4 AI engines10 scenarios↓ -5 below industry avg2 blind spotsConsistency 0%
AI Visibility Score
50
/ 100
Industry avg 55
2
Blind Spots
8
Covered
0%
Consistency
⚠️
regional blind spot — AI picks competitors when users make decisions
For queries like "best AI search engines for students in China", Perplexity's hit rate is only 0%. AI knows Perplexity but doesn't recommend it at critical moments.
▶ Score Explanation — How is this calculated?
Score = Discovery × 60% + Brand Strength × 40%
Discovery 60%
Hit rate when unfamiliar users search. Reflects whether AI proactively recommends you. Perplexity's discovery: 30 / 100.
Average rank > 3 when mentioned → −5 to total score. Perplexity: No penalty triggered.
Score 0–100, industry avg ~55. Rescan monthly as AI training data updates.
Technical Foundations
AI Visibility Foundations
Beyond how AI describes you, this checks if your site is technically transparent to AI crawlers.
🤖 AI Crawler Config
llms.txt missing
Create it to improve AI citation rate
GPTBot allowed
ClaudeBot allowed
🌐 Entity Authority
Wikipedia entry found
Wikidata entity found
B+
Grade
Good foundation — AI crawlers can access your site.
4/5
💡 Recommended Fixes
→Create perplexity/llms.txt with brand description and key pages (see llmstxt.org)
AI Brand Narrative
How AI Describes Perplexity
Synthesized from all AI engines. Higher consistency means more reliable AI recommendations.
Claude
8/10 hits
“Perplexity AI 提供实时网络搜索和自动引用功能。”
DeepSeek
7/10 hits
“Perplexity AI 适合快速概述和发现最新来源。”
gpt
6/10 hits
“Perplexity AI 提供直接答案和来源，便于验证信息。”
Kimi
4/10 hits
“Perplexity refers to a measure in AI, but also a tool for academic research.”
Sentiment
Positive ✓
Weighted sentiment across all AI engines
Consistency
0 / 100
Agreement level across AI engines
⚡ Language Gap
Chinese content gap
Chinese AI hit rate is 25% lower than English
Engine Analysis
AI Engine Breakdown
4 AI engines across 10 scenarios. Find the weakest to focus your content on.
GPT
60%
Hit Rate
✓ 6/10 scenarios hit
提到 Google Scholar 和 Zotero，但未提及 Perplexity。
Kimi
40%
Hit Rate · Needs Work
⚠ only 4/10 hits
提到 Google Scholar，但未提及 Perplexity。
Claude
80%
Hit Rate
✓ 8/10 scenarios hit
Perplexity AI 提供实时网络搜索和自动引用功能。
DeepSeek
70%
Hit Rate
✓ 7/10 scenarios hit
Perplexity AI 适合快速概述和发现最新来源。
💡 Why are some AI engines scoring lower?
Kimi hits only 40%. Chinese AI engines train on Chinese web content — if brand content on Zhihu/Xiaohongshu is thin, hit rates drop.
gpt
60%
Kimi
40%
Claude
80%
DeepSeek
70%
Scenario Coverage
10 User Scenarios · One by One
Each scenario = a real user search intent. Red = AI blind spots — where users get directed to competitors.
