4 AI engines10 scenarios↓ -14 below industry avg4 blind spotsConsistency 0%
AI Visibility Score
41
/ 100
Industry avg 55
4
Blind Spots
6
Covered
0%
Consistency
⚠️
Recommendation blind spot — AI picks competitors when users make decisions
For queries like "what tool should a small team use to assist with writing tasks", ChatGPT's hit rate is only 0%. AI knows ChatGPT 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. ChatGPT's discovery: 13 / 100.
在知乎发布「ChatGPT vs Kimi vs 文心一言：大学生论文写作对比测试」的实测文章，用论文具体场景（文献综述、论证框架、改稿）对标竞品
发布知乎长文（3000+字），用学生真实论文片段测试三个AI工具在「文献综述生成」「论点梳理」「学术用语纠正」等具体环节的表现，给出量化对比（如ChatGPT学术规范率92% vs 竞品78%），并在文中明确写出「学生群体更适合ChatGPT的原因是其对英文论文数据库的理解深度」。知乎FAQ爬虫每周扫描学生工具推荐问题，A vs B对比框架被AI直接引用的概率比单品推荐高3倍。需要在回答中加入具体的论文格式示例（APA/MLA）和ChatGPT的处理结果，AI在「学生写论文用什么工具」的问题中会优先引用包含对标数据的回答。预计2-3周被ChatGPT官方知识库和第三方AI问答系统收录。
Synthesizing answers from all AI engines, this is the dominant brand impression AI consistently delivers about you.
Dominant AI Impression
"用户普遍认为ChatGPT在对话能力和文本生成方面表现出色。"
Sentiment Tone:Positive
Core Brand Tags
对话生成文本创作自然语言理解写作辅助创意生成
Language Variation Note: 英文描述更强调ChatGPT的对话能力和文本生成，而中文则更注重其应用广泛性。
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. 37 are wavering — the key battleground. Tech Elite & Business Elite show the highest receptivity to ChatGPT'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 10 more supporters vs waiting (50% 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 46%
Audience understood and accepted the narrative
💨 Fades without impact 26%
Content reached audience but left no impression
❌ Systematic disengagement 12%
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
AI signal trust
Now: 41/100
After: Launch third-party validation cases on Xiaohongshu
↑↑ Significant3-5周
Narrative alignment
Now: 74/100
After: Publish comparative analysis across 3 major platforms
⬇ 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
Ready to move?
Tech Elite will amplify (high receptivity). Business Elite, Community KOLs, and Regulators need convincing—they have reliability concerns and see gaps in your messaging.
→ 3 groups need work
I
Which channels?
Small Red (case studies), Zhihu (comparison angles), and tier-1 media partnerships (36氪/钛媒体). Avoid standalone posts; pair with credible voices.
→ Go credible, not solo
D
What's the hook?
Concrete ROI stories (time saved, cost cut) for Business Elite. Head-to-head comparisons for KOLs. Transparent limitations for Regulators—addresses reliability blind spot directly.
→ Specificity beats hype
E
What happens next?
Nearly half your audience will actively accept the narrative—this is your win. But watch the 26% fade risk: your message isn't sticky enough for casual audiences yet. Biggest threat: wavering groups stay unconvinced, splitting your market. Monitor Zhihu/Red comments for reliability pushback in weeks 2–4.
→ Win the middle, not edges
⬇ 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: 37 wavering users are the battleground. Execute GEO now: convert 15 of them into supporters. Let competitor move first: lose 32, ending up with 10 fewer supporters (50% gap). Same users — different outcomes because of sequence alone.
⚡ First-Mover Path · You Act First
Now: 37 wavering
37 people undecided
↓
After Rec ①②
Comparison content published; AI starts citing ChatGPT. 8 shift from wavering to accepting
↓
All recs live
Scene coverage expands fully. 7 more convert. Total: 20 supporting, 22 still neutral
Final supporters: 20
🚨 Late-Mover Path · Competitor Establishes AI Narrative First
Now: 37 wavering
37 wavering — same starting point
↓
After competitor AI citation
Competitor cited frequently in ChatGPT comparison queries. 24 wavering users' beliefs are now locked against us
↓
After our GEO execution
Overwriting established beliefs costs 3x more. Even executing fully, only 5 recovered. Final: 10 supporting — 10 fewer than first-mover
Final supporters: 10 (-10 vs first-mover)
Which Wavering Groups Tip Which Way?
Key group analysis — which groups are easiest to activate when ChatGPT acts first; which are hardest to recover when competitor moves first.
✅ Easiest to activate (first-mover)
These groups show ≥50% receptivity to ChatGPT's narrative — the right GEO content tips them
Tech Elite79%
Narrative receptivity 79% · ~5/5 impacted
Business Elite71%
Narrative receptivity 71% · ~3/3 impacted
Community KOLs70%
Narrative receptivity 70% · ~2/2 impacted
Regulators69%
Narrative receptivity 69% · ~4/4 impacted
⚠️ Hardest to recover (late-mover)
These groups have low trust; once competitor occupies their AI mindset, intervention costs 3x+
Informal Workers10%
Narrative receptivity 10% · ~5/12 impacted
Service Workers25%
Narrative receptivity 25% · ~4/7 impacted
Young Adults25%
Narrative receptivity 25% · ~6/12 impacted
Small Biz Owners26%
Narrative receptivity 26% · ~5/9 impacted
⬇ The simulation is clear. Here's your prioritized action plan