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 neural classification. 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 achieves 75-85% accuracy on standard benchmarks. No AI detector is 100% accurate. Accuracy varies based on content type, AI model used, text length, and whether the text has been paraphrased or edited. We are transparent about our accuracy because we believe 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) occur at a rate of approximately 6-15% depending on the text type. 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 processes content in real-time for analysis only. Your text is never stored, logged, or used for training purposes. Content is processed and immediately discarded after generating results.

Yes, AI detection is not foolproof. Paraphrasing tools, adversarial prompting, and human editing can reduce detection rates. However, our ensemble approach combining multiple detection methods makes evasion significantly harder than defeating any single method.

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 use metadata, spectral, and pattern analysis techniques.

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 produce text with distinct statistical fingerprints. For example, ChatGPT output tends to differ from Claude output in sentence structure and vocabulary patterns. However, as models converge in quality, distinguishing between specific models becomes harder.

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 measures perplexity at token, sentence, and paragraph levels.

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 reduce detection rates by 15-25 percentage points. 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 leave detectable artifacts including GAN fingerprints in the frequency domain, missing or synthetic EXIF metadata, inconsistent noise patterns, and semantic anomalies like impossible shadows or anatomical errors.

Deepfakes are AI-generated or AI-manipulated videos and audio that depict people saying or doing things they never did. EyeSift can analyze videos for temporal inconsistencies, facial landmark anomalies, audio-visual sync issues, and compression artifacts that indicate manipulation.

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.

Our detection models are re-evaluated monthly against the latest AI-generated content. We update algorithms when accuracy degrades, and we retrain on samples from new model versions. Our accuracy figures are updated to reflect current performance, not historical results.

EyeSift currently operates as a web-based tool. We are exploring API access for enterprise users. Contact [email protected] 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. Your submitted content is processed in real-time and immediately discarded. We do not store analyzed text, analysis results, or any derived data. Our privacy policy details our data handling practices.