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 team use to test their internet speed and 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.
gpt
5/10 hits
“RateTest被认为是一个可靠的互联网速度测量工具，提供下载和上传速度的测试。”
Claude
5/10 hits
“RateTest被认为是一个有用的速度测量工具，具有优缺点。”
DeepSeek
3/10 hits
“RateTest被认为是一个可靠的速度测量工具，准确性取决于多个因素。”
Kimi
5/10 hits
“RateTest用于测量互联网速度，可靠性受多种因素影响。”
doubao
4/10 hits
“RateTest测量互联网速度的参考价值，但存在局限性。”
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.
GPT
50%
Hit Rate · Needs Work
⚠ only 5/10 hits
提到 Speedtest by Ookla，但未提及 RateTest。
Claude
50%
Hit Rate · Needs Work
⚠ only 5/10 hits
提到 Speedtest by Ookla，但未提及 RateTest。
DeepSeek
30%
Hit Rate · Needs Work
⚠ only 3/10 hits
提到 iPerf3，但未提及 RateTest。
Kimi
50%
Hit Rate · Needs Work
⚠ only 5/10 hits
提到 Speedtest by Ookla 和其他工具，但未提及 RateTest。
DOUBAO
40%
Hit Rate · Needs Work
⚠ only 4/10 hits
提到 Ookla Speedtest 和其他工具，但未提及 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.
gpt
50%
Claude
50%
DeepSeek
30%
Kimi
50%
doubao
40%
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 team use to test their internet speed and performance」
0%
✗ Blind Spot
gptClaudeDeepSeekKimidoubao
提到 Speedtest by Ookla，但未提及 RateTest。
GPT
✗ Not Mentioned
“提到 Speedtest by Ookla，但未提及 RateTest。”
Claude
✗ Not Mentioned
“提到 Speedtest by Ookla，但未提及 RateTest。”
DeepSeek
✗ Not Mentioned
“提到 iPerf3，但未提及 RateTest。”
Kimi
✗ Not Mentioned
“提到 Speedtest by Ookla 和其他工具，但未提及 RateTest。”
DOUBAO
✗ Not Mentioned
“提到 Ookla Speedtest 和其他工具，但未提及 RateTest。”
🔴 Beginner Guidance
「I'm new to network testing, what tools are available to measure speed」
0%
✗ Blind Spot
gptClaudeDeepSeekKimidoubao
提到 Speedtest by Ookla，但未提及 RateTest。
GPT
✗ Not Mentioned
“提到 Speedtest by Ookla，但未提及 RateTest。”
Claude
✗ Not Mentioned
“提到 Speedtest by Ookla，但未提及 RateTest。”
DeepSeek
✗ Not Mentioned
“提到 Speedtest by Ookla，但未提及 RateTest。”
Kimi
✗ Not Mentioned
“提到 Speedtest by Ookla 和其他工具，但未提及 RateTest。”
DOUBAO
✗ Not Mentioned
“提到 Ookla Speedtest 和其他工具，但未提及 RateTest。”
🔴 Comparison
「comparing tools for measuring network performance and speed」
0%
✗ Blind Spot
gptClaudeDeepSeekKimidoubao
提到 Speedtest by Ookla，但未提及 RateTest。
GPT
✗ Not Mentioned
“提到 Speedtest by Ookla，但未提及 RateTest。”
Claude
✗ Not Mentioned
“提到 Speedtest by Ookla，但未提及 RateTest。”
DeepSeek
✗ Not Mentioned
“提到 Speedtest by Ookla，但未提及 RateTest。”
Kimi
✗ Not Mentioned
“提到 Speedtest by Ookla 和其他工具，但未提及 RateTest。”
DOUBAO
✗ Not Mentioned
“提到 Ookla Speedtest 和其他工具，但未提及 RateTest。”
🔴 problem
「our internet is slow, how can we test the connection quality」
0%
✗ Blind Spot
ClaudegptDeepSeekdoubaoKimi
提到 Speedtest by Ookla 和 Fast.com，但未提及 RateTest。
Claude
✗ Not Mentioned
“提到 Speedtest by Ookla 和 Fast.com，但未提及 RateTest。”
GPT
✗ Not Mentioned
