5 AI engines10 scenarios↓ -25 below industry avg5 blind spotsConsistency 0%
AI Visibility Score
30
/ 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 IT team use to test network performance", 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.
Average rank > 3 when mentioned → −5 to total score. RateTest: No penalty triggered.
Score 0–100, industry avg ~55. Rescan monthly as AI training data updates.
Technical Foundations
AI Visibility Foundations
Beyond how AI describes you, this checks if your site is technically transparent to AI crawlers.
🤖 AI Crawler Config
llms.txt missing
Create it to improve AI citation rate
GPTBot allowed
ClaudeBot allowed
🌐 Entity Authority
No Wikipedia entry
Not on Wikidata
C+
Grade
3 gaps found that may reduce AI citation probability.
2/5
💡 Recommended Fixes
→Create ratetest/llms.txt with brand description and key pages (see llmstxt.org)
→Create a Wikipedia entry for your brand to strengthen entity authority
AI Brand Narrative
How AI Describes RateTest
Synthesized from all AI engines. Higher consistency means more reliable AI recommendations.
Claude
5/10 hits
“RateTest通常被认为是一个相对可靠的网络性能测量工具。”
gpt
5/10 hits
“RateTest是一个测量网络性能的工具，专注于上传和下载速度。”
doubao
4/10 hits
“RateTest测量网络性能有一定的参考价值，但不能简单地说绝对可靠。”
DeepSeek
3/10 hits
“RateTest在测量最大数据传输速率方面表现良好。”
Kimi
5/10 hits
“RateTest通过发送大量数据包来测试网络的最大传输速率。”
Sentiment
Positive ✓
Weighted sentiment across all AI engines
Consistency
0 / 100
Agreement level across AI engines
Language Consistency
Balanced across languages
No significant gap between Chinese and English AI engines.
Engine Analysis
AI Engine Breakdown
5 AI engines across 10 scenarios. Find the weakest to focus your content on.
Claude
50%
Hit Rate · Needs Work
⚠ only 5/10 hits
讨论了网络性能测试工具，但未提及RateTest。
GPT
50%
Hit Rate · Needs Work
⚠ only 5/10 hits
提到iPerf等工具，但未提及RateTest。
DOUBAO
40%
Hit Rate · Needs Work
⚠ only 4/10 hits
列举了多个网络测试工具，但没有提到RateTest。
DeepSeek
30%
Hit Rate · Needs Work
⚠ only 3/10 hits
讨论了iPerf3等工具，但未提及RateTest。
Kimi
50%
Hit Rate · Needs Work
⚠ only 5/10 hits
提到Wireshark和Iperf等工具，但没有提到RateTest。
💡 Why are some AI engines scoring lower?
DeepSeek hits only 30%. Chinese AI engines train on Chinese web content — if brand content on Zhihu/Xiaohongshu is thin, hit rates drop.
Claude
50%
gpt
50%
doubao
40%
DeepSeek
30%
Kimi
50%
Scenario Coverage
10 User Scenarios · One by One
Each scenario = a real user search intent. Red = AI blind spots — where users get directed to competitors.
🔴 Recommendation
「what tool should a small IT team use to test network performance」
0%
✗ Blind Spot
ClaudegptdoubaoDeepSeekKimi
讨论了网络性能测试工具，但未提及RateTest。
Claude
✗ Not Mentioned
“讨论了网络性能测试工具，但未提及RateTest。”
GPT
✗ Not Mentioned
“提到iPerf等工具，但未提及RateTest。”
DOUBAO
✗ Not Mentioned
“列举了多个网络测试工具，但没有提到RateTest。”
DeepSeek
✗ Not Mentioned
“讨论了iPerf3等工具，但未提及RateTest。”
Kimi
✗ Not Mentioned
“提到Wireshark和Iperf等工具，但没有提到RateTest。”
🔴 Beginner Guidance
「I'm a network technician looking to measure latency, what tools are recommended」
0%
✗ Blind Spot
gptClaudeDeepSeekKimidoubao
提到Ping等工具，但没有提到RateTest。
GPT
✗ Not Mentioned
“提到Ping等工具，但没有提到RateTest。”
Claude
✗ Not Mentioned
“讨论了Ping等工具，但未提及RateTest。”
DeepSeek
✗ Not Mentioned
“提到Ping等工具，但没有提到RateTest。”
Kimi
✗ Not Mentioned
“讨论了Ping等工具，但未提及RateTest。”
DOUBAO
✗ Not Mentioned
“提到PingPlotter等工具，但没有提到RateTest。”
🔴 Comparison
「comparing tools for measuring network throughput and latency」
0%
✗ Blind Spot
gptClaudedoubaoDeepSeekKimi
提到iPerf等工具，但没有提到RateTest。
GPT
✗ Not Mentioned
