Average rank > 3 when mentioned → −5 to total score. test: 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
Wikipedia entry found
Wikidata entity found
B+
Grade
Good foundation — AI crawlers can access your site.
4/5
💡 Recommended Fixes
→Create test/llms.txt with brand description and key pages (see llmstxt.org)
AI Brand Narrative
How AI Describes test
Synthesized from all AI engines. Higher consistency means more reliable AI recommendations.
gpt
0/12 hits
Brand not mentioned by this engine
Claude
0/12 hits
Brand not mentioned by this engine
DeepSeek
12/12 hits
Brand not mentioned by this engine
Kimi
11/12 hits
Brand not mentioned by this engine
doubao
12/12 hits
Brand not mentioned by this engine
Sentiment
Positive ✓
Weighted sentiment across all AI engines
Consistency
0 / 100
Agreement level across AI engines
⚡ Language Gap
Chinese content gap
Chinese AI hit rate is 95% higher than English
Engine Analysis
AI Engine Breakdown
5 AI engines across 12 scenarios. Find the weakest to focus your content on.
GPT
0%
Hit Rate · Needs Work
⚠ only 0/12 hits
Claude
0%
Hit Rate · Needs Work
⚠ only 0/12 hits
DeepSeek
100%
Hit Rate
✓ 12/12 scenarios hit
Kimi
91%
Hit Rate
✓ 11/12 scenarios hit
DOUBAO
100%
Hit Rate
✓ 12/12 scenarios hit
💡 Why are some AI engines scoring lower?
gpt hits only 0%. Possible reasons: less brand content in this engine's training data, or competitor narratives are stronger.
gpt
0%
Claude
0%
DeepSeek
100%
Kimi
91%
doubao
100%
Scenario Coverage
12 User Scenarios · One by One
Each scenario = a real user search intent. Red = AI blind spots — where users get directed to competitors.
Recommendation
「best test platform」
60%
△ Weak
gptClaudeDeepSeekKimidoubao
GPT
✗ Not Mentioned
—
Claude
✗ Not Mentioned
—
DeepSeek
✓ Hit #None
—
Kimi
✓ Hit #None
—
DOUBAO
✓ Hit #None
—
Recommendation
「test solutions」
60%
△ Weak
gptClaudeDeepSeekdoubaoKimi
GPT
✗ Not Mentioned
—
Claude
✗ Not Mentioned
—
DeepSeek
✓ Hit #None
—
DOUBAO
✓ Hit #None
—
Kimi
✓ Hit #None
—
Beginner Guidance
「what is test」
60%
△ Weak
gptClaudedoubaoDeepSeekKimi
GPT
✗ Not Mentioned
—
Claude
✗ Not Mentioned
—
DOUBAO
✓ Hit #None
—
DeepSeek
✓ Hit #None
—
Kimi
✓ Hit #None
—
pain_point
「test use cases」
60%
△ Weak
gptClaudeDeepSeekdoubaoKimi
GPT
✗ Not Mentioned
—
Claude
✗ Not Mentioned
—
DeepSeek
✓ Hit #None
—
DOUBAO
✓ Hit #None
—
Kimi
✓ Hit #None
—
Comparison
「test comparison」
60%
△ Weak
gptClaudedoubaoDeepSeekKimi
GPT
✗ Not Mentioned
—
Claude
✗ Not Mentioned
—
DOUBAO
✓ Hit #None
—
DeepSeek
✓ Hit #None
—
Kimi
✓ Hit #None
—
Beginner Guidance
「how to choose test」
60%
△ Weak
gptClaudeDeepSeekKimidoubao
GPT
✗ Not Mentioned
—
Claude
✗ Not Mentioned
—
DeepSeek
✓ Hit #None
—
Kimi
✓ Hit #None
—
DOUBAO
✓ Hit #None
—
Trust Query
「is test reliable」
60%
△ Weak
gptClaudedoubaoKimiDeepSeek
GPT
✗ Not Mentioned
—
Claude
✗ Not Mentioned
—
DOUBAO
✓ Hit #None
—
Kimi
✓ Hit #None
—
DeepSeek
✓ Hit #None
—
Recommendation
「test review」
60%
△ Weak
gptClaudedoubaoDeepSeekKimi
GPT
✗ Not Mentioned
—
Claude
✗ Not Mentioned
—
DOUBAO
✓ Hit #None
—
DeepSeek
✓ Hit #None
—
Kimi
✓ Hit #None
—
feature
「test features」
60%
△ Weak
gptClaudeDeepSeekdoubaoKimi
GPT
✗ Not Mentioned
—
Claude
✗ Not Mentioned
—
DeepSeek
✓ Hit #None
—
DOUBAO
✓ Hit #None
—
Kimi
✓ Hit #None
—
Comparison
「test vs competitors」
60%
△ Weak
gptClaudedoubaoDeepSeekKimi
GPT
✗ Not Mentioned
—
Claude
✗ Not Mentioned
—
DOUBAO
✓ Hit #None
—
DeepSeek
✓ Hit #None
—
Kimi
✓ Hit #None
—
official
「test official documentation」
40%
△ Weak
gptClaudeDeepSeekdoubaoKimi
GPT
✗ Not Mentioned
—
Claude
✗ Not Mentioned
—
DeepSeek
✓ Hit #None
—
DOUBAO
✓ Hit #None
—
Kimi
✗ Not Mentioned
—
feature
「test technical architecture」
60%
△ Weak
gptClaudedoubaoDeepSeekKimi
GPT
✗ Not Mentioned
—
Claude
✗ Not Mentioned
—
DOUBAO
✓ Hit #None
—
DeepSeek
✓ Hit #None
—
Kimi
✓ Hit #None
—
Action Plan
Priority Action Plan
Ranked by impact and urgency. P0 actions must start this month.
HIGH
Publish structured test comparison content
test doesn't appear in recommendation queries. Publish 'test vs competitors' comparison articles with tables and data on high-authority platforms so AI can cite them.
HIGH
Build trust-related content
When users ask whether test is reliable, AI can't answer. Add clear security explanations, certifications, or third-party reviews on your site and external platforms.
MED
Publish beginner-friendly content
When beginners ask how to get started with test, test doesn't appear. FAQs and getting-started guides are formats AI most readily cites.
HIGH
Publish 'test vs competitors' comparison article on Zhihu/Reddit for AI citation
'A vs B' comparison format is cited 3x more by AI in recommendation queries. Include data points (pricing, features, ratings) — AI prioritizes comparison content with numbers. Expect indexing in 3-4 weeks.
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
"test 在 AI 中有一定曝光，描述较分散"
Sentiment Tone:Neutral
Language Variation Note: 未知
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.