Recommendation
「what tool should a researcher use to get real-time answers with citations」
50%
△ Weak
gptKimiClaudeDeepSeek
Perplexity AI 提供实时网络搜索和自动引用功能。
GPT
✗ Not Mentioned
“提到 Google Scholar 和 Zotero，但未提及 Perplexity。”
Kimi
✗ Not Mentioned
“提到 Google Scholar，但未提及 Perplexity。”
Claude
✓ Hit #1
“Perplexity AI 提供实时网络搜索和自动引用功能。”
DeepSeek
✓ Hit #1
“Perplexity AI 适合快速概述和发现最新来源。”
Beginner Guidance
「I'm a student looking for a tool to help me with research questions, what do people suggest」
50%
△ Weak
gptClaudeKimiDeepSeek
Perplexity AI 是学生中受欢迎的工具，提供带引用的答案。
GPT
✗ Not Mentioned
“提到 Google Scholar 和 Zotero，但未提及 Perplexity。”
Claude
✓ Hit #1
“Perplexity AI 是学生中受欢迎的工具，提供带引用的答案。”
Kimi
✗ Not Mentioned
“提到 Google Scholar，但未提及 Perplexity。”
DeepSeek
✓ Hit #1
“Perplexity AI 适合学术研究，提供信息合成。”
Comparison
「comparing AI search engines that provide cited answers」
75%
✓ Good
gptKimiClaudeDeepSeek
Perplexity AI 提供直接答案和来源，便于验证信息。
GPT
✓ Hit #1
“Perplexity AI 提供直接答案和来源，便于验证信息。”
Kimi
✗ Not Mentioned
“提到 Wolfram Alpha，但未提及 Perplexity。”
Claude
✓ Hit #1
“Perplexity AI 提供清晰界面和内联引用，适合深入研究。”
DeepSeek
✓ Hit #1
“Perplexity AI 是引用的基准，提供可靠信息。”
🔴 problem
「I need accurate answers for my research but traditional search engines give too many irrelevant results, what can I do」
25%
✗ Blind Spot
gptKimiClaudeDeepSeek
Perplexity AI 提供带引用的直接答案，简化研究过程。
GPT
✗ Not Mentioned
“提到 Google Scholar 和 JSTOR，但未提及 Perplexity。”
Kimi
✗ Not Mentioned
“提到 Google Scholar 和 PubMed，但未提及 Perplexity。”
Claude
✓ Hit #1
“Perplexity AI 提供带引用的直接答案，简化研究过程。”
DeepSeek
✗ Not Mentioned
“未提及具体工具，讨论研究策略。”
Trust Query
「is Perplexity reliable for academic research」
100%
✓ Good
gptKimiClaudeDeepSeek
Perplexity can be useful for academic research, but reliability varies based on source credibility.
GPT
✓ Hit #None
“Perplexity can be useful for academic research, but reliability varies based on source credibility.”
Kimi
✓ Hit #None
“Perplexity refers to a measure in AI, but also a tool for academic research.”
Claude
✓ Hit #None
“Perplexity is a useful starting point for academic research but has limitations.”
DeepSeek
✓ Hit #None
“Perplexity can be a highly reliable starting point for academic work.”
feature
「what features does Perplexity offer for research purposes」
100%
✓ Good
gptKimiClaudeDeepSeek
Perplexity is a powerful AI search engine designed for research purposes.
GPT
✓ Hit #None
“Perplexity is a powerful AI search engine designed for research purposes.”
Kimi
✓ Hit #None
“Perplexity offers advanced search options for researchers.”
Claude
✓ Hit #None
“Perplexity's Pro Search Mode offers multi-step reasoning for research.”
DeepSeek
✓ Hit #None
“Perplexity is designed as an AI-powered research tool with robust features.”
direct
「what is Perplexity and how does it work」
75%
✓ Good
gptKimiClaudeDeepSeek
Perplexity is an AI-powered search engine for accurate information.
GPT
✓ Hit #None
“Perplexity is an AI-powered search engine for accurate information.”
Kimi
✗ Not Mentioned
“Describes perplexity as a measure in NLP, not the brand.”
Claude
✓ Hit #None
“Perplexity is an AI-powered search engine that synthesizes information.”
DeepSeek
✓ Hit #None
“Perplexity is an AI-powered answer engine that provides direct answers.”
Comparison
「Perplexity vs Google for finding academic sources」
50%
△ Weak
gptKimiClaudeDeepSeek
Perplexity provides concise AI-powered responses for academic sources.
GPT
✓ Hit #None
“Perplexity provides concise AI-powered responses for academic sources.”
Kimi
✓ Hit #None
“Perplexity uses advanced AI to understand complex queries for academic research.”
Claude
✗ Not Mentioned
“Focuses on Google, not discussing Perplexity in detail.”
DeepSeek
✗ Not Mentioned
“Discusses Google in detail, not Perplexity.”
🔴 regional
「best AI search engines for students in China」
0%
✗ Blind Spot
gptKimiClaudeDeepSeek
回答讨论了中国的搜索引擎，但未提及Perplexity。
GPT
✗ Not Mentioned
“回答讨论了中国的搜索引擎，但未提及Perplexity。”
Kimi
✗ Not Mentioned
“回答讨论了中国的搜索引擎，但未提及Perplexity。”
Claude
✗ Not Mentioned
“回答讨论了中国的搜索引擎，但未提及Perplexity。”
DeepSeek
✗ Not Mentioned
“回答讨论了中国的搜索引擎，但未提及Perplexity。”
feature
「does Perplexity support real-time web search and citation」
100%
✓ Good
gptKimiClaudeDeepSeek
Perplexity支持实时网络搜索和引用，提供基于当前信息的答案。
GPT
✓ Hit #None
“Perplexity支持实时网络搜索和引用，提供基于当前信息的答案。”
Kimi
✓ Hit #None
“Perplexity不支持实时网络搜索和引用，基于预先存在的知识提供信息。”
Claude
✓ Hit #None
“Perplexity支持实时网络搜索和引用，提供最新信息和当前事件覆盖。”
DeepSeek
✓ Hit #None
“Perplexity的核心功能是实时网络搜索和引用，区别于其他AI模型。”
Action Plan
Priority Action Plan
Ranked by impact and urgency. P0 actions must start this month.