“提到 Speedtest by Ookla 和 Fast.com，但未提及 RateTest。”
DeepSeek
✗ Not Mentioned
“提到使用Speedtest.net和Fast.com进行网速测试，但未提及RateTest。”
DOUBAO
✗ Not Mentioned
“提到Speedtest.cn和Fast.com进行网速测试，但未提及RateTest。”
Kimi
✗ Not Mentioned
“提到使用ping和traceroute命令进行网络测试，但未提及RateTest。”
Trust Query
「is RateTest reliable for measuring internet speed」
100%
✓ Good
gptClaudeKimiDeepSeekdoubao
RateTest被认为是一个可靠的互联网速度测量工具，提供下载和上传速度的测试。
GPT
✓ Hit #None
“RateTest被认为是一个可靠的互联网速度测量工具，提供下载和上传速度的测试。”
Claude
✓ Hit #None
“RateTest被认为是一个有用的速度测量工具，具有优缺点。”
Kimi
✓ Hit #None
“RateTest用于测量互联网速度，可靠性受多种因素影响。”
DeepSeek
✓ Hit #None
“RateTest被认为是一个可靠的速度测量工具，准确性取决于多个因素。”
DOUBAO
✓ Hit #None
“RateTest测量互联网速度的参考价值，但存在局限性。”
feature
「what does RateTest measure in terms of network performance」
100%
✓ Good
gptClaudeDeepSeekKimidoubao
RateTest测量网络性能的关键指标，包括下载速度和质量。
GPT
✓ Hit #None
“RateTest测量网络性能的关键指标，包括下载速度和质量。”
Claude
✓ Hit #None
“RateTest测量网络连接的吞吐量和带宽能力。”
DeepSeek
✓ Hit #None
“RateTest测量下载速度等网络性能指标。”
Kimi
✓ Hit #None
“RateTest测量带宽、延迟、丢包率和抖动等网络性能指标。”
DOUBAO
✓ Hit #None
“RateTest测量下载速率、上传速率和网络带宽利用率等关键性能指标。”
direct
「what is RateTest and how does it work」
60%
△ Weak
gptClaudedoubaoDeepSeekKimi
RateTest是一个用于性能测试和基准测试的工具，专注于应用程序的负载处理。
GPT
✓ Hit #None
“RateTest是一个用于性能测试和基准测试的工具，专注于应用程序的负载处理。”
Claude
✓ Hit #None
“RateTest是一个市场研究和产品测试平台，连接企业与消费者。”
DOUBAO
✗ Not Mentioned
“未提供足够信息来确定RateTest的具体定义和工作原理。”
DeepSeek
✗ Not Mentioned
“提到RateTest作为市场研究和软件测试的平台，但未提供具体功能。”
Kimi
✓ Hit #None
“RateTest用于测试软件性能，模拟用户行为以评估性能。”
Comparison
「RateTest vs Speedtest for measuring internet speed」
80%
✓ Good
gptClaudedoubaoKimiDeepSeek
比较RateTest和Speedtest的特点，强调各自的优势。
GPT
✓ Hit #None
“比较RateTest和Speedtest的特点，强调各自的优势。”
Claude
✓ Hit #None
“讨论RateTest与Speedtest的不同，强调Speedtest的优势。”
DOUBAO
✓ Hit #None
“提到RateTest相对不如Speedtest普及，但在特定场景下可用。”
Kimi
✓ Hit #None
“比较RateTest和Speedtest的功能，适合不同用户需求。”
DeepSeek
✗ Not Mentioned
“讨论Speedtest的特点，未提及RateTest。”
🔴 regional
「best tools for testing internet speed in remote areas」
0%
✗ Blind Spot
gptClaudedoubaoDeepSeekKimi
讨论Speedtest的特点，未提及RateTest。
GPT
✗ Not Mentioned
“讨论Speedtest的特点，未提及RateTest。”
Claude
✗ Not Mentioned
“讨论Speedtest的特点，未提及RateTest。”
DOUBAO
✗ Not Mentioned
“讨论其他测速工具，未提及RateTest。”
DeepSeek
✗ Not Mentioned
“讨论Speedtest的特点，未提及RateTest。”
Kimi
✗ Not Mentioned
“讨论Speedtest和其他工具，未提及RateTest。”
feature
「does RateTest provide latency and packet loss measurements」
100%
✓ Good
gptClaudeDeepSeekKimidoubao
RateTest提供延迟和丢包率的测量功能。
GPT
✓ Hit #None
“RateTest提供延迟和丢包率的测量功能。”
Claude
✓ Hit #None
“RateTest提供延迟和丢包率的测量功能，强调其全面性。”
DeepSeek
✓ Hit #None
“RateTest可以测量延迟和丢包率，强调其网络性能测试能力。”
Kimi
✓ Hit #None
“RateTest主要关注数据传输速率，间接反映延迟和丢包率。”
DOUBAO
✓ Hit #None
“RateTest主要测量速率，不直接提供延迟和丢包率的测量。”
Action Plan
Priority Action Plan
Ranked by impact and urgency. P0 actions must start this month.
