You definitely don't want your texts flagged like this. We know how to fix that.
Where the AI fingerprints are
Each highlighted segment triggers a detection signal. Hover to see which one.
20-Signal Detection Matrix
Over 20 analysis parameters. Mathematical — rhythm, length, repetition. Linguistic — formulaic phrases, artificial balance typical of trained models.
Every model has a writing fingerprint
Language models don't choose words — they sample from a probability distribution frozen at training time. That distribution is the fingerprint. It's always there, whether the model knows it or not.
The model reads billions of documents. Every phrase it sees repeatedly becomes a high-probability path. "In today's rapidly evolving landscape" — seen millions of times — becomes the path of least resistance.
Human raters reward structured, balanced, polished answers. The model learns: use formal connectors, acknowledge all sides, hedge uncertain claims, avoid controversy. This preference encoding is shared across all major models.
At every token, the model picks the most statistically likely continuation. It isn't reasoning — it's sampling. The output is deterministically shaped by the distribution. The fingerprint is in the statistics.
AI text sits at probability maxima — local peaks of the model's distribution. Perturb the text slightly and it becomes less likely. Human text has no such property. This asymmetry is measurable.
Encyclopedic coverage, RLHF-polished positivity, heavy em-dash use
Rigid hierarchical structure, SEO-optimized phrasing, trailing participials
These signals appear across every RLHF-trained model without exception
The same idea. A different voice.
We change the rhythm, sentence structure and word choice — so the text reads as written by a human. Your idea stays intact, everything else finds a living voice.
The implementation of advanced machine learning algorithms has significantly transformed the landscape of modern data analytics, enabling organisations to derive actionable insights from previously unstructured datasets.
Three years ago I watched a junior analyst spend a week manually tagging survey responses. Last month, the same task took forty minutes — and the results were sharper. That's the shift we're living through.
8 signals that separate a writer from a machine
These are what the LLM layer evaluates. Each characteristic scores 0–100 based on presence in the text. Low scores are the target.
Humans shift between formal and casual mid-sentence. AI never does.
Real writers choose sides. AI considers all perspectives equally.
Feeling bleeds into language. Frustration, excitement — all leave marks.
Human paragraphs wander, backtrack, and contradict themselves.
We assume shared context. Humans write for a specific reader, not everyone.
References, idioms, and local knowledge anchor text to a real person.
Humans leap. Conclusions precede evidence. Ideas interrupt each other.
Style is a fingerprint. Word choices and rhythms that repeat across everything you write.
What the score actually measures
AI detection at every service — ours included — is a probability estimate, not a verdict. Here's what that means in practice.
Text doesn't exist in two categories — AI or Human. Every piece sits somewhere on a continuum. Formal academic writing, technical documentation, and text by non-native speakers naturally share many statistical patterns with AI output.
A high score means: this text exhibits a high density of patterns statistically associated with AI generation. It does not mean the text was generated by AI. Think of it like a medical screening — a signal for closer attention, not a diagnosis.
ESL writers, technical authors, legal and academic writers, and anyone trained to write in a formal structured style will score higher than a casual native blogger — regardless of whether they used AI at all.
If AI output has been substantially rewritten by a human — or if a human has been specifically instructed to write "less formally" — the statistical signal weakens. Our score reflects what is detectable, not what is true.
A high score is a signal for review — not proof of AI authorship. A low score does not guarantee human authorship. Use the result as one input among several, not as a final verdict. Our goal is to help you understand your text's statistical profile and improve it — not to judge authorship.
Detect the fingerprint. Remove it.
Two tools that work together — one to show you exactly where AI patterns live in your text, one to replace them with something that reads like a person actually wrote it.
Detect
We analyse your text across 20 signals in two layers — 12 computational metrics and 8 LLM-evaluated characteristics. The result is a precise 0–100 score with a full breakdown: which phrases triggered which signals, and why.
- 20 signals · 2 analysis layers
- Sentence-level annotation
- Weighted hybrid scoring
- 15 tokens
Humanise
Our AI agent rewrites the flagged segments — not by swapping synonyms, but by reconstructing sentence rhythm, varying structure, and replacing model-specific vocabulary with authentic patterns. The meaning stays. The fingerprint doesn't.
- Structural rewrite, not synonym swap
- Meaning and intent preserved
- Measurable before/after score
- 30 tokens
"By the time you reach the bottom of this page, your text could already be different."
Transform your text
Paste it. We detect across 20 signals. You choose depth. Our AI agent humanises. 60 seconds.
Begin the transformation →