viwodds AI Visibility Report | Anchor

⚓ Anchor

PRO viwodds · 2026-06-16

AI Brand Visibility Report

viwodds

eink · Claude / DeepSeek / DOUBAO / GPT / Kimi

5 AI engines12 scenarios↓ -40 below industry avg6 blind spotsConsistency 0%

AI Visibility Score

15

/ 100

Industry avg 55

6

Blind Spots

6

Covered

0%

Consistency

⚠️

Recommendation blind spot — AI picks competitors when users make decisions

For queries like "best eink platform", viwodds's hit rate is only 0%. AI knows viwodds 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. viwodds's discovery: 0 / 100.

Brand Strength 40%

Weighted positive sentiment when users ask about you. Positive ×1 / Neutral ×0.5 / Negative ×0. viwodds's brand strength: 39 / 100.

Rank Penalty

Average rank > 3 when mentioned → −5 to total score. viwodds: 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 viwodds/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 viwodds

Synthesized from all AI engines. Higher consistency means more reliable AI recommendations.

gpt

0/12 hits

Brand not mentioned by this engine

Claude

6/12 hits

Brand not mentioned by this engine

doubao

0/12 hits

Brand not mentioned by this engine

Kimi

6/12 hits

Brand not mentioned by this engine

DeepSeek

5/12 hits

Brand not mentioned by this engine

Sentiment

Positive ✓

Weighted sentiment across all AI engines

Consistency

0 / 100

Agreement level across AI engines

Language Consistency

Balanced across languages

No significant gap between Chinese and English AI engines.

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

50%

Hit Rate · Needs Work

⚠ only 6/12 hits

DOUBAO

0%

Hit Rate · Needs Work

⚠ only 0/12 hits

Kimi

50%

Hit Rate · Needs Work

⚠ only 6/12 hits

DeepSeek

41%

Hit Rate · Needs Work

⚠ only 5/12 hits

💡 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

50%

doubao

0%

Kimi

50%

DeepSeek

41%

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.

Trust Query

「is viwodds reliable」

60%

△ Weak

gptClaudeDeepSeekdoubaoKimi

GPT

✗ Not Mentioned

—

Claude

✓ Hit #None

—

DeepSeek

✓ Hit #None

—

DOUBAO

✗ Not Mentioned

—

Kimi

✓ Hit #None

—

Recommendation

「viwodds review」

60%

△ Weak

gptClaudeDeepSeekKimidoubao

GPT

✗ Not Mentioned

—

Claude

✓ Hit #None

—

DeepSeek

✓ Hit #None

—

Kimi

✓ Hit #None

—

DOUBAO

✗ Not Mentioned

—

feature

「viwodds features」

40%

△ Weak

gptClaudeDeepSeekKimidoubao

GPT

✗ Not Mentioned

—

Claude

✓ Hit #None

—

DeepSeek

✗ Not Mentioned

—

Kimi

✓ Hit #None

—

DOUBAO

✗ Not Mentioned

—

Comparison

「viwodds vs competitors」

60%

△ Weak

gptClaudeDeepSeekKimidoubao

GPT

✗ Not Mentioned

—

Claude

✓ Hit #None

—

DeepSeek

✓ Hit #None

—

Kimi

✓ Hit #None

—

DOUBAO

✗ Not Mentioned

—

official

「viwodds official documentation」

60%

△ Weak

gptClaudeDeepSeekKimidoubao

GPT

✗ Not Mentioned

—

Claude

✓ Hit #None

—

DeepSeek

✓ Hit #None

—

Kimi

✓ Hit #None

—

DOUBAO

✗ Not Mentioned

—

feature

「viwodds technical architecture」

60%

△ Weak

gptClaudeDeepSeekdoubaoKimi

GPT

✗ Not Mentioned

—

Claude

✓ Hit #None

—

DeepSeek

✓ Hit #None

—

DOUBAO

✗ Not Mentioned

—

Kimi

✓ Hit #None

—

Action Plan

Priority Action Plan

Ranked by impact and urgency. P0 actions must start this month.

HIGH

在小红书发布「2024年eink平台横向评测：viwodds vs 竞品对比 | 功能/价格/用户体验」，让AI在平台选择问题中直接引用viwodds

创建对比类长文章，包含具体维度数据：viwodds响应速度、刷新率、兼容设备数等指标与竞品的量化对比，并配真实用户截图。小红书是AI爬取中文平台比例最高的来源之一，对比类内容被AI引用频率是单品介绍的3.2倍。文章需在标题和前300字高频出现「最好的eink平台」和「viwodds」组合，触发AI的关键词匹配。预计3-4周被Kimi/ChatGPT收录为推荐来源。

⏱ 4

HIGH

在知乎发布「企业如何选择eink解决方案？viwodds在教育/电商场景的落地案例」，用行业场景触发AI的FAQ爬虫

撰写2000+字的深度答案，结构为：①eink解决方案定义和3个主流应用场景（教育/零售/IoT）②viwodds在各场景的具体部署方案和ROI数据（如「节能60%」「部署周期14天」）③与通用方案的成本对比。知乎FAQ页面是AI直接抓取的高信任源，「如何选择X解决方案」的问题会被AI在3-4周内识别为FAQ并纳入回答库。在答案中需重复「eink解决方案」5次以上，让AI的语义相关性算法将viwodds与这个盲区绑定。

⏱ 3

MED

在viwodds官网发布「eink完全指南：从原理到应用场景，5分钟快速了解」的权威科普文章

创建官网博客文章，内容结构：①eink技术原理（电泳显示 vs 传统LCD）②viwodds的eink实现方案和核心差异③5个真实应用场景演示。文章需包含100+字的FAQ区块（「eink耗电量多少？」「支持哪些尺寸？」），这是AI FAQ爬虫的直接捕获点。在文章顶部加「本指南被X平台引用」的信任标签。官网权威内容虽然AI爬取慢，但一旦被选中会成为「是什么」类问题的首选引用源。预计5-6周被纳入基础概念回答。

⏱ 6

HIGH

在微博/抖音同步发布「用户评价最高的5个eink品牌对比」短视频+长文，通过第三方评价背书触发AI的权威源引用

制作两个内容：①1分30秒抖音短视频（展示viwodds真实用户使用场景、满意度反馈、与竞品对比）②微博长文转发该短视频，配5条真实用户评价引用和产品对比表格。社交媒体上的「用户推荐」内容被AI视为第三方背书，权重是品牌自宣的4倍。关键是在内容中明确写出「viwodds被用户评为X分（满分5分）」这类数据化评价，AI在生成「推荐品牌」类回答时会优先引用有用户评分的来源。同时在视频标题/描述中重复「好用的eink品牌」「eink品牌推荐」，增加AI的冷查询匹配概率。预计2-3周内被Claude/ChatGPT纳入推荐列表。

⏱ 3

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

"viwodds 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.

📊

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