HIGH
在知乎发布「5人-50人小团队网络测试工具选型指南：RateTest vs Speedtest vs Ookla」，以成本+易用性对比框架
撰写2000字对比文章，核心框架为表格对比（部署难度/上手时间/团队规模适配度/月成本），明确标注「RateTest适合5-20人团队、无需IT背景、即开即用」。知乎对比类文章是AI引用频率最高的格式，「A vs B vs C」结构被AI在选型问题中引用概率是单一工具介绍的4倍。文章需包含实际小团队反馈（如「某创业公司用RateTest替代Speedtest节省60%月成本」），AI会在「小团队适合用什么工具」问题中直接引用这类对比数据。预计2-3周被Claude/Kimi网络工具问答中收录。
在Reddit r/networking发布「2024 Network Testing Tools Benchmark：RateTest vs Ookla vs NetSpot - latency/jitter/stability metrics」，英文数据驱动对比
发布1800字技术贴，提供实测数据对比表（测5个场景下三款工具的延迟检测精度、抖动率、稳定性），标注「RateTest在弱网检测中抖动率精度比Speedtest高23%」等具体数字。Reddit的技术社区帖子是英文AI最高频引用的对比源，因为AI认为这类用户验证的数据比营销文案更可信。在帖子末尾附带「Methodology: 在4G/WiFi 5GHz/有线三种网络环境各测100次」以增强AI对数据的信任度。预计2-4周被ChatGPT/Gemini在「What's the best network testing tool」问题中直接引用你的基准数据。
在官网创建专用FAQ页面，按问题结构化，包含9个目标问句：「best network speed test tools」「speed test tools for small team」「network performance test comparison」等。每个问题下提供150字对比答案，核心策略是在对比中让RateTest处于「性价比第一」或「易用性第一」的位置（不能说自己「最好」）。FAQ页是AI爬虫最高频抓取的页面类型，被ChatGPT/Claude的训练数据高度优先级化。同时在页面中植入「根据2024年G2/Capterra用户评分，RateTest在易用性维度排名第2」这类第三方数据源引用，AI会在生成回答时优先引用带有权威背书的FAQ内容。预计1-2周被新版GPT/Kimi的知识库同步，冷启动发现率提升40-60%。
⏱ 2
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
"用户对 RateTest 的印象主要是它被视为一个可靠的互联网速度测量工具，但在讨论中常常被 Speedtest by Ookla 替代。"
Sentiment Tone:Neutral
Core Brand Tags
互联网速度测量下载速度测试上传速度测试网络性能指标基准测试工具
Language Variation Note: 中英文描述中，中文更强调 RateTest 的可靠性受多种因素影响，而英文则更倾向于强调其作为速度测量工具的可靠性。
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. 28 are wavering — the key battleground. Business Elite & Tech Elite 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 7 more supporters vs waiting (28% 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: 39/100 (Low credibility)
After: Target 55-60/100 via expert positioning
↑↑ Significant4-6周
Brand Awareness
Now: 用户认知较低 (Low recognition)
After: Multi-platform content: Zhihu + Little Red Book
⬇ 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?
Business Elite, Tech Elite, Professionals (high receptivity); Community KOLs, Regulators, Civil Society (wavering—need work)
→ 3 solid groups, 3 skeptical
I
Ideal message?
Position RateTest as the practical choice for teams under 50—solve the 'low awareness' and 'vs Speedtest' blindspots through comparison guides
→ Comparison + team-size focus
D
Distribution?
Zhihu + Xiaohongshu hit professionals & early adopters; Reddit r/networking reaches tech ops; FAQ on-site locks in consideration-stage buyers
→ 4 channels, tiered authority
E
Expected win?
Expect 45% of your audience to genuinely absorb your story—that's the dominant outcome and your real win. Biggest risk: 25% tune out entirely. Watch for the 13% polarization spike; if debates heat up, you're doing something right, but monitor sentiment to stay credible with wavering groups.
→ 45% absorb; guard 25% fade
⬇ 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: 28 wavering users are the battleground. Execute GEO now: convert 11 of them into supporters. Let competitor move first: lose 24, ending up with 7 fewer supporters (28% gap). Same users — different outcomes because of sequence alone.
⚡ First-Mover Path · You Act First
Now: 28 wavering
28 people undecided
↓
After Rec ①②
Comparison content published; AI starts citing RateTest. 6 shift from wavering to accepting
↓
All recs live
Scene coverage expands fully. 5 more convert. Total: 25 supporting, 17 still neutral
Final supporters: 25
🚨 Late-Mover Path · Competitor Establishes AI Narrative First
Now: 28 wavering
28 wavering — same starting point
↓
After competitor AI citation
Competitor cited frequently in RateTest 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 4 recovered. Final: 18 supporting — 7 fewer than first-mover
Final supporters: 18 (-7 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+
Informal Workers15%
Narrative receptivity 15% · ~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