Structural Authority Score (SAS)
Structural Authority Score (SAS) is a deterministic, additive metric that measures how structurally ready a business is to be cited by AI systems. SAS evaluates 8 independent dimensions of AI extraction readiness and produces a transparent, reproducible score verified by SHA-256 hash comparison.
The 8 Dimensions of Structural Authority
Each dimension captures a distinct aspect of how AI systems evaluate a source for citation readiness. Dimensions are scored independently on a 0.0 to 1.0 scale.
1. Entity Identity Clarity (EIC)
Measures whether AI can confidently identify who the business is. Evaluates Organization schema presence, consistent naming across the page, and entity disambiguation signals like sameAs links.
Low score causes: Missing Organization schema, inconsistent business naming, no sameAs links, ambiguous entity identity.
2. Structured Data Density (SDD)
Measures the depth and breadth of machine-readable schema markup. Evaluates JSON-LD variety, @graph cross-referencing, schema type count, and structured data completeness.
Low score causes: No JSON-LD present, single schema type only, no @graph structure, missing cross-references between entities.
3. Semantic Structure Integrity (SSI)
Measures whether content follows a parseable semantic hierarchy. Evaluates H1-H6 heading structure, proper nesting, list usage, definition blocks, and absence of heading level skips.
Low score causes: Skipped heading levels (H1 to H3), div soup without semantic elements, missing lists, no definition blocks.
4. Service Clarity Depth (SCD)
Measures how clearly services are defined and structured. Evaluates whether service pages exist with deliverable descriptions, outcome definitions, measurement criteria, and Service schema markup.
Low score causes: Vague “we help businesses grow” language, no specific service definitions, no Service schema, no deliverable specifications.
5. Cross-Domain Authority (CDS)
Measures external authority reinforcement signals. Evaluates sameAs links to authoritative platforms, entity consistency across domains, and cross-domain identity verification.
Low score causes: No sameAs links, no LinkedIn/GitHub/social references, inconsistent entity names across platforms.
6. FAQ Intent Coverage (FIC)
Measures structured question-answer content availability. Evaluates FAQPage schema presence, question count, topic breadth, and answer substance. FAQ content provides direct extraction targets for AI question-answering systems.
Low score causes: No FAQPage schema, fewer than 10 Q&A pairs, thin or generic answers, narrow topic coverage.
7. Crawl Accessibility (CAD)
Measures how easily AI crawlers can access and parse content. Evaluates response time, server-side rendering availability, robots.txt permissions, and content delivery method (SSR vs client-side only).
Low score causes: Slow response time, client-side-only rendering, blocked AI crawlers in robots.txt, heavy JavaScript dependency.
8. Consistency Coherence (CCH)
Measures internal consistency across the site. Evaluates naming coherence (same entity referenced the same way), entity definition alignment, and structural pattern uniformity across pages.
Low score causes: Inconsistent business name usage, contradictory descriptions, mixed terminology, structural pattern divergence between pages.
Calculation Model
SAS uses a pure additive weighted model. Each dimension contributes linearly and transparently to the final score.
SAS = w1×EIC + w2×SDD + w3×SSI + w4×SCD + w5×CDS + w6×FIC + w7×CAD + w8×CCH
Design Decisions
- Additive, not nonlinear: Improvements translate directly and proportionally. No hidden thresholds or diminishing returns.
- Deterministic: SHA-256 hash verification on every scan. Stable JSON serialization with sorted keys eliminates object-order non-determinism.
- Transparent weights: All weights are published and fixed during calibration windows. No secret adjustments.
- Immediate reflection: Structural improvements produce instant SAS changes. Capability is not smoothed or delayed.
- Calibrated: Validated against 156+ probe sites across WordPress, Shopify, Wix, Squarespace, Webflow, Next.js, Hugo, and custom platforms.
SAS Grading Scale
| Score Range | Grade | Meaning |
|---|---|---|
| 70–100 | A / A+ | AI citation ready. Strong structural authority across most dimensions. |
| 50–69 | B / B+ | Moderate structural authority. Specific dimension deficits identifiable. |
| 35–49 | C / D+ | Weak structural authority. Multiple missing structural signals. |
| 0–34 | D / F | Structurally invisible to AI. Fundamental structural work required. |
Calibration Methodology
SAS undergoes rigorous calibration to ensure accuracy, stability, and fairness across all CMS platforms and verticals.
- Probe Observatory: 156+ curated probe sites across 8 CMS types, multiple verticals, and diverse structural patterns.
- Determinism Testing: Every probe scanned 3+ times. SHA-256 hash must match on every repeated scan.
- Distribution Analysis: SAS distribution monitored for compression, skew, and bimodal artifacts.
- Dimension Correlation: No single dimension explains more than 35% of total SAS variance.
- Weight Sensitivity: Simulated weight perturbations confirm ranking stability.
- Adversarial Testing: 20 adversarial probes specifically designed to stress schema inflation, FAQ spam, headless rendering, floor compression, and enterprise complexity.
- 60-Day Calibration Windows: Weights locked during observation periods. Only extraction bug fixes allowed.
Frequently Asked Questions
What is Structural Authority Score?
SAS is a deterministic, additive metric that measures how structurally ready a business is to be cited by AI systems across 8 independent dimensions.
Is SAS deterministic?
Yes. Given identical HTML input, SAS produces identical output every time. Verified through SHA-256 hash comparison using stable JSON serialization with sorted keys.
How is SAS calibrated?
SAS is calibrated against 156+ probe sites across 8 CMS types with determinism testing, distribution analysis, correlation studies, weight sensitivity simulation, and adversarial testing during 60-day windows.
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