How the AI Answerability Index Works

The Analysis Process Overview

The AI Answerability Index uses a sophisticated multi-stage analysis process to evaluate your content. Understanding this process helps you appreciate what the scores represent and how to act on the recommendations you receive.

The complete analysis flows through five distinct stages. First, we fetch and parse your content to understand its structure. Second, we extract key elements including text, markup, and metadata. Third, AI systems evaluate the content against our 106-point framework. Fourth, we calculate weighted scores across all dimensions. Fifth, we generate prioritized recommendations based on your specific results.

This process typically completes within 30 to 60 seconds for a single page, though complex pages with extensive content may take slightly longer. The speed allows for rapid iteration as you make improvements and want to verify their impact.

Why Multiple Stages Matter

Breaking the analysis into distinct stages serves several purposes. It ensures thorough evaluation without missing important factors. It allows each stage to build on information gathered in previous stages. Most importantly, it produces consistent, reproducible results that you can trust and compare over time.

The staged approach also allows us to handle different types of content appropriately. A product page requires different emphasis than a blog article or a service description. By processing content through structured stages, we can adapt the evaluation to match the content type while maintaining consistent scoring methodology.

Content Fetching and Parsing

The first stage of analysis involves fetching your page content and parsing it into a structured format suitable for evaluation. This stage is more complex than simply downloading HTML because we need to process the page as AI systems would encounter it.

Page Retrieval

When you submit a URL for analysis, our system retrieves the page using methods that simulate how AI crawlers access content. We follow the same protocols that major AI systems use, including respect for robots.txt directives and appropriate request headers. This ensures our analysis reflects what AI systems actually see when they access your pages.

We handle JavaScript-rendered content appropriately, recognizing that many modern websites rely on client-side rendering. Pages that require JavaScript execution are processed in a way that captures the fully rendered content, not just the initial HTML source.

Structure Extraction

Once retrieved, the page content undergoes parsing to extract its structural elements. This includes identifying heading hierarchies, paragraph boundaries, list structures, tables, and other organizational elements. We also extract metadata including title tags, meta descriptions, and Open Graph properties.

The parsing process pays special attention to content organization. We identify the main content area distinct from navigation, sidebars, and footers. This distinction matters because AI systems similarly focus on primary content when extracting information for answers.

Schema Markup Detection

We identify and parse all structured data markup present on the page. This includes JSON-LD scripts, Microdata attributes, and RDFa markup. Each schema object is validated for proper syntax and evaluated for completeness relative to its type.

Schema detection is particularly important because structured data provides explicit signals that AI systems use for entity recognition and relationship mapping. Missing or malformed schema reduces your content's machine readability and can significantly impact your answerability score.

AI-Powered Evaluation

The core of the AI Answerability Index involves evaluation by advanced language models. We use AI to evaluate AI-readiness because this approach directly tests how AI systems interpret and understand your content.

Dimension Analysis

Each of the seven dimensions receives dedicated AI analysis. The AI evaluates your content against specific criteria for each dimension, identifying both strengths and weaknesses. This analysis goes beyond simple pattern matching to understand context, meaning, and effectiveness.

For example, when evaluating Entity Authority, the AI examines whether people, organizations, and products are clearly identified and properly attributed. It looks for contextual signals that establish expertise and trustworthiness. It checks whether entity relationships are explicit or must be inferred.

Check Execution

Within each dimension, the 106 individual checks are executed systematically. Each check examines a specific aspect of your content and produces a pass, partial, or fail result. Some checks are binary while others allow for gradations based on how well criteria are met.

Checks are designed to be specific and actionable. Rather than vague assessments like "content quality is moderate," our checks identify concrete issues like "primary heading does not include main topic keyword" or "product descriptions lack specific attribute values."

Context Sensitivity

The AI evaluation considers the context of your content. A page about technical documentation is evaluated differently than a page about local services. This context sensitivity ensures that recommendations are relevant and achievable for your specific content type.