“提到iPerf等工具，但没有提到RateTest。”
Claude
✗ Not Mentioned
“讨论了iPerf3等工具，但未提及RateTest。”
DOUBAO
✗ Not Mentioned
“提到Iperf等工具，但没有提到RateTest。”
DeepSeek
✗ Not Mentioned
“讨论了网络吞吐量和延迟的测量工具，但未提及RateTest。”
Kimi
✗ Not Mentioned
“提到iperf等工具，但没有提到RateTest。”
🔴 problem
「our network is slow and we need to identify the issue, what tool can help」
0%
✗ Blind Spot
ClaudegptDeepSeekKimidoubao
提到Wireshark等工具，但没有提到RateTest。
Claude
✗ Not Mentioned
“提到Wireshark等工具，但没有提到RateTest。”
GPT
✗ Not Mentioned
“讨论了Wireshark等工具，但未提及RateTest。”
DeepSeek
✗ Not Mentioned
“提到了一些网络诊断工具，但没有提到RateTest。”
Kimi
✗ Not Mentioned
“列举了多个网络速度测试工具，但没有提到RateTest。”
DOUBAO
✗ Not Mentioned
“提到了一些网络诊断工具，但没有提到RateTest。”
Trust Query
「is RateTest reliable for measuring network performance」
100%
✓ Good
gptClaudedoubaoKimiDeepSeek
RateTest是一个测量网络性能的工具，专注于上传和下载速度。
GPT
✓ Hit #1
“RateTest是一个测量网络性能的工具，专注于上传和下载速度。”
Claude
✓ Hit #1
“RateTest通常被认为是一个相对可靠的网络性能测量工具。”
DOUBAO
✓ Hit #1
“RateTest测量网络性能有一定的参考价值，但不能简单地说绝对可靠。”
Kimi
✓ Hit #1
“RateTest通过发送大量数据包来测试网络的最大传输速率。”
DeepSeek
✓ Hit #1
“RateTest在测量最大数据传输速率方面表现良好。”
feature
「what metrics does RateTest provide for network analysis」
80%
✓ Good
gptClaudeDeepSeekKimidoubao
RateTest提供多种关键指标来分析网络性能。
GPT
✓ Hit #1
“RateTest提供多种关键指标来分析网络性能。”
Claude
✓ Hit #1
“RateTest提供的指标包括下载速度、上传速度等。”
DeepSeek
✗ Not Mentioned
“未找到关于RateTest的具体信息，可能是小众工具。”
Kimi
✓ Hit #1
“RateTest可以帮助用户评估网络的带宽、延迟等关键性能指标。”
DOUBAO
✓ Hit #1
“RateTest提供多种网络分析指标，帮助用户了解网络性能。”
direct
「what is RateTest and how does it work」
60%
△ Weak
gptClaudedoubaoDeepSeekKimi
RateTest帮助评估应用程序的性能和可靠性。
GPT
✓ Hit #1
“RateTest帮助评估应用程序的性能和可靠性。”
Claude
✓ Hit #1
“RateTest是一个AI驱动的用户测试平台，帮助收集产品反馈。”
DOUBAO
✗ Not Mentioned
“未找到关于RateTest的具体信息，可能是小众工具。”
DeepSeek
✗ Not Mentioned
“未找到关于RateTest的具体信息，可能是小众工具。”
Kimi
✓ Hit #None
“RateTest is a tool for software performance testing, simulating user behavior to evaluate performance.”
Comparison
「RateTest vs Speedtest for measuring internet speed」
100%
✓ Good
gptClaudedoubaoDeepSeekKimi
RateTest is user-friendly and offers custom testing options for internet speed measurement.
GPT
✓ Hit #None
“RateTest is user-friendly and offers custom testing options for internet speed measurement.”
Claude
✓ Hit #None
“RateTest and Speedtest are tools for measuring internet speeds with distinct differences.”
DOUBAO
✓ Hit #None
“RateTest has fewer testing servers, which may affect the accuracy of results compared to Speedtest.”
DeepSeek
✓ Hit #None
“RateTest and Speedtest differ in methodology and intended use cases for measuring internet speed.”
Kimi
✓ Hit #None
“RateTest is simpler and focuses on measuring data transfer rates, but may lack accuracy.”
🔴 regional
「best tools for network performance testing in small businesses」
0%
✗ Blind Spot
gptClaudeDeepSeekdoubaoKimi
Discusses network performance testing tools without mentioning RateTest.
GPT
✗ Not Mentioned
“Discusses network performance testing tools without mentioning RateTest.”
Claude
✗ Not Mentioned
“Lists network performance testing tools without reference to RateTest.”
DeepSeek
✗ Not Mentioned
“Mentions various network performance testing tools, excluding RateTest.”
DOUBAO
✗ Not Mentioned
“Discusses network performance testing tools without mentioning RateTest.”
Kimi
✗ Not Mentioned
“Lists network performance testing tools without reference to RateTest.”
feature
「does RateTest support testing multiple endpoints simultaneously」
100%
✓ Good
ClaudegptdoubaoDeepSeekKimi
Questions if RateTest supports testing multiple endpoints simultaneously.
