Because detectors don’t care about your topic, they care about patterns. If your writing shows low perplexity, flat burstiness or stylometric signals that match LLMs like ChatGPT or Gemini, it’ll get flagged, even if the information is accurate.

Not anymore. Basic paraphrasers just swap words. Detectors now analyze structure, rhythm and tone. If your sentences still follow AI-like pacing and syntax, you’ll still fail the check.

Platforms like Netus merge detection and humanization into a closed loop. This eliminates context-switching hell, letting you scan, rewrite and rescan iteratively until authenticity sticks.

Yes. Shifting from robotic or formal tone to something more conversational, varied or emotionally resonant can reduce AI detection scores. Detectors often flag content that sounds flat, overly structured or generic.

Very important. Human writers naturally mix short and long sentences. AI tends to write in uniform blocks. Adding burstiness (sentence length variation) is one of the quickest ways to humanize your content.

Tools like Grammarly enforce robotic uniformity: flattened tone, predictable syntax and error-free monotony, all triggers for false positives.

No. AI detectors don’t check facts. They only analyze writing style, structure and statistical patterns that match known LLM outputs.

Ideally, after every significant rewrite. The safest workflow is:
Write → Detect → Rewrite → Retest → Repeat until clean.

When non-native writers use rigid structures or excessive formality, they inadvertently mimic AI “tells.” Tools specializing in organic rhythm and tonal variance become critical for bridging this gap.

Start with your raw draft → run it through a detector → use a humanization tool to rewrite → rescan → publish or submit only when you hit a “Human” score.