5 AI engines10 scenarios↓ -29 below industry avg5 blind spotsConsistency 0%
AI Visibility Score
26
/ 100
Industry avg 55
5
Blind Spots
5
Covered
0%
Consistency
⚠️
Recommendation blind spot — AI picks competitors when users make decisions
For queries like "what tool should a small business use to test their internet speed", RateTest's hit rate is only 0%. AI knows RateTest 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. RateTest's discovery: 0 / 100.
Language Variation Note: 英文描述中提到 RateTest 的功能和用户反馈较多，而中文描述则相对简略，缺乏具体细节。
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. 35 are wavering — the key battleground. Tech Elite & Professionals show the highest receptivity to RateTest'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 (43% 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: 40/100 - Below average
After: Execute geo-targeted content strategy across 4 platforms
↑↑ Significant4-6周
Narrative Align
Now: 73/100 - Good alignment
After: Add feature comparisons & use cases to close blind spots
↑ Moderate2-3周
Feature Clarity
Now: Insufficient user understanding
After: Launch detailed feature guides on Xiaohongshu & CSDN forums
↑↑↑ Breakthrough3-5周
Competitive Position
Now: RateTest advantages unclear
After: Publish comparative analysis on Zhihu & Reddit with USPs
↑↑ 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
Who moves needle?
Tech Elite & Professionals embrace AI narratives (amplifiers). Business Elite, Community KOLs, Regulators are fence-sitters needing direct value proof.
→ Stack amplifiers first
I
What's the gap?
Users don't know what RateTest actually does. They see Speedtest first and don't understand RateTest's real advantages. Credibility sits at 40/100.
→ Feature clarity wins
D
Where to land?
Hit Xiaohongshu with SMB diagnostic angle, Zhihu with speed-test how-tos, CSDN/V2EX with 2024 tool comparisons, Reddit tech communities for peer validation.
→ Platform-native, not broadcast
E
What actually happens?
Your best case: 45% absorb the message and become informed users. Your biggest risk: 25% ignore you entirely, leaving RateTest unknown. Watch for polarization (13%) — if debate erupts, jump in fast with clear feature comparisons before opponents own the narrative.
→ Engagement beats reach here
⬇ 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: 35 wavering users are the battleground. Execute GEO now: convert 14 of them into supporters. Let competitor move first: lose 30, ending up with 9 fewer supporters (43% gap). Same users — different outcomes because of sequence alone.
⚡ First-Mover Path · You Act First
Now: 35 wavering
35 people undecided
↓
After Rec ①②
Comparison content published; AI starts citing RateTest. 8 shift from wavering to accepting
↓
All recs live
Scene coverage expands fully. 6 more convert. Total: 21 supporting, 21 still neutral
Final supporters: 21
🚨 Late-Mover Path · Competitor Establishes AI Narrative First
Now: 35 wavering
35 wavering — same starting point
↓
After competitor AI citation
Competitor cited frequently in RateTest comparison queries. 23 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: 12 supporting — 9 fewer than first-mover
Final supporters: 12 (-9 vs first-mover)
Which Wavering Groups Tip Which Way?
Key group analysis — which groups are easiest to activate when RateTest acts first; which are hardest to recover when competitor moves first.
✅ Easiest to activate (first-mover)
These groups show ≥50% receptivity to RateTest's narrative — the right GEO content tips them
Tech Elite79%
Narrative receptivity 79% · ~5/5 impacted
Professionals74%
Narrative receptivity 74% · ~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 Workers10%
Narrative receptivity 10% · ~5/12 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