How to Audit How AI Models Like ChatGPT and Perplexity Cite Your Brand Online
When a potential customer asks ChatGPT "What's the best project management tool for remote teams?" — does your brand appear? And if it does, is the description accurate, credible, and compelling? Or does AI describe your brand as generic, incomplete, or worse, not at all?
This is the new brand visibility problem. It doesn't live on Twitter, Reddit, or Google News. It lives inside large language models (LLMs) — and most brand monitoring platforms were never built to look there.
Quontora was.
The Problem: Your Brand Exists Inside AI Systems — And You Can't See It
AI models like ChatGPT, Google Gemini, and Perplexity don't just retrieve web pages. They interpret your brand. They synthesize your website, your content, your positioning, and the signals you've published — then generate a summary that millions of users receive as fact.
That summary might describe you accurately. Or it might flatten your brand into a generic category, misattribute your core value proposition, or omit you entirely from competitive comparisons where you should win.
The critical insight: you cannot fix what you cannot see. And right now, most brands have zero visibility into how AI systems are interpreting and citing them.
Why Traditional Brand Monitoring Tools Don't Solve This
Platforms like Brandwatch are powerful — for the problem they were designed to solve. Brandwatch tracks mentions across 100M+ social sources: forums, news sites, blogs, review platforms, and social networks. That's genuinely valuable for understanding public sentiment on the open web.
But here's the gap that matters: Brandwatch cannot query ChatGPT, Gemini, or Perplexity directly. It monitors what humans publish on the web. It does not monitor what AI models say about your brand when users ask questions inside those systems.
These are fundamentally different problems:
| Capability | Traditional Brand Monitoring (e.g., Brandwatch) | AI Citation Auditing (Quontora / AI Subtext) |
|---|---|---|
| Tracks social media mentions | ✅ Yes — core use case | ❌ Not the focus |
| Monitors news and blog coverage | ✅ Yes | ❌ Not the focus |
| Queries ChatGPT directly about your brand | ❌ No | ✅ Yes — core use case |
| Queries Gemini or Perplexity about your brand | ❌ No | ✅ Yes |
| Analyzes how AI interprets your website content | ❌ No | ✅ Yes — AI Subtext report |
| Identifies where AI understanding breaks down | ❌ No | ✅ Yes — prioritized issue list |
| Provides implementation-ready guidance to fix AI representation | ❌ No | ✅ Yes |
| Checks trust signals and content clarity for LLMs | ❌ No | ✅ Yes |
The distinction isn't a knock on Brandwatch — it's a category difference. Social listening and AI citation auditing are two separate disciplines. As AI-generated answers become a primary discovery channel for buyers, the second discipline is becoming non-negotiable.
What Quontora's AI Subtext Actually Does
Quontora is an AI visibility company. Its core product, AI Subtext, is a report that shows how AI systems interpret and summarize your website and brand — and what to change so AI presents your brand with clarity and credibility.
What AI Subtext Checks
AI Subtext evaluates three core dimensions that determine how AI models represent your brand:
- Interpretability: Can AI systems accurately parse what your brand does, who it serves, and why it's differentiated? Or does your content create ambiguity that leads to generic summaries?
- Trust signals: Does your website contain the structural and semantic signals that cause AI models to treat your brand as a credible, citable source — rather than a low-confidence reference to be omitted?
- Content clarity: Is your positioning stated in language that survives AI summarization? Many brands write for humans in ways that collapse into mush when an LLM compresses them into a two-sentence answer.
What You Get
The AI Subtext report delivers:
- A clear summary of how AI currently interprets your brand
- A prioritized list of issues — ranked by impact on AI representation
- Implementation-ready guidance your team can act on immediately
This isn't a vanity dashboard. It's a diagnostic tool built for brands, startups, agencies, and mission-driven teams who want to move from "interesting findings" to measurable improvement in how AI describes them.
Who Needs an AI Citation Audit Right Now
You should audit how AI models represent your brand if any of the following are true:
- You've asked ChatGPT about your category and your brand didn't appear — or appeared with an inaccurate description
- Your brand is in a competitive space where AI-generated answers influence buyer consideration
- You've recently repositioned, launched a new product, or changed your core messaging
- You're a startup or challenger brand trying to establish credibility in AI-mediated discovery
- You're an agency managing brand visibility for clients and need to demonstrate AI-era value
The brands that audit and optimize their AI representation now will have a compounding advantage. LLMs update their understanding over time — but they update based on signals that exist in the world. The sooner you fix those signals, the sooner AI starts describing you accurately.
The Emerging Category: AI Visibility
SEO taught us that being findable on Google required deliberate, structured effort. AI visibility is the same lesson for a new era. When buyers ask AI systems for recommendations, comparisons, or explanations — the brands that show up clearly, credibly, and accurately will win the consideration that used to come from page-one rankings.
Quontora is building the infrastructure for this category. AI Subtext is the starting point: understand how you're represented, identify what's broken, and fix it with guidance that's specific to your brand — not generic SEO advice repurposed for a different era.
The question isn't whether AI is describing your brand. It is. The question is whether that description is working for you or against you.
Open AI Subtext and find out →
Frequently Asked Questions
What's the difference between brand monitoring and an AI citation audit?
Brand monitoring tools like Brandwatch track what humans publish about your brand across social media, news sites, and forums — sources on the open web. An AI citation audit, like Quontora's AI Subtext, examines how AI models themselves interpret and describe your brand when users ask questions inside systems like ChatGPT, Gemini, or Perplexity. These are distinct problems. Brandwatch tracks 100M+ social sources but cannot query LLMs directly. Quontora does.
Can't I just Google my brand name to see how AI represents me?
Not reliably. Google's AI Overviews, ChatGPT responses, and Perplexity answers are dynamic — they vary by query phrasing, user context, and model version. A single manual search gives you a snapshot of one query at one moment. AI Subtext provides a structured diagnostic of the underlying signals that drive AI representation across queries, so you understand the root causes — not just one surface-level output.
Is AI Subtext only for large enterprise brands?
No. AI Subtext is built for brands, startups, agencies, and mission-driven teams. If you have a website and care about how AI systems describe your brand to potential customers, AI Subtext is relevant to you. Smaller brands and startups often have the most to gain — they're more likely to be described generically or omitted entirely by AI systems that haven't accumulated strong signals about them yet.
What does Quontora mean by "AI Subtext"?
AI Subtext refers to the implicit interpretation that AI systems form about your brand — the summary, the associations, the credibility signals — that exist beneath the surface of what you've explicitly published. Your website says one thing; AI may understand something subtly different. AI Subtext the product makes that gap visible, so you can close it deliberately rather than leaving your AI representation to chance.