Audit Dimensions
Every OptimizeCamp audit evaluates your content across four core dimensions. Each dimension targets a different aspect of what makes AI models choose to cite a piece of content.
Accuracy
The Accuracy engine verifies every factual claim in your content.
What it checks:
- Claim extraction — Identifies every verifiable statement (statistics, dates, facts, attributions).
- Source verification — Checks claims against current, authoritative sources.
- Freshness — Flags outdated data with the latest available figures.
- Coherence — Ensures claims don't contradict each other within the article.
Scoring: Each claim is scored as verified, unverified, or outdated. Your accuracy score reflects the percentage of claims that pass verification, weighted by claim significance.
Why it matters: AI models are trained to prefer factually accurate sources. A single outdated statistic can cause an AI to skip your content entirely in favor of a competitor.
Authority
The Authority engine measures how comprehensively you cover your topic compared to the best content currently ranking.
What it checks:
- SERP analysis — Fetches and analyzes top-ranking pages for your target keyword.
- Subtopic coverage — Maps every subtopic covered by competitors and checks if you address them.
- Content gaps — Identifies specific topics, data points, and formats you're missing.
- Depth comparison — Measures the depth of your coverage versus competitors.
Scoring: Your authority score is based on the percentage of identified subtopics you cover, weighted by subtopic importance.
Why it matters: AI models synthesize answers from the most comprehensive sources. If a competitor covers 15 subtopics and you cover 8, the AI will prefer their content.
Citation Readiness (GEO)
The Citation Readiness engine evaluates how easily an AI model can extract and cite information from your content. This is based on Generative Engine Optimization (GEO) principles.
What it checks across 8 sub-dimensions:
- Clarity — Sentence-level readability and plain language usage.
- Structure — Heading hierarchy, logical flow, scannable formatting.
- Quotability — Presence of concise, self-contained statements AI can directly quote.
- Entity richness — Named entities, specific figures, proper nouns.
- Data richness — Statistics, percentages, concrete numbers.
- Freshness signals — Date references, recency indicators.
- Schema markup — Structured data that helps AI understand content type.
- FAQ patterns — Question-answer pairs that map to common queries.
Scoring: Each sub-dimension is scored 0–100. The Citation Readiness score is a weighted average, with structure and clarity weighted highest.
Why it matters: Even accurate, comprehensive content gets skipped if AI can't easily parse and extract citable statements from it.
AI Search Coverage
The AI Search Coverage engine reveals the hidden sub-queries that AI search engines generate for your target keyword — and checks whether your content answers them.
What it checks:
- Query fan-out — Simulates the sub-queries AI would generate for your keyword (clarifying, comparative, follow-up, personalized, implicit).
- Content matching — Checks each sub-query against your content to determine coverage status.
- Gap identification — Finds sub-queries your content doesn't address at all.
- Passage generation — Generates ready-to-insert passages that fill coverage gaps (Pro+ plans).
Scoring: Your AI search coverage percentage is based on the proportion of sub-queries your content addresses. Covered sub-queries count fully, partial coverage counts at half weight.
Why it matters: AI engines decompose every search into hidden sub-queries. If your content doesn't answer those sub-queries, you don't get cited — even if your page title matches the original keyword. For a deeper dive, see the AI Search Coverage guide.