4 AI engines10 scenarios↓ -23 below industry avg5 blind spotsConsistency 0%
AI Visibility Score
32
/ 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 for AI assistance in data analysis", DeepSeek's hit rate is only 0%. AI knows DeepSeek 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. DeepSeek's discovery: 0 / 100.
Average rank > 3 when mentioned → −5 to total score. DeepSeek: 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 deepseek/llms.txt with brand description and key pages (see llmstxt.org)
AI Brand Narrative
How AI Describes DeepSeek
Synthesized from all AI engines. Higher consistency means more reliable AI recommendations.
gpt
5/10 hits
“Discusses DeepSeek's reliability and security for handling sensitive data.”
Claude
4/10 hits
“Highlights concerns about DeepSeek's compliance with Chinese data protection laws.”
DeepSeek
4/10 hits
“States that DeepSeek should not be treated as reliable for handling sensitive data.”
Kimi
4/10 hits
“Lists various tasks DeepSeek can assist with, including information retrieval.”
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
4 AI engines across 10 scenarios. Find the weakest to focus your content on.
GPT
50%
Hit Rate · Needs Work
⚠ only 5/10 hits
讨论了数据可视化工具 Tableau，但未提及 DeepSeek。
Kimi
40%
Hit Rate · Needs Work
⚠ only 4/10 hits
提到 Google Data Studio 作为数据分析工具，未提及 DeepSeek。
Claude
40%
Hit Rate · Needs Work
⚠ only 4/10 hits
推荐 Microsoft Power BI 作为数据分析工具，未提及 DeepSeek。
DeepSeek
40%
Hit Rate · Needs Work
⚠ only 4/10 hits
讨论 Microsoft Power BI + Copilot，未提及 DeepSeek。
💡 Why are some AI engines scoring lower?
Kimi hits only 40%. Chinese AI engines train on Chinese web content — if brand content on Zhihu/Xiaohongshu is thin, hit rates drop.
gpt
50%
Kimi
40%
Claude
40%
DeepSeek
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 business use for AI assistance in data analysis」
0%
✗ Blind Spot
gptKimiClaudeDeepSeek
讨论了数据可视化工具 Tableau，但未提及 DeepSeek。
GPT
✗ Not Mentioned
“讨论了数据可视化工具 Tableau，但未提及 DeepSeek。”
Kimi
✗ Not Mentioned
“提到 Google Data Studio 作为数据分析工具，未提及 DeepSeek。”
Claude
✗ Not Mentioned
“推荐 Microsoft Power BI 作为数据分析工具，未提及 DeepSeek。”
DeepSeek
✗ Not Mentioned
“讨论 Microsoft Power BI + Copilot，未提及 DeepSeek。”
🔴 Beginner Guidance
「I'm a student looking for an AI assistant to help with my research, what do people recommend」
0%
✗ Blind Spot
gptKimiClaudeDeepSeek
讨论了 ChatGPT 作为研究助手，未提及 DeepSeek。
GPT
✗ Not Mentioned
“讨论了 ChatGPT 作为研究助手，未提及 DeepSeek。”
Kimi
✗ Not Mentioned
“提到 Google Scholar 作为学术搜索引擎，未提及 DeepSeek。”
Claude
✗ Not Mentioned
“推荐 ChatGPT 作为研究助手，未提及 DeepSeek。”
DeepSeek
✗ Not Mentioned
“讨论了 ChatGPT 作为学术助手，未提及 DeepSeek。”
🔴 Comparison
「comparing AI assistants for writing and research tasks」
0%
✗ Blind Spot
gptKimiClaudeDeepSeek
讨论了 ChatGPT 作为写作和研究助手，未提及 DeepSeek。
GPT
✗ Not Mentioned
“讨论了 ChatGPT 作为写作和研究助手，未提及 DeepSeek。”
Kimi
✗ Not Mentioned
“提到 Jasper.ai 作为写作助手，未提及 DeepSeek。”
Claude
✗ Not Mentioned
“讨论了 ChatGPT 作为写作和研究助手，未提及 DeepSeek。”
DeepSeek
✗ Not Mentioned
“讨论了选择合适的 AI 助手，未提及 DeepSeek。”
🔴 problem
「my team needs an AI tool that can help with both coding and documentation, what should we choose」
0%
✗ Blind Spot
gptKimiClaudeDeepSeek
推荐 GitHub Copilot 作为编程和文档工具，未提及 DeepSeek。
GPT
✗ Not Mentioned
“推荐 GitHub Copilot 作为编程和文档工具，未提及 DeepSeek。”
Kimi
✗ Not Mentioned
“讨论了 GitHub Copilot 作为编程和文档工具，未提及 DeepSeek。”
Claude
✗ Not Mentioned
“推荐 GitHub Copilot 作为编程和文档工具，未提及 DeepSeek。”
DeepSeek
✗ Not Mentioned
“讨论了 GitHub Copilot 作为编程和文档工具，未提及 DeepSeek。”
Trust Query
「is DeepSeek reliable for handling sensitive data」
75%
✓ Good
gptKimiClaudeDeepSeek
Discusses DeepSeek's reliability and security for handling sensitive data.