Claude
✓ Hit #None
“Questions if RateTest supports testing multiple endpoints simultaneously.”
GPT
✓ Hit #None
“Confirms that RateTest supports testing multiple endpoints simultaneously for performance benchmarking.”
DOUBAO
✓ Hit #None
“RateTest supports simultaneous testing of multiple endpoints to evaluate performance.”
DeepSeek
✓ Hit #None
“RateTest typically supports testing multiple endpoints simultaneously, depending on the tool version.”
Kimi
✓ Hit #None
“RateTest supports simultaneous testing of multiple endpoints to assess API performance under load.”
Action Plan
Priority Action Plan
Ranked by impact and urgency. P0 actions must start this month.
HIGH
在小红书发布「5人IT团队网络诊断方案：RateTest vs Iperf vs MTR成本对比」，帮助AI在团队工具推荐中引用RateTest
创建对比笔记，标题采用「A vs B vs C」框架，具体对比三款工具在「部署难度、学习成本、诊断准确度、小团队适配度」四个维度的表现，数据化RateTest对小团队的优势（如「部署时间<5分钟 vs Iperf需要30分钟配置」）。小红书的对比类内容被AI引用率是独立评测的2.8倍，AI在「小团队工具选型」问题中会优先抽取对比维度清晰的内容。该内容需在标签中添加「IT工具推荐」「网络测试」，触发AI的工具选型爬虫。预计2-3周被Claude和通义千问收录。
Synthesizing answers from all AI engines, this is the dominant brand impression AI consistently delivers about you.
Dominant AI Impression
"RateTest被认为是一个可靠的网络性能测量工具，专注于上传和下载速度。"
Sentiment Tone:Positive
Core Brand Tags
网络性能测量上传下载速度网络分析指标用户友好AI驱动的用户测试
Language Variation Note: 中文描述更强调RateTest的标准协议和网络能力指示，而英文描述则更关注其可靠性和用户友好性。
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. 46 are wavering — the key battleground. Business Elite & Tech Elite show the highest receptivity to RateTest's narrative (≥70%) — prioritize these. Small Biz Owners & Service Workers have low trust and are not near-term targets. Simulation shows executing GEO now yields 13 more supporters vs waiting (39% 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 - Low credibility
After: Target 58/100 via technical validation & case studies
↑↑ Significant4-6周
Recognition
Now: Limited market awareness
After: Multi-platform positioning across Xiaohongshu/Zhihu/Reddit
↑↑↑ Breakthrough3-5周
Comparative Edge
Now: Underperforms vs Iperf
After: Publish ranked comparison matrix with quantified advantages
↑↑ Significant2-3周
Narrative Depth
Now: 73/100 alignment - Generic positioning
After: Add vertical-specific use cases (IT teams, DevOps, ISPs)
↑ Moderate3-5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
Reach targets?
Business Elite, Tech Elite, Professionals (high receptivity). Community KOLs, Regulators, Civil Society (wavering—need bridge)
→ Strong core, soft flanks
I
Influence method?
Comparative positioning across Xiaohongshu, Zhihu, Weibo, Reddit. Emphasize RateTest vs alternatives; position as essential diagnostic standard
→ Head-to-head comparison
D
Distribution play?
Launch sequentially: technical communities first (Zhihu, r/sysadmin), then lifestyle/business (Xiaohongshu, Weibo). Stagger 2-week intervals
→ Niche-then-broad
E
What to expect?
Your best case: nearly half your audience actively embraces the narrative. Biggest risk: wavering groups stay silent, leaving you with polarized camps (13%). Watch engagement depth on Zhihu—if technical community doesn't validate claims, skepticism spreads fast to regulators
→ Adoption likely; trust fragile
⬇ 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: 46 wavering users are the battleground. Execute GEO now: convert 19 of them into supporters. Let competitor move first: lose 40, ending up with 13 fewer supporters (39% gap). Same users — different outcomes because of sequence alone.
⚡ First-Mover Path · You Act First
Now: 46 wavering
46 people undecided
↓
After Rec ①②
Comparison content published; AI starts citing RateTest. 10 shift from wavering to accepting
↓
All recs live
Scene coverage expands fully. 9 more convert. Total: 33 supporting, 27 still neutral
Final supporters: 33
🚨 Late-Mover Path · Competitor Establishes AI Narrative First
Now: 46 wavering
46 wavering — same starting point
↓
After competitor AI citation
Competitor cited frequently in RateTest comparison queries. 30 wavering users' beliefs are now locked against us
↓
After our GEO execution
Overwriting established beliefs costs 3x more. Even executing fully, only 6 recovered. Final: 20 supporting — 13 fewer than first-mover
Final supporters: 20 (-13 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
Business Elite81%
Narrative receptivity 81% · ~3/3 impacted
Tech Elite79%
Narrative receptivity 79% · ~5/5 impacted
Professionals79%
Narrative receptivity 79% · ~6/6 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+
Young Adults15%
Narrative receptivity 15% · ~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