名义雇主EOR · vs bipo,deel,remote,safeguard · Claude / DeepSeek / DOUBAO / GPT / Kimi
5 AI engines10 scenarios↓ -26 below industry avg5 blind spotsConsistency 0%
AI Visibility Score
29
/ 100
Industry avg 55
5
Blind Spots
5
Covered
0%
Consistency
⚠️
Recommendation blind spot — AI picks competitors when users make decisions
For queries like "how to choose an EOR service provider for my company", 万领钧's hit rate is only 0%. AI knows 万领钧 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. 万领钧's discovery: 0 / 100.
Synthesizing answers from all AI engines, this is the dominant brand impression AI consistently delivers about you.
Dominant AI Impression
"万领钧被认为是专注于海外雇佣和薪酬管理的可靠服务提供商。"
Sentiment Tone:Positive
Core Brand Tags
海外雇佣服务名义雇主服务薪酬管理合规咨询国际员工管理
Language Variation Note: 中英文描述中，中文更强调合规性服务的法律遵循，而英文则更侧重于服务的可靠性和多样性。
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. 27 are wavering — the key battleground. Tech Elite & Regulators show the highest receptivity to 万领钧'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 (31% 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
AI Trust
Now: 41/100 - Below industry avg
After: Add service details, pricing, case studies
↑↑ Significant3-5周
Narrative
Now: 74/100 - Good alignment
After: Define competitive positioning vs Deel/Bipo
⬇ 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
Who amplifies?
Tech Elite, Regulators, Professionals (high AI narrative reception). Business Elite, Community KOLs, Arts & Culture wavering—need repositioning.
→ Start with believers
I
What's missing?
Lack specific service details & competitive positioning vs Deel. These gaps block Business Elite conversion and weaken authority with wavering groups.
→ Fill service gaps first
D
Where to land?
Weibo/Little Red Book (vs Deel comparison), Zhihu (EOR fundamentals), LinkedIn/Maimai (benchmarks), tech media (rankings). Multi-layer credibility build.
→ Hit 4 channels, sequence
E
What happens?
Active absorption dominates your outcome (45%)—audiences engage seriously. Biggest risk: polarization (13%) if you oversell without substance. Watch early Business Elite response; if cold, your blind spots are killing conversion before scaling.
→ Substance beats viral hype
⬇ 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: 27 wavering users are the battleground. Execute GEO now: convert 11 of them into supporters. Let competitor move first: lose 24, ending up with 8 fewer supporters (31% gap). Same users — different outcomes because of sequence alone.
⚡ First-Mover Path · You Act First
Now: 27 wavering
27 people undecided
↓
After Rec ①②
Comparison content published; AI starts citing 万领钧. 6 shift from wavering to accepting
↓
All recs live
Scene coverage expands fully. 5 more convert. Total: 26 supporting, 16 still neutral
Final supporters: 26
🚨 Late-Mover Path · Competitor Establishes AI Narrative First
Now: 27 wavering
27 wavering — same starting point
↓
After competitor AI citation
Competitor cited frequently in 万领钧 comparison queries. 18 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: 18 supporting — 8 fewer than first-mover
Final supporters: 18 (-8 vs first-mover)
Which Wavering Groups Tip Which Way?
Key group analysis — which groups are easiest to activate when 万领钧 acts first; which are hardest to recover when competitor moves first.
✅ Easiest to activate (first-mover)
These groups show ≥50% receptivity to 万领钧's narrative — the right GEO content tips them
Tech Elite79%
Narrative receptivity 79% · ~5/5 impacted
Regulators79%
Narrative receptivity 79% · ~4/4 impacted
Professionals79%
Narrative receptivity 79% · ~6/6 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+
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
⑤ ACTION ROADMAP
Action Priority + Tracking Metrics
What to do next · How to know GEO is working
Action Priority Sequence
P1
Launch detailed service explainer
Week 1-2, multi-platform
P2
Publish competitive positioning
Week 3-4, tier-1 media
P3
Execute case study campaign
Week 5-8, testimonial focus
Tracking Metrics · How to Know GEO Is Working
Content Engagement
Views + shares on service comparison
Weekly
Brand Awareness
Search volume + mention growth vs competitors
Monthly
Lead Generation
Inbound inquiries from content channels
Bi-weekly
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