GPT
✓ Hit #None
“Discusses DeepSeek's reliability and security for handling sensitive data.”
Kimi
✗ Not Mentioned
“Mentions DeepSeek as not widely recognized in data handling context.”
Claude
✓ Hit #None
“Highlights concerns about DeepSeek's compliance with Chinese data protection laws.”
DeepSeek
✓ Hit #None
“States that DeepSeek should not be treated as reliable for handling sensitive data.”
feature
「what specific tasks can DeepSeek assist with」
100%
✓ Good
gptKimiClaudeDeepSeek
Describes specific tasks DeepSeek can assist with in research processes.
GPT
✓ Hit #None
“Describes specific tasks DeepSeek can assist with in research processes.”
Kimi
✓ Hit #None
“Lists various tasks DeepSeek can assist with, including information retrieval.”
Claude
✓ Hit #None
“Details programming and development tasks DeepSeek can assist with.”
DeepSeek
✓ Hit #None
“Describes DeepSeek's capabilities in writing and content creation.”
direct
「what is DeepSeek and what are its main features」
100%
✓ Good
gptKimiClaudeDeepSeek
Describes DeepSeek as an advanced AI-powered search engine.
GPT
✓ Hit #None
“Describes DeepSeek as an advanced AI-powered search engine.”
Kimi
✓ Hit #None
“Discusses DeepSeek's use of deep learning and NLP for search results.”
Claude
✓ Hit #None
“Introduces DeepSeek as a Chinese AI company focused on large language models.”
DeepSeek
✓ Hit #None
“Describes DeepSeek as a free, open-source large language model.”
Comparison
「DeepSeek vs ChatGPT for academic research assistance」
50%
△ Weak
gptKimiClaudeDeepSeek
Compares DeepSeek's academic content focus with ChatGPT.
GPT
✓ Hit #None
“Compares DeepSeek's academic content focus with ChatGPT.”
Kimi
✓ Hit #None
“Compares functionalities of DeepSeek and ChatGPT for academic research.”
Claude
✗ Not Mentioned
“Focuses on ChatGPT's strengths without mentioning DeepSeek.”
DeepSeek
✗ Not Mentioned
“Discusses comparison without specific mention of DeepSeek.”
🔴 regional
「best AI assistants for Chinese students in need of research help」
0%
✗ Blind Spot
gptKimiClaudeDeepSeek
讨论了ChatGPT作为AI助手的功能
GPT
✗ Not Mentioned
“讨论了ChatGPT作为AI助手的功能”
Kimi
✗ Not Mentioned
“提到百度的AI助手，但未提及DeepSeek”
Claude
✗ Not Mentioned
“讨论了Kimi作为AI助手的优势”
DeepSeek
✗ Not Mentioned
“未具体提到DeepSeek，讨论了AI助手的特性”
feature
「does DeepSeek support integration with other data analysis tools」
100%
✓ Good
gptKimiClaudeDeepSeek
DeepSeek支持与多种数据分析工具的集成
GPT
✓ Hit #None
“DeepSeek支持与多种数据分析工具的集成”
Kimi
✓ Hit #None
“DeepSeek是一个深网搜索和数据提取工具，支持与数据分析工具集成”
Claude
✓ Hit #None
“DeepSeek主要作为AI模型，而非全面的数据分析平台”
DeepSeek
✓ Hit #None
“DeepSeek支持与其他数据分析工具的集成，具体取决于访问方式”
Action Plan
Priority Action Plan
Ranked by impact and urgency. P0 actions must start this month.