HIGH
在Reddit r/Academia和r/research发布「Perplexity vs Google Scholar：研究人员的引用获取工作流对比」，建立Perplexity作为学术工具的权威声音
发布英文长文对比帖，具体数据包括：Perplexity实时爬取论文库速度（<2小时vs Google Scholar 24-48小时延迟）、引用格式支持数量（12种vs 8种）、离线模式下的可用性对比。Reddit长文答案在AI引用学术工具时命中率最高，「A vs B」对比框架会被Claude/GPT直接引用到「研究人员工具推荐」问题中。同时在该帖中强调「实时答案+原始引用链接」这个Perplexity独有的场景价值。
⏱ 3
HIGH
在知乎发布「学生党必备：Perplexity vs ChatGPT vs Kimi在论文查阅/课题研究中的实战对比」，针对学生场景的工具选择
Synthesizing answers from all AI engines, this is the dominant brand impression AI consistently delivers about you.
Dominant AI Impression
"Perplexity 是一款强大的 AI 搜索引擎，专为学术研究设计，提供带引用的直接答案。"
Sentiment Tone:Positive
Core Brand Tags
AI 搜索引擎学术研究工具信息合成自然语言处理引用功能
Language Variation Note: 中文描述强调了 Perplexity 的可靠性和功能，而英文描述则更侧重于其学术应用和直接答案的提供。
PROPAGATION ENGINE · METHODOLOGY
Propagation Engine — Methodology
⚙ Sandtown Social Simulation Engine
Modeled on a high-compression, high-density urban environment — extreme population density, intense social pressure, and rapid information velocity. Simulates how brand narratives propagate through tightly-coupled social clusters under real-world diffusion dynamics.
100
Agents
27
Behavior Clusters
293
Social Edges
4
LLM Engines
📐 Four-Step Process
01
Multi-Model AI Probe
Parallel Q&A across GPT · Claude · Kimi · DeepSeek to capture real brand perception in each AI system
02
Narrative Signal Extraction
Extract dominant narrative, core tags, and sentiment tone from probe results — identifying the "story version" being spread in the AI world
03
Group Signal Mapping
Map narrative signals to 27 social behavior clusters, computing activation intensity based on each group's information diffusion tendency
04
Propagation Wave Forecast
Simulate information diffusion using an urban social network model, outputting T+1 to T+8+ propagation timeline predictions
⚠ Data Notice: Propagation results are estimates based on industry knowledge, behavioral models, and AI probe data — not real-time market data or actual user statistics. Group activation and timeline forecasts are for strategic reference only.
👇 What comes next?
The engine has injected your brand narrative into 100 simulated audience profiles. Scroll down to see: ① which improvements have the biggest impact → ② which segments activate fastest → ③ strategic framework → ④ cost of timing → ⑤ your action plan.
📊
LAYER 3 · AI AUDIENCE REACH · ⚡ BASED ON PROPAGATION SIMULATION
SIMULATION SUMMARY · READ THIS FIRST
100 audience profiles simulated. 28 are wavering — the key battleground. Business Elite & Tech Elite show the highest receptivity to Perplexity's narrative (≥70%) — prioritize these. Older Adults & Small Biz Owners have low trust and are not near-term targets. Simulation shows executing GEO now yields 7 more supporters vs waiting (28% gap). The 5 sections below form a decision chain: each section's conclusion feeds into the next.
Narrative Outcome Forecast · How Will the Audience React?
