How DecodeIQ Works: The Semantic Analysis Methodology

Executive Summary {#executive-summary}

DecodeIQ analyzes content for AI retrievability using semantic extraction. The input is any public URL or pasted draft text. The output is a metrics dashboard with scores, a prioritized fix list ranked by impact, and example rewrites you can copy directly. Analysis completes in approximately 60 seconds.

The methodology is mechanism-first: we explain exactly how each metric is calculated and what it predicts. This page documents our semantic extraction pipeline, the research behind our target ranges, and the limitations of predictive analysis. No black boxes.

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Part 1: What DecodeIQ Analyzes {#what-decodeiq-analyzes}

The Problem We Solve

Traditional SEO tools measure keywords, backlinks, and SERP rankings. These metrics predict visibility in traditional search results. They do not measure what AI systems evaluate when deciding whether to cite content.

AI retrieval systems evaluate semantic structure: the entities in your content, how clearly they are defined, how they relate to each other, and whether the content maintains logical coherence. A page can rank #1 on Google and still be invisible to ChatGPT, Claude, and Perplexity because it lacks the semantic architecture these systems require.

No tool existed to measure what AI systems actually evaluate. DecodeIQ bridges this gap with semantic analysis designed specifically for AI retrieval prediction.

Three Steps to Semantic Analysis

Step 1: Input Your Content

Two input methods are supported:

• Paste any public URL. We fetch the page, parse the content, and strip navigation, footers, and ads to extract the main body.
• Paste draft text directly. For pre-publication validation, paste your content as plain text or markdown. Content is processed in memory and not stored after analysis.

Works with blog posts, landing pages, documentation, product pages, guides, and any substantive technology or SaaS content. Start with a 7-day free trial on any plan.

Step 2: Semantic Extraction Pipeline

Analysis completes in approximately 60 seconds. The pipeline performs:

• Entity identification and extraction using Named Entity Recognition
• Relationship mapping between concepts via co-occurrence analysis
• Density and coherence calculation against established targets
• Retrieval confidence prediction by comparing against our calibrated corpus of 1,200+ high-performing technology articles with verified AI citations

Step 3: Actionable Results

Results include:

• Metrics dashboard with scores for Semantic Density, Contextual Coherence, Retrieval Confidence, and DecodeScore
• Prioritized fix list ranked by impact, with highest-value improvements first
• Example rewrites you can copy directly (Starter and Pro plans)
• Before/after comparisons showing the effect of suggested changes
• Links to methodology documentation for each metric, so you understand why each recommendation matters

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Part 2: The Semantic Extraction Pipeline {#semantic-extraction-pipeline}

This section documents exactly how DecodeIQ processes content. We explain each stage so you understand the methodology, not just the output.

Stage 1: Content Ingestion

For URLs:

• Fetch page content via headless browser to capture JavaScript-rendered content
• Extract main content by stripping navigation, sidebars, footers, ads, and boilerplate
• Preserve semantic HTML structure (headings, lists, tables, paragraphs)
• Handle dynamic content and single-page applications

For Pasted Text:

• Accept raw text or markdown formatting
• Parse structure from formatting cues (headings, bullets, paragraph breaks)
• Content processed in memory, not stored after analysis completes

Stage 2: Entity Extraction

Methodology: Named Entity Recognition (NER) using spaCy pipeline

What we identify:

• Named entities: People, organizations, products, locations, technologies
• Technical concepts: Domain terminology, industry jargon, technical processes
• Relationships: Entity co-occurrence patterns, dependency structures
• Entity boundaries: Where concepts begin and end, how they are defined

Why it matters: AI retrieval systems evaluate content at the entity level. When you mention "usage-based pricing" without defining it, AI systems have lower confidence in your content as an authoritative source. Content rich in clearly-defined entities creates stronger vector representations that match more query patterns.

Stage 3: Relationship Mapping

Methodology: Co-occurrence analysis and dependency parsing

What we map:

• Semantic clusters: Which entities appear together and reinforce each other
• Explicit relationships: Statements like "X enables Y" or "A differs from B in that"
• Relationship density: How interconnected is the conceptual structure
• Missing relationships: Entities mentioned but not connected to other concepts

Why it matters: AI systems do not just retrieve entities, they retrieve context. When your content explicitly maps how concepts relate, AI systems can cite your content accurately. Implicit relationships require inference, which reduces citation confidence.

Stage 4: Semantic Density Calculation

Formula: (Unique entities + Explicit relationships) / Word count × 1,000

Output: Percentage score (0-10%)

Target: 4-6%

Interpretation:

• Below 4%: Insufficient entity coverage. Content appears superficial to AI systems because it lacks the conceptual richness required for confident retrieval.
• 4-6%: Optimal range. Content demonstrates comprehensive coverage without being over-packed. This range produces the strongest correlation with AI citation.
• Above 6%: Risk of entity stuffing. Readability may suffer, and quality filters may flag the content as over-optimized.

