Practical product guides for checking AI-generated text, images, audio, video, and code.
Two text detectors can assign different labels to the same passage because their scores, thresholds, definitions, and input conditions may differ. Here is how to compare the results without treating the larger number as a winner.
Use a simple record to separate detection signals, source evidence, decisions, and unanswered questions when reviewing potentially AI-generated content.
A practical checklist for documenting a questionable video, its source, context, detection signals, and the questions still open.
A practical sequence for reviewing an unexpected audio message, checking its context, and verifying important requests without treating sound as proof.
A practical escalation process for reviewing an AI-associated signal on short or heavily revised writing without turning it into an accusation.
Different AI detector results are not a verdict. Learn how to review a passage, its edits, source, and context before deciding what to do next.
A practical sequence for reviewing a questionable image: pause, trace its source, compare the context, and treat AI detection as one signal.
A practical way to interpret an AI detector score, review supporting signals, and decide what to check before you trust or share text.
Learn what an AI text detector evaluates, how confidence scores should be read, and when human review is required.
Open GuideReview image generation, face swap, metadata, and provenance signals with an explainable detection workflow.
Open GuideUnderstand frame-level, temporal, face swap, lip-sync, and audio-video consistency signals.
Open GuideSee how prosody, spectral, speaker consistency, and recording quality affect voice clone checks.
Open GuideInspect generated code signals and use detection as one input in secure repository review.
Open GuideCopy a working request, review the unified response schema, and create an API key for your integration.
Open Guide