Detect research-integrity risks before they become publication problems.
AntiAI helps editors, reviewers, and institutions screen scientific manuscripts for AI-generated text, manipulated figures, and statistical fabrication signals — producing structured evidence for expert review, not black-box accusations.
Designed for triage, documentation, and transparent human decision-making.
From manuscript text to figures and statistical traces.
AI-text screening
Identify linguistic patterns, generic framing, weak methodological anchoring, citation drift, and AI-like academic phrasing across 32 indicators.
Image & figure integrity
Flag visual anomalies, duplicated regions, suspicious figure construction, and AI-assisted image fabrication indicators across 47 checks.
Statistical fabrication signals
Screen distributions, p-values, effect sizes, digit patterns, internal inconsistencies, and improbable reporting structures across 65 indicators.
A defensible review trail, not a yes/no verdict.
AntiAI turns manuscript screening into a documented process: upload, analyze, inspect flagged evidence, export a report, and route the case to the appropriate reviewer or integrity officer.
Reports built for editorial decisions.
Each report separates indicators from conclusions, highlights passages and figures requiring inspection, and records the reasoning path used in triage.
Add research-integrity screening to your editorial workflow.
Start with a sample manuscript, compare the report structure, and define the review thresholds appropriate for your journal or institution.