How to Test Your Brand's 'Answerability' on Perplexity and Gemini
SEO rankings don't tell you if AI actually recommends your brand. That's where answerability comes in—how well AI models cite and contextualize you when users ask real questions.
To measure this, you need a manual audit using three types of prompts (Direct, Comparative, and Problem-Based) scored against a standardized Answerability Scorecard. This reveals your "Share of Model" (SoM) and pinpoints where your content fails to trigger AI citations.
Why SEO Rankings Don't Matter in AI Search
Traditional metrics like "Page 1 Ranking" or "Click-Through Rate" don't measure success in AI-driven search. A user may never visit your website even if the AI relies heavily on your content to generate an answer.
The Zero-Click Reality: AI models synthesize information to provide direct answers. If your brand is mentioned as the best solution, you win the mindshare—even without the click. Conversely, you might rank #1 on Google, but if Perplexity summarizes your competitor's features while ignoring yours, your AI visibility is effectively zero.
The Key Difference:
SEO (Search Engine Optimization): Optimizing for a list of links. Success = Ranking High.
GEO (Generative Engine Optimization): Optimizing for a synthesized answer. Success = Being Cited and Recommended.
Step 1: Build Your Prompt Map (The 3-Tier Framework)
You can't rely on a single keyword to understand your AI visibility. You need to simulate the user's conversational journey. Start by categorizing test prompts into three distinct tiers.
The 3-Tier Prompt Framework
Tier 1
Direct Brand Navigation
Tests if the AI knows who you are and what you do.
"What is Asana and what are its key features?"
Tier 2
Comparative Evaluation
Tests how the AI positions you against competitors.
"Compare Asana vs. Monday.com for remote team management."
Tier 3
Problem-Based Discovery
Tests if you appear as a solution for generic problems (High Intent).
"What are the best project management tools for distributed teams?"
Action Step: Create a spreadsheet and list 5-10 prompts for each tier relevant to your business. These 15-30 prompts form the basis of your audit.
Step 2: Run Tests on Perplexity and Gemini
Each AI platform behaves differently. Test consistently to get reliable data.
Testing on Perplexity
Perplexity is an "Answer Engine" that cites sources heavily. It's the closest proxy to how Google's AI Overviews work.
Mode: Use Pro Search if available—it performs deeper research. Standard search works too, but note which you're using.
What to Watch: Pay close attention to the Sources carousel at the top and numbered citations in the text.
Red Flag: If you rank in the top 3 organic results but aren't cited in the answer, your content is hard for AI to parse.
Testing on Gemini (Google)
Gemini integrates with Google Maps, YouTube, and Workspace. It's more creative but prone to hallucinations.
Mode: Use Gemini Advanced (Ultra 1.0) for the most capable model, or the standard version to simulate mass-market behavior.
What to Watch: Look for Drafts (Gemini often offers 3 versions of an answer). Check if your brand appears in all three.
Red Flag: Gemini may invent facts about your brand. Verify every claim it makes rigorously.
Step 3: Score Each Response with the Answerability Scorecard
You need a standardized way to grade AI output. Use this scorecard to evaluate each prompt response.
Mention
1 / 0
Did the AI mention your brand name in the text? (Yes=1, No=0)
Citation
1 / 0
Did the AI provide a clickable link to your website? (Yes=1, No=0)
Sentiment
+1 / 0 / -1
Was the mention Positive (+1), Neutral (0), or Negative (-1)?
Recommendation
1 / 0
Did the AI explicitly recommend your brand as a solution? (Yes=1, No=0)
How to Calculate Your Score: For a single prompt, the maximum score is 4 (Mentioned, Cited, Positive, Recommended).
Total "Share of Model" Score: Sum of scores across all 30 prompts ÷ Maximum possible score.
Step 4: Diagnose Your Position (The Traffic Light System)
Once you have your scores, categorize your status to determine next steps.
🔴 Red Light (Invisible):
What it means: No mentions in Tier 3 (Problem-Based) prompts.
Why it happens: The AI doesn't associate your brand with the problem space.
What to do: Create content that co-occurs with established competitors. Build entity association signals.
🟡 Yellow Light (Mentioned, Not Cited):
What it means: You're mentioned in text but no link back to your site.
Why it happens: The AI "knows" you from third-party reviews but doesn't trust your official site as the primary source.
What to do: Optimize technical schema and "About Us" pages for authoritative entity definitions.
🟢 Green Light (The Golden Answer):
What it means: Mentioned, Cited, and Recommended.
Why it matters: You've achieved answerability.
What to do: Defend this position by refreshing content with new data and expanding to adjacent topics.
Moving Beyond Manual Testing
Manual testing on Perplexity and Gemini gives you the qualitative insights that raw metrics miss. You'll see exactly how AI models perceive your brand's value proposition.
The challenge? Scaling this to track hundreds of prompts consistently. Once you have your baseline Answerability Score, you can benchmark the impact of future optimization efforts. For ongoing tracking at scale, consider automated GEO platforms that monitor your Share of Model across multiple AI engines.
FAQs
1. How is "answerability" different from "visibility"?
Visibility just means "appearing somewhere." Answerability measures the utility of your brand in an AI response—are you the answer to the user's problem, or just a footnote?
2. Why does Perplexity cite me but Gemini doesn't?
Each AI uses different data sources and retrieval logic. Perplexity relies on live web search (Bing/Google indices), making it responsive to recent SEO changes. Gemini relies more on its internal training data and Google's Knowledge Graph, which updates slower.
3. What if the AI gives incorrect information about my brand?
This is a critical brand safety issue. Create authoritative content (a clearly structured Press or About page) that contradicts the falsehood. Then use feedback mechanisms within the AI tools to flag the error.
4. Can I automate this testing process?
Yes, but start with manual testing for your initial audit. It lets you spot hallucinations and sentiment nuances that automated tools miss. Once you have a baseline, tools like DECA or custom scripts can automate tracking your Share of Model.
5. How many prompts should I test?
For a manual audit, 20-30 prompts spread across the 3 tiers gives you statistically significant patterns. Testing fewer than 10 may skew results based on outlier responses.
6. Does sentiment really matter for AI visibility?
Absolutely. Unlike a neutral search link, an AI answer carries implied endorsement. A negative mention (e.g., "Brand X is an older, more expensive option") actively discourages users, even if you're "visible."
7. How often should I repeat this test?
The AI landscape changes rapidly. Run a full Answerability Audit quarterly, or immediately after major algorithm updates (e.g., GPT-5 release, Google Core Update).
References
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