AI detector

GPTCleanup Pro's AI detector reads a block of text and reports the patterns common across AI writing tools, ChatGPT, Claude, Gemini, Grok, DeepSeek and the rest, with each signal named and explained rather than compressed into one score that hides its own reasoning. It's built for the question people type into search: is this AI generated text. The answer here is a breakdown, not a verdict.

It's a server tool by necessity: the pattern model is too large to run in a browser, so text is sent to our AI server to be scored, and the result returns to the page. You get 5 free checks in any rolling 24 hours, no account required.

The output is deliberately a list rather than a score. An AI detector that returns a bare percentage asks for unearned trust; this one names each pattern (a formulaic opener, a dense run of even sentence lengths, a cluster of stock transitions) so the reasoning is available for inspection rather than taken on faith.

One round trip to the server, then a reasoned result. Nothing to configure beforehand, no particular format required: plain text in, a breakdown out.

Step 1

Paste the sample. A longer passage gives the pattern model more structure to weigh; a single sentence rarely produces a confident read.

Step 2

Run the check. The text is sent once to our AI server, scored against the patterns below, and nothing further is done with it.

Step 3

Review each flagged pattern and its reasoning. The output is an argument you can inspect, not a number you're asked to trust.

Training on overlapping material leaves a statistical fingerprint shared across model families, distinctive enough to describe precisely. No single item below is disqualifying alone; the detector's read is a function of how many co-occur across one passage, and how consistently they recur.

Even sentence rhythm

Sentence length clustering tightly around one value, where human prose tends to alternate short and long.

Formulaic openers

A first line generic enough to fit almost any topic, the canonical example being some variant of “In today's fast-paced world.”

Stock transitions

Moreover, furthermore, additionally: grammatically correct connectors rarely reached for in ordinary prose.

Weightless hedges

“It's important to note that,” language signalling information is arriving rather than delivering any.

Low surprise, word to word

A prose stream where each word sits close to the expected next word, reading smooth but faintly generic.

An unrequested closing summary

A final paragraph that restates the piece rather than closing it.

Not with confidence, and stating that plainly is more useful than avoiding the question. The major model families train on overlapping corpora and continue to converge stylistically, so the differences between them are matters of degree, not a distinguishing fingerprint.

A system prompt further erodes the signal: instruct any model to write short, unhedged, plain sentences, and most of its default stylistic profile disappears in one step. What this detector reports is a match to patterns typical of AI writing broadly, not a determination of which model, or which person, produced the text under review. Treat a strong match as grounds for a closer look, not a finding.

This is precisely why this site also runs a checker per model family rather than relying on one general score. A pattern set built around a single model's own habits catches distinctions this general detector, tuned for breadth, is designed to miss.

Several categories of legitimate human writing share these patterns for reasons unrelated to AI, and a serious detector says so rather than burying it.

Second-language writers

Formal connectors and explicit hedging are standard ESL instruction, the exact material that produces flags here.

Careful academic prose

Deliberate hedging is good scholarly practice; overstating an unsupported claim is the actual error it avoids.

Rigid house styles

A style guide banning contractions and favouring even, measured sentences reads like a model's, for institutional reasons.

Translated prose

Both machine and human translation flatten idiom and lean on formal connectors, producing a surface resemblance to the patterns above.

Technical and procedural writing

Documentation is written in short, even, declarative sentences by design, reading statistically similar to model output.

A flag from this tool, or from any detector, is a signal rather than a finding, and should be treated that way by anyone using it to make a decision about your work.

Preserve your drafts

Outlines, early versions and marked-up revisions are the clearest evidence a piece was composed, not generated whole.

Retain version history

Docs, Word and most editors log revisions automatically. Leave it intact while a question is open.

Be prepared to discuss specific choices

Explaining why a line or structure exists is harder to fabricate than the prose itself.

Ask for the named signal, not the label

A specific pattern (sentence rhythm, a stock-phrase cluster) can be examined on its merits. An unexplained score cannot.

The same checker, applied at different points in a review.

Editorial and publishing teams

A first pass on submissions, to route unedited AI drafts before committing editorial time.

Instructors

One input among several when a submission reads unusually polished, never the sole basis for a decision.

Hiring reviewers

Checking whether a writing sample matches the voice demonstrated live in an interview.

Writers reviewing their own drafts

Running a piece through before submission, to see whether their own habits read as machine-typical.

Moderators

A first read on a batch of posts sounding oddly alike, ahead of a closer manual review.

This detector is server-side by design: scoring against AI writing patterns requires a model too large to ship into a page, so each check is a single round trip, your text out, a result back.

That trip is metered rather than unlimited: 5 free checks in any rolling 24-hour window, tracked without an account. There's no paid tier behind the limit; it reopens as the window rolls forward.

Five checks covers ordinary use of a general AI detector: one submission, one email, one paragraph under review. For a longer batch, spacing checks across the day works better than exhausting the allowance at once.