AI content detector
Paste a passage and get an honest read on whether it shows the patterns common to AI-written text. You get a likelihood band, the specific signals behind it, and a plain-language reason, never a fabricated percentage. AI detectors are unreliable by nature, so we treat the result as a signal, not a verdict.
How the AI detector works
When you press Detect AI, your text is sent to our server and read for the patterns that tend to separate machine-generated writing from human writing. The check looks at things like how evenly the sentences are paced, how predictable the word choices are, how often the text reaches for the same connective phrases, and whether the prose has the small irregularities that human drafts usually carry. Those observations are weighed together into a single likelihood band rather than a number.
The output is deliberately coarse. You get one of four readings: low, medium, or high likelihood of AI-like patterns, or inconclusive when there is not enough to go on. Short text and writing that looks like it came from a non-native English speaker default toward inconclusive on purpose, because those are exactly the cases where detectors are most likely to be wrong. Alongside the band you get a short list of the specific signals the check noticed, each marked as leaning AI, leaning human, or neutral, so you can see the reasoning instead of trusting a black box.
Why we refuse to show a percentage
Most AI checkers slap a confident number on your text: 92 percent AI, 14 percent human, and so on. That number is the most dishonest part of the whole category. No detector can actually measure the odds that a specific passage was written by a machine, because the underlying signal is weak and the same patterns show up in plenty of careful human writing. A percentage borrows the look of a measurement to sell a guess, and once a reader sees 92 percent they stop thinking and start accusing.
A band keeps the result honest about its own limits. High likelihood means the patterns lean machine-like and you should look closer, not that the case is closed. Low likelihood means the patterns lean human, not that AI was definitely absent. Inconclusive means we would rather say nothing than risk a false flag. This is the whole design philosophy of the tool: a useful nudge that never pretends to be proof.
When to use an AI detector, and when not to
A detector is reasonable as a first-pass sanity check. An editor skimming a large batch of submissions might run a few that read oddly flat, then read those more carefully. A writer who used AI for a draft might check whether the result still sounds generic before they revise it into their own voice. A teacher curious about a sudden change in a student’s style might use a flag as a reason to ask about the writing process. In all of those, the detector points attention; the human makes the judgment.
It is the wrong tool the moment the output is treated as evidence. Failing a student, rejecting a candidate, or making a public accusation on the strength of a detector result is not defensible, because the tool is wrong often enough that real people get hurt by false positives. There have been documented cases of students accused of cheating on work they wrote themselves, with non-native English speakers hit hardest. If a decision matters, the detector cannot make it for you.
How to read a result, step by step
- Paste at least 300 words of continuous text. Anything shorter will usually come back inconclusive, which is the honest answer for a few sentences.
- Press Detect AI and read the band first. Treat it as a direction, not a score: which way do the patterns lean, and how strongly.
- Read the listed signals. They tell you what the check actually noticed, such as unusually even sentence length or repeated stock transitions, and which way each one points.
- Read the one-line reason and the caveats. The caveats are not boilerplate; they flag when the read is shaky, such as short text or possible non-native phrasing.
- Make a human decision. If it matters, verify with the writer, check sources, or ask about the process. Never act on the band alone.
A worked example
Consider two passages on the same topic. The first reads: “The integration of renewable energy sources represents a pivotal shift in the global energy landscape. Solar and wind technologies have demonstrated significant potential to reduce carbon emissions while simultaneously fostering economic growth.” Every sentence is the same measured length, the vocabulary is uniformly formal, and the connective phrases are the ones language models reach for by default. A detector will tend to read this as higher likelihood, and the listed signals will point at the even pacing and the stock phrasing.
The second reads: “We put up the solar panels last spring, mostly because the power bill had gotten ridiculous. The wind turbine idea fell through, the site was too sheltered, but the panels alone cut the bill by about a third.” The rhythm is uneven, there are concrete specifics, and the voice carries small human irregularities. A detector will tend to read this as lower likelihood. Notice that neither read is proof: a careful human can write in the flat style of the first passage, and a model can be prompted to imitate the second. The example shows what the signals respond to, not a guarantee about authorship.
The detector against other tools
It helps to be clear about what this tool is not. A plagiarism comparison checks whether your text matches another specific text or sources on the public web, which is a question about copying, not about machine authorship. A grammar checker fixes correctness, not origin. If you have AI-drafted text and want it to read naturally in your own voice, that is a rewriting job, not a detection job, and the honest framing for that is editing for quality rather than hiding the source. The detector answers one narrow question, badly enough that it should never stand alone, which is why the rest of the toolkit exists alongside it.
Practical tips
- Paste long, continuous passages. Stitched-together fragments confuse the pattern read and skew the result.
- Run the same text more than once if you have edited it. Small changes can move the band, which is itself a reminder of how fragile the signal is.
- Weight the inconclusive result. It is a real answer that means the tool declined to guess, not a failure.
- Never paste another person’s writing to build a case against them. The false-positive rate makes that unfair, especially for non-native English writers.
- If the stakes are real, treat the detector as the start of a conversation, not the end of one.
Frequently asked questions
- How accurate are AI content detectors?
- They are not reliable enough to use as proof. Independent testing and the detector vendors themselves report frequent errors in both directions: human writing flagged as AI, and AI writing passing as human. OpenAI withdrew its own classifier in 2023 for low accuracy. Treat any detector, including this one, as a rough signal that points you toward a closer human read, never as evidence.
- Why does this tool show a band instead of a percentage?
- A percentage implies a precision that no AI detector actually has. A number like 87 percent reads as a measurement, but it is really a guess dressed up as data, and people act on it as if it were proof. We show a likelihood band (low, medium, high, or inconclusive) so the result stays honest about its own uncertainty. The band tells you which way the patterns lean without pretending to be exact.
- Will the detector flag my writing if English is not my first language?
- It might, and that is the single biggest reason to distrust any detector. Non-native English writers tend to use plainer vocabulary and more even sentence rhythm, which is exactly what these tools read as machine-like. This is a documented bias that has produced real false accusations in schools. If you wrote the text yourself, a detector flag does not change that. We default short or borderline passages to inconclusive partly to limit this harm.
- Can a teacher or employer use this as proof a student or candidate used AI?
- No, and they should not. No detector output, including this one, meets any reasonable standard of evidence. The honest use is as a prompt to have a conversation, ask about the writing process, or look for verifiable sources, not to make an accusation. We label every result a signal, not a verdict, for exactly this reason.
- How much text do I need to paste for a useful result?
- Aim for at least 300 words. Below that, there is simply not enough writing for any pattern to be meaningful, so we return an inconclusive result rather than guess. Longer, continuous passages give the most stable read. A few sentences will almost always come back inconclusive on purpose.
- Does editing or paraphrasing AI text make it undetectable?
- Often, yes, which is another reason detectors cannot be trusted as proof. Light editing, paraphrasing, or running text through a rewriter changes the surface patterns these tools rely on. We do not market any way to evade detection, and we would not, because the only honest position is that detection itself is unreliable. If you want AI text to read naturally in your own voice, that is editing for quality, not for evasion.
- Is my text stored or used to train anything?
- Your text is sent to our server only when you press Detect AI, used to produce the result, and not retained for training. The check is one request and done. If you are pasting sensitive or confidential material, the safest habit with any online tool is to remove names and identifying details first.
- What is the difference between an AI detector and a plagiarism checker?
- A plagiarism checker asks whether text matches existing sources on the public web or in a reference database. An AI detector asks whether text looks like it was generated by a language model, which is a guess about style rather than a match against sources. They answer different questions, and a clean plagiarism check says nothing about whether AI was involved, and the reverse.