A multi-dimensional quality assessment of content that measures readability, SEO structure, originality, authority signals, depth, and AI citability.
Content scoring is the practice of evaluating content quality across multiple dimensions using a structured rubric rather than subjective judgment. A content score typically combines automated analysis (readability formulas, keyword density, structural checks) with AI-powered evaluation (originality assessment, E-E-A-T signal detection, depth analysis).
Effective content scoring goes beyond simple readability metrics like Flesch-Kincaid. It evaluates whether content demonstrates expertise, provides actionable takeaways, cites specific evidence, and is structured for both human readers and AI extraction.
Without objective quality measurement, content teams rely on gut feelings about whether a piece is "good enough" to publish. This leads to inconsistent quality, especially at scale. Content scoring provides a repeatable standard that catches quality issues before publishing.
For AI-generated content specifically, scoring is essential because AI can produce text that reads smoothly but lacks substance, specificity, or genuine expertise. A good scoring system detects these patterns.
Acta AI includes the Acta Score, a 6-dimension content health assessment that evaluates every article on Readability, SEO Structure, Originality, E-E-A-T, Depth, and GEO Citability. Five dimensions are computed locally at zero cost, and one (Depth) uses a GPT-4o evaluation. The score is computed automatically at the end of the content pipeline and recalculated when articles are edited.
Learn more about this featureAn article might score 85/100 overall but flag a weak Originality score (65) due to overuse of common AI phrases. The content team can then use the "Revise with AI" feature to improve the flagged dimension before publishing.
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