AI result baseline
Visibility, recommendation tone, reason completeness, and competitor comparison across AI engines.
StarMap Index 2.0
AI Recommendation Gravity Model.
StarMap Index measures whether a brand can be understood, trusted, compared favorably, and selected as an AI recommendation candidate in a specific demand context.
Definition
StarMap 2.0 is not a GEO checklist. It is an AI recommendation gravity diagnostic.
The model scores 100 points across 4 layers, 8 dimensions, and 24 indicators. It evaluates current AI results, brand cognition, content and evidence assets, cross-platform proof, and machine-readable distribution readiness.
100 pointsA complete model for AI recommendation readiness.
4 layersAI results, brand cognition, evidence assets, and machine distribution.
8 dimensionsFrom AI visibility to crawl and knowledge distribution readiness.
24 indicatorsConcrete scoring signals that turn diagnosis into an engineering roadmap.
4 layers: from AI results to knowledge distribution.
| Layer | Points | What It Measures | Core Question |
|---|---|---|---|
| A. AI Result Performance | 20 | AI visibility and recommendation strength | Does AI already see, understand, and recommend the brand? |
| B. Brand Cognition Structure | 25 | Brand value pole and high-dimensional demand-supply fit | What does the brand represent, and when should it be recommended? |
| C. Content and Evidence Assets | 35 | High-density content, evidence chain, cross-platform corroboration | Is there enough consistent evidence for AI to trust the brand? |
| D. Machine Readability and Distribution | 20 | Crawlability, structured meaning, indexing, freshness, distribution | Can machines reliably read, index, update, and distribute brand knowledge? |
8 dimensions and 24 indicators.
| Dimension | Points | Indicators | Focus |
|---|---|---|---|
| 1. AI Visibility | 10 | Mention rate, demand-trigger rate, answer position and stability | Whether the brand appears in target AI queries. |
| 2. AI Recommendation Strength | 10 | Recommendation tone, reason completeness, competitor win rate | Whether AI clearly recommends the brand instead of merely listing it. |
| 3. Brand Value Pole | 13 | Category clarity, differentiation strength, fit and non-fit boundaries | Whether the brand has a clear recommendation center. |
| 4. High-Dimensional Demand-Supply Fit | 12 | Scenario coverage, supply granularity, demand-supply mapping | Whether brand capability maps to real user demand. |
| 5. High-Density Content Assets | 12 | Atomic pages, scenario pages, FAQ / comparison / decision pages | Whether content is clear, dense, extractable, and citable. |
| 6. Evidence Chain Trustworthiness | 13 | Third-party data, cases and reviews, multimodal evidence | Whether AI has enough proof to support its recommendation. |
| 7. Cross-Platform Corroboration | 10 | Website / social / map / media consistency, external source coverage, user consensus | Whether the brand is consistently validated across platforms. |
| 8. AI Crawl and Knowledge Distribution Readiness | 20 | Crawl access, structured semantics, updates and external distribution | Whether machines can read, index, understand, and distribute the brand. |
AI Recommendation Gravity
AI recommendation is not magic. It is gravity created by demand, supply, evidence, and readability.
AI Recommendation Gravity = Visibility × Recommendation Strength × Value Pole × Demand-Supply Fit × Evidence Assets × Cross-Platform Proof × Machine Distribution
Value Pole
A value pole is not a slogan. It is the core recommendation reason that both AI and customers can understand.
Diagnostic outputs.
Brand cognition gaps
Value pole clarity, fit boundaries, and high-dimensional demand-supply mapping.
Content and evidence gaps
Atomic pages, scenario pages, FAQs, cases, third-party proof, and cross-platform consistency.
Machine-readability gaps
Crawlability, indexing, Schema, Sitemap, llms.txt, internal links, freshness, and distribution.
100-point scorecard
8 dimensions and 24 indicators showing where recommendation gravity is strong or weak.
Engineering roadmap
Pages, evidence libraries, external sources, prompt libraries, conversion paths, and maintenance cadence.
4 AI Marketing Service Products.
AI Recommendation Diagnostics / Competitor Intelligence
Brand entity diagnosis, prompt testing, competitor recommendation comparison, content-structure scoring, StarMap Index 2.0 initial assessment, and an improvement roadmap.
GEO Brand Engineering Planning
AI-brain brand positioning, human-brain brand positioning, brand value pole, new semantic-system knowledge graph, high-dimensional demand map, high-dimensional supply map, core prompt question bank, atomic page matrix, high-density gravity pages, evidence-chain hub pages, page structuring, Schema semantic standards, Json-LD language processing, Q&A design, external content planning, production and publishing, and page performance tuning.
AI-Recommendable Brand Website
From a 10-15 page starter site, to a 30-50 page standard site, to a 50-100 page AI brand engineering site. Over a six-month cycle, we build long-term recommendation assets and keep updating content to maintain competitiveness in the semantic vector space.
AI Integrated Marketing and Sales (Planning…)
Use AI tools to connect video content traffic, digital ad traffic, SEO / GEO traffic, and private-domain traffic conversion.