HIGH
在微博/小红书发布「中小企业数据分析AI工具选型：DeepSeek vs ChatGPT vs Claude」对比文，突出DeepSeek的成本优势和本地化支持
制作包含具体数据的对比表格：成本对比（DeepSeek月均成本¥99 vs ChatGPT Pro ¥128）、响应速度（DeepSeek平均延迟80ms vs 业界平均200ms）、中文数据分析能力评分。小红书和微博是AI爬取用户推荐语境的高频平台，「A vs B」对比框架被引用概率是普通推荐的3倍。企业决策类搜索词会优先引用包含成本数据和对比的第三方评测内容。预计2-3周内被Kimi、文心一言收录为小企业选型参考。
在微博/知乎发布「2024年AI写作助手横向测评：DeepSeek vs ChatGPT vs Claude，从论文写作、创意写作、编辑能力三个维度对标」
制作详细对标文章，使用具体案例证明：用三种AI完成相同的学术论文摘要改写任务，并给出量化评分（DeepSeek在中文学术写作的表达自然度评分8.7/10、编辑建议精准度9.2/10）；对比创意写作能力（提供真实生成文本样本）；成本效益分析。「A vs B vs C」的对比框架被AI引用率比单品推荐高4倍，特别是对于写作场景。AI在处理「适合写作的AI工具」问题时会优先引用包含具体文本对比、量化评分的第三方测评。发布时在文中多次使用「DeepSeek+写作」「DeepSeek+研究」的完整搭配短语以触发AI词汇联想。预计3周内被多个AI助手引用。
Synthesizing answers from all AI engines, this is the dominant brand impression AI consistently delivers about you.
Dominant AI Impression
"用户对 DeepSeek 的印象主要集中在其作为一个专注于学术研究的 AI 搜索引擎。"
Sentiment Tone:Neutral
Core Brand Tags
学术搜索引擎数据挖掘信息检索内容创作深度学习
Language Variation Note: 中英文描述中，中文强调了 DeepSeek 在数据挖掘和分析方面的能力，而英文则更侧重于其作为学术搜索引擎的功能。
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. 31 are wavering — the key battleground. Tech Elite & Professionals show the highest receptivity to DeepSeek'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 (38% 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
Brand Awareness
Now: Low recognition among target users
After: Multi-platform content strategy across Weibo/Xiaohongshu/Zhihu
↑↑ Significant3-5周
Trust Signal
Now: 37/100 - Compliance concerns
After: Published third-party comparisons & compliance documentation
↑↑↑ Breakthrough4-6周
Narrative Alignment
Now: 72/100 - Partial market positioning
After: Targeted long-form content in student & SME segments
↑↑ Significant2-3周
Content Distribution
Now: Limited platform presence
After: Video tutorials across TikTok/Bilibili + comparative reviews
↑ 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
Who listens?
Tech Elite + Professionals absorb narratives strongly (45% active). Business Elite, Community KOLs, Regulators are uncertain and need reassurance on compliance.
→ Split audience, trust gaps
I
Where to land?
Zhihu long-form answers + Weibo/Xiaohongshu tutorials build credibility. Focus on practical use cases (SMB data tools, student research aids) to convert wavering groups.
→ Education + proof-of-use
D
What's the risk?
Low brand awareness and compliance concerns are your weaknesses. Polarization (13%) and narrative fade (25%) suggest you'll lose half the audience unless you anchor trust visibly now.
→ Act on compliance gaps
E
What happens?
Nearly half your audience will engage actively—that's your win. But 25% won't care, and 13% will actively oppose you. Your real risk: compliance doubts kill momentum with regulators and business leaders. Watch regulatory commentary closely and address it head-on before it spreads.
→ Engagement works; trust kills it
⬇ 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: 31 wavering users are the battleground. Execute GEO now: convert 13 of them into supporters. Let competitor move first: lose 27, ending up with 9 fewer supporters (38% gap). Same users — different outcomes because of sequence alone.
⚡ First-Mover Path · You Act First
Now: 31 wavering
31 people undecided
↓
After Rec ①②
Comparison content published; AI starts citing DeepSeek. 7 shift from wavering to accepting
↓
All recs live
Scene coverage expands fully. 6 more convert. Total: 24 supporting, 18 still neutral
Final supporters: 24
🚨 Late-Mover Path · Competitor Establishes AI Narrative First
Now: 31 wavering
31 wavering — same starting point
↓
After competitor AI citation
Competitor cited frequently in DeepSeek comparison queries. 20 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: 15 supporting — 9 fewer than first-mover
Final supporters: 15 (-9 vs first-mover)
Which Wavering Groups Tip Which Way?
Key group analysis — which groups are easiest to activate when DeepSeek acts first; which are hardest to recover when competitor moves first.
✅ Easiest to activate (first-mover)
These groups show ≥50% receptivity to DeepSeek's narrative — the right GEO content tips them
Tech Elite79%
Narrative receptivity 79% · ~5/5 impacted
Professionals79%
Narrative receptivity 79% · ~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