ZeroGPT analyzes text and estimates the likelihood it was generated by AI models, providing a probability score and contextual cues. Paste content, upload files, or call the API to evaluate drafts at scale. Explanations help readers understand which patterns influenced the result, and batch tools report on many documents at once. Used with human judgment and policy, it supports classrooms, publishers, and teams that need signals without blocking legitimate writing or nuanced style. Confidence hints highlight uncertainty and advise human review where needed.
Measure a probability that text was likely produced by AI, accompanied by thresholds that categorize risk. Scores consider token patterns and other indicators. While not definitive, these signals help triage reviews and focus attention. Organizations combine results with policy and context to avoid false positives while still catching obvious synthetic content in submissions. Batch reports summarize outcomes for classes, clients, and editorial teams now.
View highlights and notes that indicate which spans contributed to the score. Explanations promote transparency and guide follow up conversations with authors. By seeing the patterns detected, reviewers avoid opaque decisions and can weigh context, quotes, or referenced material carefully before acting, especially in academic and editorial environments sensitive to fairness. APIs return scores, tokens, and flags for audit logs and workflows downstream.
Upload folders or spreadsheets and receive consolidated summaries by class, team, or campaign. Reports track volume, average scores, and flags that need attention. CSV and JSON exports support audits. With batch runs, reviewers manage surges during deadlines and monitor trends without opening every document manually, reserving deep reading for the edge cases. Model updates align with new writing tools so drift is tracked over releases.
Use API endpoints to submit text and retrieve scores with metadata. Webhooks notify systems on completion. Logs store request identifiers for audits. Integrations connect LMS, CMS, and help desks so screening happens where work already lives. Consistent interfaces reduce custom glue code and let developers adapt reviews alongside updates to content pipelines. File types include text, docs, and pasted content for flexible intake today.
Manage keys, rate limits, and access. Track model updates and release notes that describe changes in behavior. Documentation recommends conservative thresholds and appeals processes. This framework helps organizations apply detection responsibly, combining automation with human review to avoid overreaching while still discouraging misuse in high stakes contexts. Explanations describe features that contributed to the score for transparency.
Educators, editors, compliance teams, community managers, and researchers who need scalable signals about likely AI generated text; groups building intake flows in LMS or CMS; and organizations that want transparent highlights, batch oversight, and APIs while pairing results with policy, rubrics, and manual checks to protect fairness for legitimate writing and quoted sources. Rate limits and keys protect shared usage across departments and programs.
Manual spot checks miss scale, and opaque detectors undermine trust. ZeroGPT provides probabilistic scoring, explanations, batch reports, and APIs so teams can screen content consistently, prioritize human review, and document outcomes. Used with policy, it reduces guesswork and supports fair conversations about originality without blocking authentic voices or rigid styles. Export options include CSV and JSON with timestamps to support compliance.
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