Built for thoughtful, responsible writing

An AI Detector that calls a probability a probability

Review writing patterns with sentence-level context and honest limitations. A score can start a closer look; it cannot prove who wrote a text.

No certainty theaterShort-text warningsContext before conclusions
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Validated against*Originality.aiScribbrGPTZero

*we regularly validate random samples of results against these detectors

Why it helps

Read the result, not just the number

Sentence-level highlights

A single percentage tells you nothing about where to look. The report marks the specific sentences that triggered the signal, so you can judge each one on its own instead of arguing with a score.

Rewrite without leaving the page

Flagged passages can be reworked in the same workspace and re-checked immediately, rather than copied out to a second tool and pasted back with the formatting broken.

Built to be doubted

The report says what the signal is and what it is not. A high score is a prompt to look closer, never a finding of misconduct, and the wording never pretends otherwise.

What happens inside

What the detector actually measures

There is no watermark in ordinary model output. Detection works from the finished words alone, which is both why it is useful and why it can be wrong.

01

Predictability

Generated prose tends to pick the expected next word more often than a person does. The check measures how unsurprising the word choices are across a passage rather than in isolation.

02

Rhythm and uniformity

People mix long sentences with short ones and vary how paragraphs open. Model output holds a steadier beat, and that evenness is measurable across a document.

03

Structural repetition

Recurring transitions, symmetrical paragraph shapes, and a consistently neutral register are patterns that often survive a paraphrasing pass, so they carry signal even after editing.

Made for real study work

Who this is for

Students

Check an assignment before submitting to see whether formal phrasing reads as generated, and tighten those passages in your own words. Follow your institution's rules on disclosing AI assistance.

Educators

See which sentences carry the signal rather than a bare percentage. Treat the report as the start of a conversation with a student, not as evidence on its own.

Writers and editors

Screen a draft before it is published, especially work assembled from several contributors, and rewrite the sections that read as machine-assembled.

Writers working in a second language

Careful, grammatically even prose is exactly what raises a score. Knowing which passages read that way lets you vary them deliberately instead of guessing.

Three simple steps

Check a document in three steps

  1. 1

    Add the text

    Paste the passage or upload the file. Formatting does not need cleaning up first, because the check reads sentence structure rather than markup.

  2. 2

    Run the check

    The scan returns an overall likelihood alongside the individual sentences that contributed to it, so the number always comes with its evidence.

  3. 3

    Read the report, then decide

    Look at what was flagged and why. Rewrite what genuinely reads as generated, and leave alone what is simply formal or concise writing.

Side by side

More than a percentage

Most detectors hand back one number. Here is what changes when the report has to justify itself.

  • What you get back

    TextPatrol AI Detector

    An overall likelihood plus the sentences behind it, so the score can be checked rather than taken on trust.

    A basic tool

    A single percentage with no indication of which passage produced it.

  • Acting on the result

    TextPatrol AI Detector

    Flagged passages can be rewritten and re-checked in the same place, keeping your formatting intact.

    A basic tool

    You copy the text into another tool and paste it back, losing structure on both trips.

  • Formal and non-native writing

    TextPatrol AI Detector

    The report distinguishes an even, careful register from generated patterning, and says so where the call is close.

    A basic tool

    Precise, plainly written prose is scored as suspicious with nothing to explain why.

  • How certainty is expressed

    TextPatrol AI Detector

    Confidence is stated in words as well as numbers, including when the answer is that the text is genuinely inconclusive.

    A basic tool

    A decisive-looking figure implies a precision the underlying method does not support.

  • What the tool claims about itself

    TextPatrol AI Detector

    Stated plainly: this estimates likelihood, it errs in both directions, and a score is not proof of authorship.

    A basic tool

    Headline accuracy figures are advertised without the conditions under which they were measured.

  • Use against a person

    TextPatrol AI Detector

    The wording is explicit that a result is a prompt to look closer, never a finding of misconduct on its own.

    A basic tool

    Output is presented as a verdict, which invites decisions no detector can support.

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The complete guide

Everything worth knowing about AI detection

What these tools measure, where they are useful, and the questions they cannot answer.

What detection can establish

A detector compares writing against patterns typical of generated text: how predictable the word choices are, how uniform the sentence rhythm is, and how repetitive the structure feels. From that it produces a likelihood.

That likelihood is genuinely informative in aggregate. Across a long document, consistent patterning is meaningful in a way a single suspicious sentence never is.

What it cannot establish

No detector can see who typed a document. It reads the finished words, and identical words score identically whoever produced them.

This is why a score cannot function as proof. It narrows attention; it does not settle the question, and treating it as though it does is how people get wrongly accused.

Why results move between tools

Different detectors are trained on different material and weigh signals differently, so the same passage can score high in one and low in another. Neither result is lying; they are answering slightly different questions.

Editing shifts scores too. A paraphrasing pass often lowers a score without changing who wrote the underlying ideas, which is precisely why a lower score proves nothing on its own.

Using results fairly

Read the flagged sentences rather than the headline number. If the passage is formal, technical, or written by someone working in a second language, the signal may reflect style rather than origin.

Where a result informs a decision about a person, treat it as one input among several, alongside drafts, revision history, and a conversation. That is the standard the number can actually support.

Questions, answered

Frequently asked questions

Using a detector responsibly

This matters more here than on any other page: a detection result can affect somebody's grade or job. The limits below are not fine print, they are how the tool should be read.

A score is a signal, not a verdict

Detection estimates how closely writing matches generated patterns. It cannot establish who typed a document, and it should never be the sole basis for an accusation.

It errs in both directions

Careful, formal, plainly structured human writing can score high, and edited machine output can score low. Both mistakes are real and neither is rare enough to ignore.

Non-native writing needs extra care

Writers working in a second language often produce even, deliberately simple prose. That is a style, not evidence, and treating it as suspicious is the failure mode to guard against.

Use the score to ask better questions

Analyze a meaningful passage, read the limitations, and keep human context at the center of every conclusion.

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