Frequently Asked Questions

AI detection analyzes text using statistical methods like perplexity analysis (measuring how predictable the text is), burstiness analysis (measuring sentence-level variation), and vocabulary/structure heuristics. Human writing tends to be more varied and unpredictable, while AI text is more uniform and statistically optimal.

Yes. EyeSift core checks are free with no signup required. The public text analyzer has no visible hard word cap in the browser UI, and the site is supported by contextual advertising.

EyeSift has not published an independent accuracy benchmark; treat its score as a heuristic triage signal, not proof. No AI detector is 100% accurate. Accuracy of any detector varies with content type, AI model used, text length, and whether the text has been paraphrased or edited. We state these limits plainly because honesty builds trust.

EyeSift can triage text that may come from ChatGPT, Claude, Gemini, DeepSeek, Grok, Copilot, and other major AI writing tools. Treat the result as a probability signal because model versions, prompts, editing, translation, and sample length all affect reliability.

False positives (flagging human text as AI) happen with every detector, and published studies show they are more common for some text types. Non-native English writing, heavily edited text, and formulaic content are more likely to trigger false positives. EyeSift provides confidence scores and sentence-level highlighting to help users assess results contextually.

Detection accuracy decreases significantly for texts shorter than about 150 words. Short samples do not contain enough statistical signal for reliable analysis. We display warnings for short inputs and recommend analyzing at least 250+ words for best results.

No. EyeSift analysis runs in your browser; your text is not uploaded to our servers, stored, logged, or used for training. Close the tab and it is gone.

Yes, AI detection is not foolproof. Paraphrasing tools, adversarial prompting, and human editing can reduce detection rates. EyeSift combines several statistical signals, which helps, but determined editing can still evade any detector.

EyeSift is a multi-modal platform that can analyze text, images, video, and audio for AI-generated content. Our text analyzer is our most mature tool, while image, video, and audio analysis rely on file metadata, provenance markers (such as C2PA), and basic signal measurements.

AI detection results are generally not sufficient as sole evidence in legal proceedings. They are informational tools best used alongside human judgment, contextual assessment, and other verification methods. Some courts have accepted AI detection as supporting evidence, but legal standards vary by jurisdiction.

Both tools detect AI-generated text, but they fit different workflows. EyeSift is a no-signup browser-first triage tool with text, image, video, and audio surfaces. GPTZero is stronger for education workflows and sentence-level review; its official Chrome-extension guide describes 10,000 free monthly credits or words, with paid plans for higher-volume use.

To some extent, yes. Different AI models can produce text with somewhat different statistical patterns, but EyeSift does not identify which model wrote a text, and as models converge in quality, distinguishing between them becomes harder for any tool.

Each AI detector uses different algorithms, training data, and confidence thresholds. A text that scores 80% AI on one tool may score 40% on another. This is why we recommend using AI detection as one data point, not the sole basis for decisions.

EyeSift is primarily optimized for English text. Detection accuracy for other languages is lower and varies by language. Languages with large AI training datasets (Spanish, French, German, Chinese) tend to have better detection than less common languages.

Perplexity measures how predictable text is to a language model. Low perplexity means the text follows highly predictable patterns (common in AI text), while high perplexity means unexpected word choices (common in human writing). EyeSift uses a lightweight in-browser proxy for perplexity (word-level predictability), not a full language model.

Burstiness measures the variation in sentence complexity within a document. Human writers naturally alternate between short and long sentences with varying complexity. AI tends to produce more uniform sentence structures. Low burstiness is a signal of AI generation.

Teachers can use EyeSift as a triage signal, but not as an automated judge. Pair the score with drafts, revision history, assignment rules, citations, source checks, and a student explanation before taking any academic-integrity action.

Paraphrasing can substantially reduce detection rates for any detector. However, heavily paraphrased AI text often retains some statistical signatures that detectors can identify. The more extensive the paraphrasing, the harder detection becomes, but the text also becomes more "human" in the process.

We recommend at least 250 words for reliable results. Texts of 150-250 words produce less certain results, and texts under 150 words may not contain enough statistical signal. Our tool displays warnings for short inputs and adjusts confidence levels accordingly.

Yes, AI-generated images from tools like DALL-E, Midjourney, and Stable Diffusion can leave detectable traces such as generator tags or missing camera EXIF metadata, C2PA content credentials, unusual noise or compression patterns, and semantic anomalies like impossible shadows or anatomical errors. EyeSift checks metadata, provenance markers, and basic image statistics; it does not run a trained image classifier.

Deepfakes are AI-generated or AI-manipulated videos and audio that depict people saying or doing things they never did. EyeSift checks video files for provenance markers (C2PA), metadata, and encoding characteristics that can hint at manipulation; it does not perform frame-by-frame facial analysis, so treat its result as a starting point, not a verdict.

AI detection is ethical when used responsibly. Key principles include: using detection as one input in decision-making (never as an automated judge), being transparent about its limitations, avoiding punitive decisions based solely on detector output, and considering the impact on vulnerable groups like ESL students.

EyeSift uses rule-based heuristics rather than a trained model, so there is nothing to retrain. We revise the rules and the site content periodically as new AI models and detection research appear.

EyeSift currently operates as a web-based tool. We are exploring API access for enterprise users. Contact sales@eyesift.com for enterprise partnership inquiries.

AI detection for code is less reliable than for natural language text. Code has inherently more structured and formulaic patterns, making it harder to distinguish between human and AI-written code. Our tool is optimized for natural language content.

Nothing leaves your browser. Analysis runs locally on your device; we do not receive, store, or log analyzed text, files, or results. Our privacy policy details our data handling practices.