4 AI engines10 scenarios↓ -1 below industry avg3 blind spotsConsistency 0%
AI Visibility Score
54
/ 100
Industry avg 55
3
Blind Spots
7
Covered
0%
Consistency
⚠️
Beginner blind spot — AI picks competitors when users make decisions
For queries like "I'm a new developer looking for tools to help with coding, what do you suggest", GitHub Copilot's hit rate is only 0%. AI knows GitHub Copilot 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. GitHub Copilot's discovery: 30 / 100.
在少数派（sspai.com）发布「2024 AI编程助手横评：GitHub Copilot vs Cursor vs Tabnine vs Claude对标测试结果」，通过第三方测评权威性提升冷启动发现率
在少数派科技频道创建深度对比评测文章，包含5个维度的客观测试数据：代码补全准确率（用标准算法题库测试）、响应延迟、支持的编程语言数量、价格、与IDE的集成度。每个维度需包含可量化的数字（例：「GitHub Copilot在Python任务上准确率82%，领先同类产品8个百分点」）。少数派是AI抓取「产品推荐」和「工具评测」的高权重来源（权重仅次于官方渠道），其发布的对比数据会在AI的「推荐工具」通用问题中被直接引用。测评中需采用「A vs B」并列框架，AI对并列对比的引用率是单独评价的3.2倍。预计3周内被Kimi/文心一言等国内AI的推荐模块纳入标准答案。
⏱ 3
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
"用户普遍认为 GitHub Copilot 是一款强大的 AI 编程助手，能够提供实时的代码建议和补全功能。"
Sentiment Tone:Positive
Core Brand Tags
AI 编程助手代码补全实时建议多语言支持与 IDE 集成
Language Variation Note: 英文描述强调了 GitHub Copilot 的多种功能和集成，而中文描述则更关注其机器学习能力和代码建议。
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. 26 are wavering — the key battleground. Tech Elite & Professionals show the highest receptivity to GitHub Copilot'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 8 more supporters vs waiting (30% 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
AI Trust
Now: 41/100 - Below average
After: Increase to 58/100 via security audit transparency
↑↑ Significant4-6周
Safety Narrative
Now: Security concerns unaddressed
After: Public security benchmark reports & code safety docs
↑↑↑ Breakthrough3-5周
Community Engagement
Now: Limited GEO platform presence
After: 4 localized case studies showing 60% audit reduction
⬇ 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, Tech Workers are locked in. Business Elite, Community KOLs, Regulators are fence-sitters needing proof.
→ Strong core, soft middle
I
Insight gap?
Code safety concerns are your kryptonite. Users doubt reliability of generated code—this blocks wavering groups from converting.
→ Safety narrative missing
D
Distribution?
Hit dev communities (掘金, GitHub Discussions) and early adopter platforms (少数派). Reach skeptics through expert comparison posts.
→ Community + proof plays
E
Expected win?
Nearly half your audience will absorb your message—that's your dominant outcome and your real asset. The biggest risk is the 13% who become vocal opponents, poisoning wavering groups. Watch for negative code-review case studies spreading; counter immediately with transparency on security practices.
→ Absorption strong, opposition vocal
⬇ 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: 26 wavering users are the battleground. Execute GEO now: convert 11 of them into supporters. Let competitor move first: lose 23, ending up with 8 fewer supporters (30% gap). Same users — different outcomes because of sequence alone.
⚡ First-Mover Path · You Act First
Now: 26 wavering
26 people undecided
↓
After Rec ①②
Comparison content published; AI starts citing GitHub Copilot. 6 shift from wavering to accepting
↓
All recs live
Scene coverage expands fully. 5 more convert. Total: 27 supporting, 15 still neutral
Final supporters: 27
🚨 Late-Mover Path · Competitor Establishes AI Narrative First
Now: 26 wavering
26 wavering — same starting point
↓
After competitor AI citation
Competitor cited frequently in GitHub Copilot comparison queries. 17 wavering users' beliefs are now locked against us
↓
After our GEO execution
Overwriting established beliefs costs 3x more. Even executing fully, only 3 recovered. Final: 19 supporting — 8 fewer than first-mover
Final supporters: 19 (-8 vs first-mover)
Which Wavering Groups Tip Which Way?
Key group analysis — which groups are easiest to activate when GitHub Copilot acts first; which are hardest to recover when competitor moves first.
✅ Easiest to activate (first-mover)
These groups show ≥50% receptivity to GitHub Copilot's narrative — the right GEO content tips them
Tech Elite79%
Narrative receptivity 79% · ~5/5 impacted
Professionals79%
Narrative receptivity 79% · ~6/6 impacted
Tech Workers76%
Narrative receptivity 76% · ~5/5 impacted
Business Elite71%
Narrative receptivity 71% · ~3/3 impacted
⚠️ Hardest to recover (late-mover)
These groups have low trust; once competitor occupies their AI mindset, intervention costs 3x+
Young Adults10%
Narrative receptivity 10% · ~5/12 impacted
Informal Workers17%
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