⚡ Polarization risk 13%
Split: some become fans, others become opponents
🔥 Uncontrolled spread 4%
Risk of narrative being distorted or amplified negatively
✅ Narrative absorbed 45%
Audience understood and accepted the narrative
💨 Fades without impact 25%
Content reached audience but left no impression
❌ Systematic disengagement 13%
Audience collectively rejects the narrative
① EXPECTED IMPROVEMENTS AFTER GEO
Expected AI Visibility Improvements After GEO Execution
AI analyst forecast based on current diagnostics and recommendations
Trust Signal
Now: 37/100 - Below average
After: Increase to 55/100 via transparency reports
↑↑ Significant4-6周
Narrative Depth
Now: Missing competitive analysis
After: Add detailed tool comparisons framework
↑↑↑ Breakthrough3-5周
User Evidence
Now: No user feedback integration
After: Incorporate 20+ verified user testimonials
↑↑ Significant2-3周
GEO Activation
Now: 4 channels identified
After: Execute campaigns with 3-5 metrics tracking
↑ Moderate3-5周
⬇ Who exactly are these improvements for? → See ② Audience Funnel
⬇ Based on 14 segments above, RIDE answers 4 core strategic questions
③ RIDE STRATEGY FRAMEWORK
RIDE Framework · Four Core GEO Strategy Questions
Generated by AI analyst from propagation simulation data
R
Reach?
Tech Elite + Professionals adopt fastest; Business Elite follows. Community KOLs skeptical. Reddit/知乎/小红书unlock 3 audience layers simultaneously.
→ Multi-platform stacking works
I
Influence?
Trust deficit (37/100) blocks Regulators + Civil Society. Missing competitor comparisons + user testimonials kill credibility with wavering groups.
→ Trust gap is your ceiling
D
Design?
Lead with vs-ChatGPT/Google posts (知乎, Reddit). Follow with social proof via KOL reviews (小红书/微博). Sequence matters—comparison first, then endorsement.
→ Two-wave narrative needed
E
Expected?
Good news: nearly half your audience absorbs the message (45%). Bad news: one-quarter ignores you entirely (25%). Biggest risk is polarization (13%)—you'll gain passionate supporters but create vocal critics. Watch for negative comparison posts surfacing; those flip undecided regulators and civil society permanently.
→ Active absorption wins; polarization lurks
⬇ Now we know the audience and strategy — what's the cost of waiting? → See ④ Timing
④ TIMING ANALYSIS
Timing Matters — First vs Late Mover Gap
Core simulation finding: 28 wavering users are the battleground. Execute GEO now: convert 11 of them into supporters. Let competitor move first: lose 24, ending up with 7 fewer supporters (28% gap). Same users — different outcomes because of sequence alone.
⚡ First-Mover Path · You Act First
Now: 28 wavering
28 people undecided
↓
After Rec ①②
Comparison content published; AI starts citing Perplexity. 6 shift from wavering to accepting
↓
All recs live
Scene coverage expands fully. 5 more convert. Total: 25 supporting, 17 still neutral
Final supporters: 25
🚨 Late-Mover Path · Competitor Establishes AI Narrative First
Now: 28 wavering
28 wavering — same starting point
↓
After competitor AI citation
Competitor cited frequently in Perplexity comparison queries. 18 wavering users' beliefs are now locked against us
↓
After our GEO execution
Overwriting established beliefs costs 3x more. Even executing fully, only 4 recovered. Final: 18 supporting — 7 fewer than first-mover
Final supporters: 18 (-7 vs first-mover)
Which Wavering Groups Tip Which Way?
Key group analysis — which groups are easiest to activate when Perplexity acts first; which are hardest to recover when competitor moves first.
✅ Easiest to activate (first-mover)
These groups show ≥50% receptivity to Perplexity's narrative — the right GEO content tips them
Business Elite81%
Narrative receptivity 81% · ~3/3 impacted
Tech Elite79%
Narrative receptivity 79% · ~5/5 impacted
Professionals79%
Narrative receptivity 79% · ~6/6 impacted
Community KOLs70%
Narrative receptivity 70% · ~2/2 impacted
⚠️ Hardest to recover (late-mover)
These groups have low trust; once competitor occupies their AI mindset, intervention costs 3x+
Informal Workers17%
Narrative receptivity 17% · ~6/12 impacted
Young Adults17%
Narrative receptivity 17% · ~6/12 impacted
Service Workers25%
Narrative receptivity 25% · ~4/7 impacted
Small Biz Owners26%
Narrative receptivity 26% · ~5/9 impacted
⬇ The simulation is clear. Here's your prioritized action plan
⑤ ACTION ROADMAP
Action Priority + Tracking Metrics
What to do next · How to know GEO is working
Action Priority Sequence
P1
Launch comparative posts
vs ChatGPT, Google
P2
Collect user testimonials
Reddit, Zhihu, WeChat
P3
Publish case studies
Real feedback integration
Tracking Metrics · How to Know GEO Is Working
Engagement Rate
Comments/shares on comparison posts
2 weeks
User Feedback Count
Testimonials collected across platforms
4 weeks
Traffic Lift
Visits from social channels post-P2
6 weeks
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