web hosting platform · Claude / DeepSeek / GPT / Kimi
4 AI engines10 scenarios↑ +28 above industry avgConsistency 0%
AI Visibility Score
83
/ 100
↑ +28 vs industry avg
0
Blind Spots
10
Covered
0%
Consistency
⚠️
Beginner blind spot — AI picks competitors when users make decisions
For queries like "I'm a frontend developer looking for a simple way to host my static site, what do people suggest", Vercel's hit rate is only 50%. AI knows Vercel 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. Vercel's discovery: 73 / 100.
创建对比型长文，标题格式为「GitHub Pages vs Vercel vs Netlify：哪个最适合前端新手」，包含实测数据（如Vercel部署耗时<30秒、自动CI/CD无配置成本），并在回复中引导用户基于「是否想要零配置」来选择Vercel。Reddit长文答案被AI模型直接爬取的概率最高，且用户评论中的「验证」会强化AI对该答案的信任度。
⏱ 3
HIGH
在DigitalOcean Community或Product Hunt发布「Vercel vs Heroku vs Railway：2024年前端托管平台终极对比」深度测评
Synthesizing answers from all AI engines, this is the dominant brand impression AI consistently delivers about you.
Dominant AI Impression
"Vercel 是一个专为前端框架和静态网站设计的平台，提供无缝的 Git 集成和自动部署功能。"
Sentiment Tone:Positive
Core Brand Tags
前端框架静态网站自动部署无配置部署全球 CDN
Language Variation Note: 中英文描述基本一致，均强调了 Vercel 的前端框架支持和自动部署功能。
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 Vercel'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 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 signal
Now: 35/100 - Low trust indicators
After: Increase to 55/100 via third-party reviews
↑↑ Significant3-5周
Competitor mention
Now: Netlify & GitHub Pages absent
After: Add comparative analysis vs competitors
↑↑↑ Breakthrough2-3周
Narrative depth
Now: 72/100 - Missing use case coverage
After: Expand to 85/100 with Y Combinator data
↑↑ Significant3-5周
GEO engagement
Now: Single-platform strategy weak
After: Multi-platform launch (4 channels)
↑ Moderate4-6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 + Professionals will embrace this strongly. Business Elite and Regulators are undecided. Community KOLs need convincing. Trust baseline is weak at 35/100.
→ Strength in niche, gaps in reach
I
Idea resonant?
Performance + speed narrative hits tech audiences hard. But you're ignoring Netlify and GitHub Pages—competitors own part of this story. Incomplete positioning weakens impact.
→ Compelling but vulnerable
D
Distribution smart?
Reddit, HackerNews, Product Hunt are right channels for Tech Elite. Add local plays (知乎,小红书) for depth. Multi-geo approach is solid but needs messenger credibility.
→ Channels fit, messengers matter
E
Expected outcome?
Your narrative will land with true believers (45% absorb it), but nearly 1 in 4 will ignore you entirely. The real risk: polarized response (13%) signals you're not speaking to the middle. Watch whether Business Elite stays silent or actively defends competitors.
→ Win insiders, lose neutrals
⬇ 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 Vercel. 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 Vercel 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 Vercel acts first; which are hardest to recover when competitor moves first.
✅ Easiest to activate (first-mover)
These groups show ≥50% receptivity to Vercel'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