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S.01 · Structure & Readability

Measure the readability dimensions AI systems respond to.

Flesch-Kincaid grade level, passive voice percentage, sentence length distribution — the three readability dimensions research links to AI extraction probability. Grade 8–10 is the documented sweet spot.

ToolS.01 · v3.1
Built forWriters and editors
Time to use~ 2 minutes
OutputGrade · Passive % · Sentence distribution chart
SourceAggarwal et al. 2024 · peer-reviewed
SEO/GEO

Readability Analyser

Measure the clarity and complexity of your content — an established readability standard applied to writing for AI and human audiences.

What is a Readability Analyser? ▼
A readability analyser measures how complex your text is to read, using established linguistic formulas that calculate sentence length and vocabulary density. This tool reports Flesch-Kincaid Grade Level — the most widely used readability standard — alongside passive voice percentage and transition word density. Together these give you a diagnostic picture of where your writing is clear and direct, and where it is likely to slow a reader down. The same properties that make text easier for humans to read also tend to make it easier for AI systems to parse and extract from, which is why readability analysis has become relevant to GEO alongside its traditional use in content and UX writing.
How to use the full readability report ▼
Flesch-Kincaid Grade Level tells you the US school grade at which your text becomes readable — Grade 8–10 is the established target for general audiences. Flesch Reading Ease expresses the same measurement on an inverse scale: higher is easier. The two scores together are the industry standard pair; a piece at Grade 9 and Flesch 65 is well-calibrated for most content use cases.

Complex word percentage shows whether a high grade level is driven by vocabulary or by sentence length. A document at Grade 12 with 8% complex words is fixable by splitting sentences. One with 28% complex words needs vocabulary simplification — a fundamentally different edit.

Long sentences lists the specific sentences driving your sentence length score upward. Aggregate averages can mask a few dense outliers — fixing three sentences can move the overall grade level by two points.
Why this matters for AI & SEO ▼
Readability affects how reliably AI systems can extract and attribute content. The Flesch-Kincaid Grade Level formula — developed by Kincaid et al. in 1975 and still the readability standard across publishing, healthcare, and education — measures average sentence length and syllable density to estimate reading complexity. Content at Grade 8–10 is consistent with practitioner GEO guidance and is broadly associated with clearer structural properties for AI parsing, though this connection is an informed inference rather than a measured finding. Active voice keeps the subject of each sentence unambiguous — important when AI systems need to attribute a claim to a named entity. Transition words support human reading flow and logical structure; however, sentences that begin with transitional openers tend to score lower on AI extractability because they depend on the preceding sentence for context. These are directional findings, not hard thresholds — treat them as indicators, not rules.
Your readability report will appear here after you click Analyse.
What the research shows

Four readability rules that matter for AI extraction.

R.01

Grade 8–10 is the extraction sweet spot

Aggarwal et al. (2024) found fluency-optimised content associated with approximately +28% citation probability. This tool treats FK Grade 8–10 as a proxy for that finding — an informed inference, not a directly calibrated research threshold. Content written at grade 14+ is measurably further from that fluency target.

R.02

Passive voice above 20% is a general writing quality guideline

No peer-reviewed study has established a direct relationship between passive voice frequency and AI citation probability. High passive voice rates obscure the subject of sentences — making it harder for NLP systems to identify agents, relationships, and claims. The tool flags content above 15% with a warning and above 25% with a clear indicator, as a readability indicator.

R.03

Sentences over 25 words fragment differently for AI parsers

Long sentences pack more clauses, conjunctions, and qualifications into a single extractable unit. The tool flags every sentence over 25 words and provides a distribution chart — so you can see whether complexity is evenly spread or clustered in specific sections.

R.04

Transition word density is a writing quality heuristic

Logical connectives ("therefore", "consequently", "however", "whereas") support reading flow and structural parsability — the tool provides this as a general readability indicator, not a research-calibrated AI extraction finding. No peer-reviewed study has established a direct relationship between transition word density and AI citation probability. The tool measures transition word density and flags content with less than 15% transition coverage.

Benchmarks

Readability targets grounded in AI extraction research.

Gr. 8–10
Flesch-Kincaid grade associated with highest AI extraction probability
Aggarwal et al. 2024 · peer-reviewed
< 15%
Passive voice target for cleaner AI parsing
Evidence-informed threshold
15–30%
Healthy transition word range for logical coherence indication
Evidence-informed threshold

Check your readability now.

The tool takes any length of content and returns a grade, passive voice rate, and sentence distribution chart in under a second.

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