Validation: Analysis of 50,000+ technology pages with tracked citation outcomes shows content in the 4-6% range receives 3.2x more citations than content below 4%. Content above 6% shows 2.1x fewer citations than the optimal range. These thresholds derive from our curated calibration corpus of 1,247 articles meeting strict inclusion criteria.

Stage 5: Contextual Coherence Scoring

Methodology: Vector similarity of adjacent section embeddings

What we measure:

• Logical flow: Do concepts progress naturally from section to section?
• Terminology consistency: Are the same terms used throughout, or does terminology drift?
• Entity persistence: Are concepts introduced, developed, and concluded appropriately?
• Topic drift signals: Does content wander off-topic or introduce contradictions?

Output: Score 0-100

Target: 80+

Interpretation:

• Below 60: Significant coherence issues. AI systems struggle to categorize scattered content, reducing retrieval probability.
• 60-79: Moderate coherence. Improvement opportunities exist. Content may be retrieved but cited less confidently.
• 80+: Strong coherence. Clear semantic structure signals focused expertise. AI systems preferentially cite coherent sources.

Validation: Content scoring 80+ coherence receives 2.4x higher AI citations than content below 80, based on analysis of our calibration corpus. See threshold derivation methodology.

Stage 6: Retrieval Confidence Prediction

Methodology: Semantic proximity to high-performing content corpus (n=1,200+ technology articles with verified AI citations)

What we compare:

• Your content's semantic signature versus successfully-cited content in the same category
• Entity coverage relative to topic expectations (what concepts should be present for this topic)
• Structural patterns that correlate with AI citation (definition placement, relationship density, coherence)

Output: Score 0-100

Target: 60+ baseline, 75+ for technical content

Interpretation:

• Below 40: Low citation probability. Significant structural issues prevent AI systems from confidently retrieving and citing this content.
• 40-59: Moderate probability. Targeted improvements can move content into competitive range.
• 60-74: Good probability. Content has baseline retrievability and can compete for AI citations with further optimization.
• 75+: Strong probability. Well-positioned for AI citation. Content demonstrates the semantic architecture AI systems prefer.

Validation: Content scoring 75+ Retrieval Confidence shows 5.2x higher citation rates than content scoring below 40. Model accuracy validated at 74% on held-out test set. See validation methodology.

Stage 7: Recommendation Generation

What we produce:

1. Prioritized Fix List: Improvements ranked by impact. Each fix includes the problem identified, why it matters for AI retrieval, and estimated effort to implement.

2. Example Rewrites: Specific text suggestions with before/after comparisons. Available on Starter and Pro plans. You can copy these directly into your content.

3. Entity Gap Analysis: Missing concepts compared to topic expectations. If content about "API authentication" lacks OAuth or JWT definitions, we identify this gap.

4. Coherence Improvements: Specific locations where topic drift occurs or where transitions would strengthen flow.

Philosophy: Recommendations are actionable, not vague. Each suggestion includes specific text changes you can implement immediately. "Improve your semantic density" is not helpful. "Add this definition for 'usage-based pricing' in paragraph 3" is helpful.

Part 3: Site-Level Analysis (Coming Soon) {#site-level-analysis}

Why Site Coherence Matters

Single-page analysis reveals individual content quality. But AI systems also evaluate site-level signals that affect how every page on your domain is perceived.

The Google API documentation leak revealed these site-level semantic signals:

• siteFocusScore: How concentrated is your topical expertise across all pages? Sites covering narrow domains with depth score higher than sites covering broad domains superficially.
• siteRadius: How far does your content drift from core topics? Sites with consistent topical focus build stronger semantic authority.
• site2vecEmbeddings: What position does your entire site occupy in semantic space relative to authoritative sources?

These signals evaluate your entire domain, not just individual pages. One poorly-structured page or off-topic article can dilute semantic authority for all pages.

What Site-Level Analysis Will Provide

Coming soon:

• Domain Semantic Health Score: Overall site coherence and focus measurement
• Topic Cluster Mapping: Visual representation of your content's semantic territory
• Topic Drift Detection: Pages that dilute your semantic focus
• Coherence Gap Analysis: Where site-level structure breaks down
• Competitive Positioning: Your site's semantic signature versus competitors

Current capability: Up to 50 pages per analysis (coming soon)

Get Started

Start a free trial to explore page-level analysis and get notified when site-level features launch.

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Part 4: The Metrics at a Glance {#metrics-at-a-glance}

Quick reference table with links to detailed methodology:

Each metric has a detailed explanation page covering calculation methodology, interpretation guidelines, and improvement strategies.

