AI Visibility &
LLM Sentiment. Know How AI
Describes Your Brand.

The New Brand Risk:
AI Is Talking About You Without You

ChatGPT, Gemini, Perplexity, and Copilot answer buyer questions about your brand right now, whether you’re monitoring them or not.

They may describe your pricing inaccurately, recommend a competitor instead, or not mention you at all.

AI visibility is the new share of voice. LLM sentiment is the new brand reputation score

Our
AI Visibility
Audit

We query every major AI system with the questions your customers are actually asking. We map where your brand appears, where it doesn’t, how AI describes it, and where competitors are outscoring you — establishing a clear Share of Model (SoM) baseline.

AI answer presence audit across ChatGPT, Gemini, Perplexity, Copilot
Sentiment tone and accuracy review of AI brand descriptions
Competitor SoM benchmarking
Hallucination detection and documentation

Ongoing
LLM Sentiment
Monitoring

LLM outputs shift as models update, new content enters the training pipeline, and competitor activity intensifies. We provide continuous monitoring via our transparent.ai dashboard — tracking sentiment trends, citation frequency, and emerging risks before a negative narrative takes hold.

“The AI Visibility insights helped us understand exactly where we were being mentioned and where we were missing opportunities. Clear, actionable, and incredibly valuable.”

“Tracking sentiment trends over time helped us spot perception shifts early and respond before they became bigger issues.”

“The competitor benchmarking revealed opportunities we wouldn’t have discovered through traditional SEO reporting.”

How often do AI models update how they describe brands?

Constantly and unpredictably. Models with live web access (Perplexity, ChatGPT search, Gemini) can shift their answers within hours as new content gets indexed, while base training updates land less often but change descriptions wholesale. This dual cadence is exactly why a one-off audit goes stale fast. Continuous monitoring catches sentiment shifts and emerging inaccuracies as they happen, so you respond before a flawed narrative hardens into the answer buyers see.

What is Share of Model (SoM)?

Share of Model measures how often your brand appears in AI-generated answers relative to competitors, across a defined set of buyer queries. It’s the GEO equivalent of share of voice in traditional search but higher stakes, because AI typically surfaces only two or three brands per answer, not a page of ten links. A low SoM means you’re effectively invisible at the exact moment buyers ask AI for a recommendation in your category.

Can you fix hallucinations where AI gets our brand details wrong?

Yes. We trace the signals causing the inaccuracy: outdated pages, weak schema, thin third-party coverage — and correct them through structured data, authoritative content, and citation building that gives models reliable sources to draw from. As those signals propagate, AI answers realign with the facts. Hallucination correction is part of our AI Reputation Watch service, with monitoring to confirm the fix holds and catch any new errors early.