Direct conclusion: StarMap Index 2.0 does not measure an SEO score or traditional website health. It measures whether a brand has AI recommendation gravity.
One-Sentence Definition
StarMap Index measures whether a brand can be understood, trusted, compared favorably, and selected as a recommendation candidate by AI systems in a specific demand context.
It is used for III's internal methodology, GEO / AIO evaluation, AI brand engineering diagnostics, website content-capability assessment, and future Codex implementation tasks.
Why Upgrade From 1.0 to 2.0?
StarMap Index 1.0 focused on whether a website had built the basic GEO infrastructure. StarMap Index 2.0 asks a more important question: does this brand have the structural conditions to become a top-three AI recommendation candidate in a specific demand context?
That means 2.0 no longer looks only at page count, Schema, Sitemap, FAQ, or crawlability. It also evaluates whether AI already sees the brand, whether the brand has a clear value pole, whether content has become high-density knowledge assets, whether the evidence chain is strong enough, whether cross-platform corroboration exists, and whether machines can reliably crawl, understand, and distribute brand knowledge.
The 4-Layer Architecture
| Layer | Dimensions | Points | Core Judgment |
|---|---|---|---|
| A. AI Result Performance | AI visibility, AI recommendation strength | 20 | Whether the brand is already seen, understood, and recommended in AI answers. |
| B. Brand Cognition Structure | Brand value pole, high-dimensional demand-supply fit | 25 | Whether the brand has clear positioning and maps to real, high-intent demand. |
| C. Content and Evidence Assets | High-density content assets, evidence-chain trustworthiness, cross-platform corroboration | 35 | Whether content, cases, data, images, video, and external sources support AI judgment. |
| D. Machine Readability and Distribution | AI crawl and knowledge-distribution readiness | 20 | Whether the site can be crawled, indexed, understood, and continuously distributed by machines. |
| Total | 8 dimensions, 24 indicators | 100 | Overall AI recommendation gravity. |
8 Dimensions and 24 Indicators
| Dimension | Points | 3 Indicators | AI Question |
|---|---|---|---|
| 1. AI Visibility | 10 | Brand mention rate, demand-scenario trigger rate, answer position and exposure stability | Does AI know the brand and mention it in core demand contexts? |
| 2. AI Recommendation Strength | 10 | Recommendation tone, reason completeness, competitor comparison win rate | Does AI merely list the brand, or does it actually recommend it? |
| 3. Brand Value Pole | 13 | Category clarity, differentiation strength, fit and non-fit boundaries | What does the brand represent, and why not someone else? |
| 4. High-Dimensional Demand-Supply Fit | 12 | Scenario coverage, supply granularity, demand-supply mapping completeness | Can brand capability match complex real user demand? |
| 5. High-Density Content Assets | 12 | Atomic page completeness, scenario page completeness, FAQ / comparison / decision page completeness | Is the content clear, dense, and absorbable by AI? |
| 6. Evidence-Chain Trustworthiness | 13 | Data and third-party sources, cases and review proof, multimodal evidence | Why should AI trust the brand? |
| 7. Cross-Platform Corroboration | 10 | Website / social / map / media consistency, external-source coverage, user consensus and activity | Is brand information consistent, stable, and verifiable across platforms? |
| 8. AI Crawl and Knowledge-Distribution Readiness | 20 | Structured data and Schema, Sitemap / robots / indexing friendliness, update frequency and knowledge distribution | Can machines reliably crawl, understand, index, and distribute brand knowledge? |
High-Dimensional Demand and Supply
The core of StarMap 2.0 is not adding more keywords. It is decomposing user demand and brand supply into structures that AI can understand.
Demand Structure
Actor + Situation + Pain + Desired Outcome + Constraints + Risk Model + Decision Criteria + Evidence Threshold + Action Path
Supply Structure
Capability + Target Demand + Transformation + Evidence + Boundary + Comparative Advantage
Citable assertion: AI recommendation is not based on keywords alone. It is formed through the whole knowledge structure. Brand content must turn supply capability into high-dimensional supply vectors that AI can understand.
Scoring Method
The full StarMap Index score is 100. Each indicator is first rated from 0 to 5, then converted by its weight. The converted indicator scores are summed into 8 dimension scores and the final total score.
| Raw Score | Meaning |
|---|---|
| 0 | Completely absent. |
| 1 | Very weak; almost unable to support AI recommendation. |
| 2 | Basic but incomplete or unstable. |
| 3 | Usable level. |
| 4 | Good performance; supports partial AI recommendation. |
| 5 | Excellent performance; can stably support AI recommendation. |
Formula: indicator score = indicator weight x raw score / 5.
Score Levels
| Total Score | Level | Meaning |
|---|---|---|
| 90-100 | S | Strong AI recommendation gravity; can serve as a benchmark case. |
| 80-89 | A | Clear recommendation potential; ready for strengthening and expansion. |
| 70-79 | B | Good foundation, but brand cognition or evidence chain still needs work. |
| 60-69 | C | Some GEO foundation, but AI recommendation reasons are insufficient. |
| 40-59 | D | Clear weaknesses in content, brand, evidence, or technical structure. |
| 0-39 | E | Not yet ready for AI recommendation; brand and content need rebuilding. |
What the Diagnostic Report Outputs
Brand / website, evaluation date, target market, core services, demand scenarios, total score, and one-sentence diagnosis.
Scores, full marks, and comments across 4 layers and 8 dimensions.
Tasks grouped by AI recommendation reasons, AI trust, and AI crawl / distribution.
Content planning, evidence-chain strengthening, platform corroboration, and technical repair.
Relationship to SEO / GEO / AIO
SEO mainly asks whether a search engine can find a page. GEO asks whether generative AI can extract and use the content. AIO is broader: it includes how a brand is understood, trusted, compared, and recommended in the AI world.
StarMap Index is one AIO diagnostic model. It retains GEO's technical foundation, but gives greater weight to brand value pole, high-dimensional demand-supply fit, recommendation reason generation, cross-platform proof, and actual AI answer performance.
FAQ
How is StarMap Index 2.0 related to AI Brand Engineering?
StarMap Index 2.0 is the diagnostic model. AI Brand Engineering is the construction method. StarMap identifies the gaps; AI Brand Engineering builds the entity, value pole, content assets, evidence chain, and machine-readable structure.
Does a high technical score guarantee AI recommendation?
No. Technical structure improves crawlability and understanding, but recommendation also depends on positioning, demand fit, evidence, competitive advantage, and external sources.
What should be improved first?
Usually the brand value pole and evidence chain. If AI does not know why the brand deserves recommendation, more pages alone will not create stable recommendation reasons.