Part 5: Why We Show Our Work {#why-we-show-our-work}

The Black Box Problem

Most SEO and content tools hide their methodology. You receive a score with no explanation of how it was calculated or what specifically to fix. This creates three problems:

Dependency on the tool. You cannot validate the score independently or understand whether it reflects reality.

Cargo cult optimization. You change things without understanding why. This leads to optimizing for scores rather than outcomes.

No learning. If you do not understand the methodology, you cannot internalize the principles and apply them to future content.

Our Philosophy: Mechanism-First

DecodeIQ operates on a different principle: every score must be explainable.

• We publish our methodology. You are reading it now. Every calculation we perform is documented. Our Calibration Methodology documents how we built and validated our scoring thresholds, including the 1,247-article corpus, statistical derivation methods, and cross-platform validation approach.
• We show the calculation behind each metric. Semantic Density is not a mysterious number. It is (Unique entities + Explicit relationships) / Word count × 1,000.
• We explain why each recommendation matters. Not just "add more entities" but "add a definition for this concept because AI systems cannot confidently cite content that mentions terms without explaining them."
• We connect metrics to the AI retrieval systems they predict. Our targets are based on analysis of what AI systems actually cite, not theoretical optimization ideals.

The goal: You should understand semantic architecture well enough to eventually not need us. If we have done our job, you internalize the principles and apply them instinctively to all future content.

What We Do Not Claim

Transparency includes acknowledging limitations:

We predict, not guarantee. Retrieval Confidence predicts citation probability based on semantic similarity to successfully-cited content. AI systems are complex, and their behavior can change. Prediction is not certainty.

Methodology evolves. As AI systems change their retrieval architectures and citation patterns, our analysis must adapt. We update our corpus and metric weights based on ongoing validation against actual citation outcomes.

Context matters. A 65 DecodeScore for a technical whitepaper has different implications than 65 for a product landing page. Benchmarks are guidelines derived from aggregate data, not absolute rules for every content type.

Correlation is not causation. Our validation data shows strong correlations between our metrics and AI citation rates. This suggests our metrics capture signals that matter for AI retrieval. It does not prove that improving these metrics will definitely increase your citations.

FAQs {#faqs}

How accurate is the semantic analysis?

Our metrics correlate with actual AI citation outcomes. Content scoring 75+ Retrieval Confidence shows 5.2x higher citation rates than content scoring below 40. Semantic density in the 4-6% range correlates with 3.2x higher citations than below 4%. These correlations are based on analysis of 50,000+ pages with tracked citation outcomes. However, correlation is not causation, and AI systems are complex. Treat metrics as strong predictive signals, not guarantees.

How long does analysis take?

Page-level analysis completes in approximately 60 seconds. This includes content fetching (for URLs), entity extraction, relationship mapping, density calculation, coherence scoring, and recommendation generation. Site-level analysis (coming soon) will take 5-15 minutes depending on page count.

What content types work best?

DecodeIQ works best with substantive content: blog posts, documentation, whitepapers, guides, product pages with descriptive text, and knowledge base articles. It works less well with image-heavy pages with minimal text, highly dynamic content (e.g., dashboards), very short pages (under 300 words), and content behind authentication. For best results, analyze content with at least 500 words of substantive text.

Can I analyze competitor pages?

Yes. Paste any public URL to analyze competitor content. This reveals their semantic density, coherence, and potential weaknesses (high Friction Index). Use competitor analysis to identify entity gaps and structural opportunities, not to copy their approach.

What is included in each plan?

All plans include a 7-day free trial. Credit card required, cancel before trial ends to avoid charges.

Basic ($29/mo): 10 pages/month, basic report with scores and entity analysis, top 3 fixes only, 48-hour report history. Best for occasional content audits.

Starter ($49/mo): 30 pages/month, full report with example fixes, all fixes with example rewrite text, 30-day report history. Best for regular content production.

Pro ($149/mo): 100 pages/month, full report with example fixes, unlimited report history, export capabilities (PDF/CSV), API access. Best for content teams and agencies.

When will site-level analysis be available?

Site-level analysis is in development. Start a free trial at app.decodeiq.ai to use page-level analysis today.

How do I know if the recommendations actually work?

Track your Share of Model before and after implementing recommendations. Run consistent queries across AI platforms (ChatGPT, Claude, Perplexity) at 2-week intervals. Citation improvements typically appear 30-60 days after content changes as AI systems re-crawl and re-index. DecodeIQ's Share of Model tracking (coming in future release) will automate this measurement.

Is my content stored or shared?

Pasted text is processed in memory and not stored after analysis completes. URLs may be cached briefly for performance. We do not share your content or analysis results with third parties. Enterprise plans (coming soon) will include additional data handling options for compliance requirements.

Start Your Analysis

DecodeIQ provides semantic analysis designed for AI retrievability. Input any URL or draft text. Receive metrics, prioritized fixes, and example rewrites in 60 seconds.

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