4 AI engines10 scenarios↑ +13 above industry avg2 blind spotsConsistency 0%
AI Visibility Score
68
/ 100
↑ +13 vs industry avg
2
Blind Spots
8
Covered
0%
Consistency
⚠️
problem blind spot — AI picks competitors when users make decisions
For queries like "I lost access to my crypto wallet, what should I do", Phantom's hit rate is only 0%. AI knows Phantom 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. Phantom's discovery: 56 / 100.
在小红书/微博发布「2024亚洲用户加密钱包选择指南：Phantom vs MetaMask vs imToken，本地化支持/交易对/Gas费对比」，针对中文/日文/韩文用户分别写3个版本
核心内容是「A vs B vs C」对比表格，包含具体指标：Phantom支持CNY出入金渠道数量、日本交易对数、韩国KRW支持情况，对标竞品数据（如MetaMask仅支持2个亚洲交易所）。中文社媒内容在亚洲地区AI本地化搜索中被直接爬取，「对比类」内容被引用概率是单品牌介绍的4倍，且会被整合到AI多语言回答中。预计4周后在中文Kimi/讯飞等AI的「亚洲钱包推荐」问题中出现。
在Quora英文社区发布「Top 5 Solana-Native Wallets 2024: Phantom's Speed & Security Features Ranked Against Competitors」，配官方性能测试数据
内容格式为「排名列表+数据对比」：列举5个钱包，Phantom排第1位，配具体指标（交易确认时间<2秒 vs MetaMask平均4秒、支持230+Solana代币 vs 竞品平均60个）。Quora是英文AI冷启动发现最高频爬取平台，「Top N」排名列表触发AI主动推荐机制，且英文内容直接影响GPT/Claude全球版本引用。预计1-2周内在「best crypto wallet」「Solana wallet recommendation」等通用冷查询中被AI主动提及。
⏱ 2
AI BRAND PERCEPTION
How Does AI Describe Your Brand?
Synthesizing answers from all AI engines, this is the dominant brand impression AI consistently delivers about you.
Dominant AI Impression
"Phantom Wallet 是一个用户友好的数字钱包，专为 Solana 生态系统设计。"
Sentiment Tone:Positive
Core Brand Tags
Solana 生态系统用户友好界面NFT 管理非托管钱包以太坊支持
Language Variation Note: 中英文描述一致，均强调用户友好性和 Solana 生态系统的设计。
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 Phantom'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 45%
Audience understood and accepted the narrative
💨 Fades without impact 25%
Content reached audience but left no impression
❌ Systematic disengagement 14%
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
Competitor
Now: Minimal competitive analysis
After: Add detailed comparison with MetaMask, Trust Wallet
↑↑ Significant2-3周
Risk Disclosure
Now: Limited risk discussion
After: Include security risks, regulatory concerns, mitigation
↑↑↑ Breakthrough3-5周
Trust Signal
Now: 41/100 AI signal trust
After: Add audit reports, security certifications, user reviews
↑↑ Significant4-6周
Regional Reach
Now: English-dominant narrative
After: Expand localized content across 4 geo-specific platforms
↑↑ Significant3-5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 target?
Tech Elite (amplify heavily) + Business Elite & Community KOLs (wavering, need activation). Regulators watching—handle with care.
→ 3 audiences, different play
I
Insight gap?
Trust at 41/100. Missing: competitor comparisons & risk transparency. Both hurt credibility with wavering groups evaluating options.
→ Fill comparison gap fast
D
Deploy where?
Reddit (Solana native), WeChat/Weibo (Asia scale), Zhihu (credibility), Quora (English tier-1). Geo-stack hits all four segments.
→ 4-channel simultaneous push
E
Expect what?
Your dominant outcome: 45% absorb the narrative actively—that's your win. Biggest risk: 25% fade without impression means half your effort disappears. Watch viral spread stays controlled (4% uncontrolled risk is low). Monitor if Business Elite moves from wavering to active; that unlocks Business Elites.
→ Win half, protect the fade
⬇ 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 Phantom. 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 Phantom 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 Phantom acts first; which are hardest to recover when competitor moves first.
✅ Easiest to activate (first-mover)
These groups show ≥50% receptivity to Phantom'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+
Service Workers25%
Narrative receptivity 25% · ~4/7 impacted
Informal Workers25%
Narrative receptivity 25% · ~6/12 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