CWE, ATT&CK, CAPEC and NIST references on your pages are resolved against the body that issued them, and an active-exploit claim is checked against CISA's Known Exploited Vulnerabilities catalog.
Identifiers are resolved before a draft is written, and anything the model produced that is not in that resolved set is stripped out before the page publishes.
New vulnerabilities and security news are swept on a schedule, scored for exploitability and for how closely they touch your products, and the survivors become monitored prompts automatically.
These prompts pull cited sources often, especially where independent test results exist, so weight Perplexity and Claude.
| Prompt category | Example query | Where it lands most |
|---|---|---|
| Framing | Do we need EDR or a managed MDR service | ChatGPTPerplexity |
| Capacity | Best endpoint platform for a team without a 24/7 SOC | ChatGPTClaude |
| Coverage | Endpoint tools with real Linux and macOS parity | ClaudePerplexity |
| Evidence | Which EDR performed best in independent testing | PerplexityChatGPT |
| Replacement | Migrating off a legacy antivirus estate | ChatGPTPerplexity |
If the engines only ever name you as a product, you never reach buyers who asked whether to outsource. Track the framings separately and publish for the one you are missing.
Explore Prompt Topics →Independent evaluations get quoted heavily and rarely in full. Citations shows which summaries the engines actually read, so you can see what shaped the sentence about you.
Explore LLM Citations →Platform parity is checked early and models fill gaps with assumptions. An explicit, current coverage page removes an objection you are otherwise losing silently.
Explore Content Engine →Sentiment moves before share of voice. A model that begins hedging about agent overhead is repeating something it read, and that source is findable.
Explore Brand Sentiment →| Stage | What you use |
|---|---|
| Diagnose Find where you are missing today | Prompt Research AI Visibility Monitoring AI Visibility Score |
| Explain Understand why a prompt goes to someone else | Visibility Diagnosis LLM Citations Competitor Monitoring |
| Fix Turn findings into published pages | Technical AEO Audit Recommendation Engine Content Engine |
| Ship Get it live and keep it moving | Tasks WordPress Publishing AI Search Analytics |
Questions B2B SaaS teams ask before getting started
The same all 10 AI engines it tracks for every vertical: ChatGPT, Perplexity, Google AI Mode, Google AI Overviews, Gemini, Microsoft Copilot, DeepSeek, Brave Leo, Grok and Claude. Coverage is by plan — 3 engines on Starter, 4 on Scale and all 10 on Enterprise.
The EDR, XDR and MDR framings are tracked as separate topics because they lead to different commercial conversations, and sentiment is watched alongside share of voice since operational objections decide this category.
Monitoring starts returning answers on the first run, so you can see where you stand immediately. Movement in citations follows publishing, which depends on how fast you ship the fixes the audit and diagnosis hand you.
No. Classic SEO governs ranking; AEO and GEO govern whether a model quotes you. The technical audit overlaps with an SEO audit in places, but the citation work sits alongside what you already do rather than replacing it.
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