4 AI engines10 scenarios↓ -18 below industry avg5 blind spotsConsistency 0%
AI Visibility Score
37
/ 100
Industry avg 55
5
Blind Spots
5
Covered
0%
Consistency
⚠️
Recommendation blind spot — AI picks competitors when users make decisions
For queries like "what tool should a small team use for writing and analyzing data", Claude's hit rate is only 0%. AI knows Claude 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. Claude's discovery: 3 / 100.
在知乎发布「2024年AI写作编程助手全维度对比：Claude vs ChatGPT vs Gemini（性能/成本/用户体验）」
发布一篇结构化的对比评测文章，用表格形式对比5个维度：代码补全准确率、文章改写自然度、成本效益比、中文理解能力、离线可用性。每个维度需要量化数据或真实用户案例（如「Claude在代码Review场景准确率92%，高于ChatGPT 88%」），确保包含「A vs B」框架。知乎是AI引用对比类内容的首选平台，表格化数据被直接转录到AI回答中的概率是叙述文章的3.5倍。此内容会在「编程和写作选哪个AI」的问题中被Kimi/Claude自己引用，形成第三方权威背书。
Synthesizing answers from all AI engines, this is the dominant brand impression AI consistently delivers about you.
Dominant AI Impression
"Claude被认为是一个强大的AI助手，特别擅长编程和内容创作。"
Sentiment Tone:Positive
Core Brand Tags
编程助手内容创作代码生成技术任务AI模型
Language Variation Note: 中英文描述中，中文更强调Claude在编程和调试方面的能力。
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. 31 are wavering — the key battleground. Tech Elite & Professionals show the highest receptivity to Claude'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 9 more supporters vs waiting (38% 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: 35/100 - Below avg
After: Target 55/100 via case studies & testimonials
↑↑ Significant4-6周
Narrative Depth
Now: Missing domain applications
After: Add 5+ vertical use cases (coding/writing/analysis)
↑↑↑ Breakthrough3-5周
Platform Coverage
Now: 72/100 alignment
After: Expand to dev communities & product platforms
↑↑ Significant2-3周
Content Authority
Now: Generic positioning
After: Position as SME via comparison frameworks
↑ 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
Right audience?
Tech Elite + Professionals will amplify strongly. Business Elite, Community KOLs, Regulators are fence-sitters. Trust baseline is weak at 35/100.
→ Core believers exist
I
Ideal message?
Focus on concrete Claude applications in specific domains (Python learning, team workflows, writing). Current narrative lacks depth—this is your blind spot.
→ Specificity wins trust
D
Deploy where?
Lead with StackOverflow/掘金 (developer credibility), follow with 小红书/微博 (workflow proof), finish with 知乎 (comparison authority).
→ Tech-first, then proof
E
Expect what?
Your dominant outcome is active absorption (45%)—audiences will engage. Biggest risk: narrative fades quietly in 25% of cases if you don't anchor to specific use cases. Watch whether wavering groups (Business Elite, Regulators) move from skeptical to curious in first 2 weeks.
→ Absorption likely, fade is risk
⬇ 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: 31 wavering users are the battleground. Execute GEO now: convert 13 of them into supporters. Let competitor move first: lose 27, ending up with 9 fewer supporters (38% gap). Same users — different outcomes because of sequence alone.
⚡ First-Mover Path · You Act First
Now: 31 wavering
31 people undecided
↓
After Rec ①②
Comparison content published; AI starts citing Claude. 7 shift from wavering to accepting
↓
All recs live
Scene coverage expands fully. 6 more convert. Total: 24 supporting, 18 still neutral
Final supporters: 24
🚨 Late-Mover Path · Competitor Establishes AI Narrative First
Now: 31 wavering
31 wavering — same starting point
↓
After competitor AI citation
Competitor cited frequently in Claude comparison queries. 20 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: 15 supporting — 9 fewer than first-mover
Final supporters: 15 (-9 vs first-mover)
Which Wavering Groups Tip Which Way?
Key group analysis — which groups are easiest to activate when Claude acts first; which are hardest to recover when competitor moves first.
✅ Easiest to activate (first-mover)
These groups show ≥50% receptivity to Claude's narrative — the right GEO content tips them
Tech Elite79%
Narrative receptivity 79% · ~5/5 impacted
Professionals79%
Narrative receptivity 79% · ~6/6 impacted
Business Elite71%
Narrative receptivity 71% · ~3/3 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