We also consider industry norms when applicable. Certain schema types are more relevant for e-commerce than for informational content. Question readiness requirements differ between how-to content and news articles. The evaluation adapts to these contextual factors.

Score Calculation Methodology

After individual checks complete, scores are calculated using a weighted aggregation methodology. Understanding this calculation helps you interpret what your scores mean and where to focus improvement efforts.

Individual Check Scoring

Each of the 106 checks produces a score on a standardized scale. Checks that pass fully receive maximum points for that check. Checks that partially pass receive proportional points based on the degree of success. Checks that fail receive zero points.

Not all checks carry equal weight. Checks that measure factors with greater impact on AI citation receive higher weights. For example, checks related to entity identification and schema completeness carry higher weights than checks related to minor structural preferences.

Dimension Aggregation

Check scores within each dimension are aggregated to produce dimension subscores. These subscores are normalized to a 0 to 100 scale for easy interpretation. You can compare your performance across dimensions using these subscores.

Each dimension score reflects the weighted sum of its constituent checks. Dimensions with more checks do not automatically receive more influence on the overall score. The weighting system balances dimensions based on their relative importance to AI answerability.

Overall Score Composition

The final AI Answerability Index score combines all dimension subscores using another layer of weights. These weights reflect extensive research into how AI systems prioritize different content factors when selecting sources for citation.

The current dimension weights are:

• Parseability: 12% of overall score
• Clarity: 15% of overall score
• Entity Authority: 18% of overall score
• Question Readiness: 17% of overall score
• AI Accessibility: 10% of overall score
• Schema Completeness: 16% of overall score
• Crawl Health: 12% of overall score

These weights may be adjusted over time as AI systems evolve and new research provides insights into citation behavior.

Generating Recommendations

The analysis produces not just scores but actionable recommendations for improvement. These recommendations are specific to your content and prioritized based on potential impact.

Issue Identification

When checks fail or partially pass, the system identifies the specific issues causing the result. These issues are documented with enough detail for you to understand exactly what needs to change. Where possible, we include examples of the problematic content.

Issues are categorized by type including content issues, structural issues, markup issues, and technical issues. This categorization helps you route issues to the appropriate team members for resolution.

Priority Assignment

Not all issues deserve equal attention. We assign priority levels based on the potential score impact of addressing each issue. High-priority issues are those that affect high-weight checks or multiple checks simultaneously. Low-priority issues are those with minimal score impact.

Priority also considers effort. When two issues offer similar score improvements, the one requiring less effort to fix receives higher priority. This approach maximizes the return on your optimization efforts.

Implementation Guidance

Recommendations include implementation guidance where appropriate. For schema issues, we provide the specific markup that should be added or corrected. For content issues, we explain what changes would resolve the issue. For technical issues, we describe the configuration changes needed.

This guidance is designed to be actionable by your team. Content recommendations can be implemented by writers. Schema recommendations can be implemented by developers. Technical recommendations can be implemented by system administrators.

Report Delivery and Formats

Analysis results are delivered through multiple channels to serve different use cases. Understanding these delivery options helps you integrate the AI Answerability Index into your workflows.

Interactive Dashboard

The primary delivery method is through your interactive dashboard. Here you can explore your results in detail, drill down into specific dimensions, and review individual check results. The dashboard updates in real-time during analysis so you can track progress.

The dashboard also maintains historical data, allowing you to track score changes over time. This historical view helps you verify that improvements are working and catch any regressions quickly.

PDF Reports

For sharing with stakeholders or archiving, you can generate PDF reports of your analysis. These reports include your overall score, dimension subscores, key findings, and prioritized recommendations. They are formatted professionally for presentation to clients or leadership.

PDF reports can be customized with your branding including logo and colors. This feature is particularly valuable for agencies delivering AI Answerability audits as a client service.

Data Export

For integration with other systems or detailed analysis, you can export your results in structured data formats. Exports include complete check results, scores, and recommendations in formats suitable for spreadsheet analysis or system integration.