Benchmark-minded detection
Designed for long-form essays, research papers, resumes, and editorial drafts with clear probability bands instead of vague pass or fail labels.
Check human-written, AI-generated, and mixed text from ChatGPT, GPT-5, GPT-4o, Gemini 2.5, Claude, DeepSeek, Llama, Grok, Copilot, and more.
Why choose this AI detector?
Get a reliable AI checker for essays, research papers, resumes, and web content with explainable results, fewer false positives, and SEO-ready answer coverage.
Detection intelligence
The workflow combines model likelihood, sentence rhythm, repeated phrasing, file-upload review, and careful probability labels so teachers and students can evaluate AI writing responsibly.
Designed for long-form essays, research papers, resumes, and editorial drafts with clear probability bands instead of vague pass or fail labels.
Identify likely AI-generated, human-written, and mixed passages from ChatGPT, GPT-5, GPT-4o, Gemini 2.5, Claude, DeepSeek, Llama, Grok, Copilot, Qwen, and newer model families.
The detection workflow is structured around retraining, fresh examples, and evolving sentence patterns so new AI systems can be evaluated responsibly.
Sentence evidence, token-level likelihood, repeated phrasing, and rhythm notes help writers understand what needs review before rewriting.
Pair AI detection with plagiarism review, paraphrase checks, readability scoring, grammar review, and fact-checking for a more complete integrity workflow.
Short, formulaic, translated, or highly edited writing can be difficult to classify, so results are presented as signals that support human judgment.
Languages
Review essays, assignments, professional documents, and web copy across major languages while keeping model coverage, sentence evidence, and privacy expectations clear.
Global writing review
Students, teachers, publishers, and multilingual teams can see supported languages, model coverage, and report outputs before they run a scan.
How it works
The detector combines language modeling, deep learning, and explainable review logic to identify generative content with practical precision.
Trained against large collections of human-written academic, professional, and web documents.
Reviews statistical patterns left by large language models, including phrasing, burstiness, token likelihood, and sentence structure.
Separates AI-generated passages from human input even when the document contains mixed authorship or AI-assisted revisions.
Explainable reports
Give writers and reviewers a readable answer: what was flagged, why it may look AI-generated, and which next step is appropriate.
Whole-document AI score with human, AI-generated, and mixed-text interpretation.
Sentence-by-sentence visualization showing which passages most influenced the result.
PDF and DOCX upload flow that extracts text first, then runs the same text-based AI detector.
Paraphrase, plagiarism, readability, grammar, and fact-check signals for deeper review.
Use cases
Different reviewers need different evidence. This homepage covers educational, editorial, recruiting, publishing, SEO, and enterprise AI checker intent.
Review AI writing risk, citation integrity, and rewrite suggestions before turning in assignments or research papers.
Use AI detection as one discussion signal alongside drafts, writing history, oral review, citations, and classroom context.
Screen articles, blog posts, newsletters, and website copy for generic AI patterns before publication.
Paste application text or connect a resume detection API to high-volume screening workflows.
Identify repetitive, low-insight, machine-generated submissions across reviews, comments, and contributed articles.
Use API, LMS, and batch workflows when teams need high-volume checks, policy reporting, or custom retention controls.
Professional review workflow
AI detection works best when reviewers can compare the score with sentence evidence, document context, policy rules, and the writer's process.

Use the AI score as a review signal, then compare sentence evidence with the document purpose, citation quality, draft history, and your institution's academic integrity rules.
AI Detection FAQs
Comprehensive answers for AI checker accuracy, model coverage, PDF and DOCX files, resume detection, API use, LMS workflows, false positives, privacy, and responsible AI use.
An AI detector uses machine-learning systems related to the architectures behind generative AI. It does not generate words. It estimates whether each word, token, sentence, and full document is more likely to be AI-generated or human-written, then visualizes the result.
It is designed for text from ChatGPT, GPT-5, GPT-4o, Gemini 2.5, Claude 4.5, DeepSeek-V3, Qwen3, Llama, Grok, Copilot, and other current or emerging AI writing systems.
Yes. Mixed text is one of the main use cases. The detector looks for sections where human-created text may have been amended, expanded, paraphrased, or rewritten by AI.
A production version can support PDF AI detection and DOCX AI detection by extracting text from the file first, then running the same text-based analysis used by the editor.
Yes. You can paste resume or cover letter text into the editor. For high-volume recruiting, an AI resume detector API can connect to an applicant tracking system.
The page is structured for API, LMS, browser-selection, and enterprise workflows. Teams can use the same detection logic inside classroom systems, editorial tools, or screening pipelines.
The free checker can support quick scans with practical text limits. Paid plans can unlock longer character limits, saved reports, batch checks, file uploads, and team controls.
Very short text provides too little signal and can create false positives. Very long text needs token limits, processing controls, and fair-use boundaries, so large documents may be split or processed through an API.
Accuracy varies by text length, subject, language, editing level, and model family. Long-form documents usually provide stronger signals, while short or generic text can be harder to classify.
The best AI detector should combine model coverage, low false positives, explainable sentence evidence, transparent limits, privacy controls, and regular evaluation against fresh AI outputs.
Basic spelling, grammar, and clarity edits should not automatically mean a document is AI-generated. Heavy rewriting, generic phrasing, and uniform sentence patterns may increase the score.
A trustworthy AI checker should explain retention, encryption, access controls, training use, deletion options, and enterprise data-processing terms before teams upload sensitive writing.
AI detection can support review, but it should not be treated as automatic proof. Teachers should combine detection with draft history, citations, student discussion, and assignment context.
AI detection and plagiarism detection answer different questions. A full integrity workflow should also review source overlap, paraphrased passages, citation quality, readability, and factual claims.
AI spam is low-quality, repetitive, generic, or mass-produced machine-generated content. It often appears in reviews, social posts, comments, articles, and other user-generated content surfaces.
Yes. Some tools claim that text will pass a detector without actually checking it. Treat those claims cautiously and verify the final document with a transparent AI checker.
Search quality systems focus on usefulness, originality, expertise, and trust. AI detection can help editors find generic drafts, but strong content still needs accurate information and human value.
That depends on the policy. Schools, publishers, and workplaces should define acceptable AI assistance, citation rules, disclosure requirements, and review steps before judging a document.