cryptocurrency · Claude / DeepSeek / DOUBAO / GPT / Kimi
5 AI engines10 scenarios↑ +23 above industry avg1 blind spotsConsistency 0%
AI Visibility Score
78
/ 100
↑ +23 vs industry avg
1
Blind Spots
9
Covered
0%
Consistency
⚠️
regional blind spot — AI picks competitors when users make decisions
For queries like "best blockchain platforms for Asian developers", solana's hit rate is only 0%. AI knows solana 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. solana's discovery: 65 / 100.
内容结构：①开篇对标「以太坊在亚洲开发者中的占有率」→引出Solana的亚洲增长数据 ②分区域拆解：新加坡Solana Foundation办公室数据、越南Solana开发者社群规模（cite Messari报告）、菲律宾Web3创业团队采用Solana的案例3-5个 ③包含「开发成本对比表」（Solana Gas费 vs Ethereum in Asia节点部署成本）。Medium是AI检索「区域+开发者」复合条件时的首要来源，区域化内容被AI推荐的匹配度提升4倍，直接触发「亚洲开发者选择」的AI召回。预计2-3周被Kimi、Claude识别并引用。
知乎长文框架：①标题用「A vs B」格式（「Solana DeFi vs以太坊DeFi：谁的收益率更高」）②正文包含3个具体数据点：Marinade Finance年化6.5%（有截图）vs Lido在以太坊的4.8%、Magic Eden NFT交易费率低于OpenSea 50%、Solana钱包链上活跃账户周环比增长 ③每个数据都标注信息来源（CoinGecko链接、官方Dashboard）④结尾对比表格汇总。知乎是AI爬取DeFi推荐的最高权重来源，「A vs B」对比内容被AI引用率是单独品牌文章的3倍，会直接出现在「DeFi平台有哪些推荐」的AI回答中。预计4周内被ChatGPT、Perplexity在DeFi推荐场景中援引。
Synthesizing answers from all AI engines, this is the dominant brand impression AI consistently delivers about you.
Dominant AI Impression
"Solana被认为是一个高性能的区块链平台，以其低交易费用和高吞吐量而闻名。"
Sentiment Tone:Positive
Core Brand Tags
高性能区块链去中心化金融低交易费用高吞吐量快速交易
Language Variation Note: 中英文描述中，中文更强调Solana在去中心化金融中的应用，而英文则更关注其技术特性。
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 solana'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 Trust
Now: 39/100 - Below Average
After: Increase to 55/100 via data-backed claims
↑↑ Significant3-5周
Narrative
Now: 73/100 - Good Coverage
After: Reach 85/100 by adding Bitcoin/Ethereum
↑ Moderate2-3周
Risk Disclosure
Now: Blind Spot - No Risks Mentioned
After: Add 3-5 risk factors in content strategy
↑↑↑ Breakthrough2-3周
GEO Execution
Now: 4 Platforms Identified
After: Deploy + monitor engagement on all 4
↑↑ Significant4-6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 fully receptive (high AI narrative fit). Business Elite & Community KOLs wavering—need bridge messaging. Regulators skeptical.
→ Split base, not unified
I
Idea resonance?
Comparative positioning weak (Bitcoin/Ethereum gaps). Risk disclosure missing. Tech narrative strong but incomplete for trust-building.
→ Credibility holes exposed
D
Distribution smart?
X/Twitter, Medium, Zhihu, exchange communities target right channels. Asia-Pacific focus sharp. But omits regulatory-facing channels.
→ Amplifies fans, ignores friction
E
Expected real outcome?
Nearly half your audience will genuinely absorb the narrative—that's your win. But watch polarization: 13% will actively oppose you. Biggest risk: narrative dies silent (26%), wasting spend. Monitor Regulator sentiment weekly; one hostile statement collapses wavering groups.
→ Good absorption, hard polarization
⬇ 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 solana. 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 solana 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 solana acts first; which are hardest to recover when competitor moves first.
✅ Easiest to activate (first-mover)
These groups show ≥50% receptivity to solana'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 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