How It Works

01 / Structure

Rhythm, complexity and punctuation.

Some signals describe how a passage changes pace, how regular its local complexity is, and how punctuation is distributed across the text.

02 / Vocabulary

Range, growth and distribution.

LexyAI examines how vocabulary is used through a passage: how varied it is, how new words appear, and how common words are distributed.

03 / Repetition

Repeated phrasing and predictability.

Short repeated constructions and local regularity can add information about the statistical texture of a passage. No single phrase is treated as an authorship marker.

04 / Composite

Many signals, one readable result.

The individual measurements are combined into a bounded AI-pattern score, with a separate confidence indicator describing how strongly the available signals agree.

That distinction matters. The headline score is a compact summary of several measurements, while the signal breakdown gives additional context about where the result is coming from. LexyAI is designed to make the output readable enough for a normal reviewer without pretending that statistical language analysis can reveal authorship with certainty.

05 / Reading the Result

Use the score as a signal, not a verdict.

AI text detection is inherently probabilistic and context-dependent. A passage can contain a mixture of writing characteristics associated with both human and AI-assisted text, especially after editing, paraphrasing or substantial rewriting. Formal business writing, educational material, technical documentation and concise informational prose can also be unusually regular without having been generated by a model.

For that reason, LexyAI separates the overall estimate from the underlying measurements. Reviewing the individual signals can help explain why a passage received a certain result and can encourage a closer look at the document itself rather than an unsupported conclusion about its author.

06 / Practical Use

Built for writers, editors and reviewers.

A browser-based AI writing detector can be useful in several ordinary workflows: reviewing a draft before publication, checking machine-assisted copy for unusually uniform phrasing, comparing sections of a longer document, or simply exploring the measurable characteristics of a passage. The tool can also be useful for researchers and students who want to understand how lexical diversity, sentence rhythm and repetition change across different kinds of writing.

Longer passages generally provide more context for these measurements because there are more sentences and more vocabulary observations to compare. Very short text is inherently less stable: a handful of sentences can make a statistical profile look stronger or weaker than it would over a larger sample.

07 / Limits

What the score cannot prove.

• It cannot identify a specific author or model from text alone.
• Human writing can produce AI-associated statistical patterns.
• Editing, paraphrasing, genre and text length can change the result.
• A detector score should not be used as sole evidence in high-stakes decisions.