Kazan SEO AI Detector
Professional-grade AI content detection and semantic SEO analysis at zero cost.
High-fidelity linguistic entropy analysis for detecting synthetic content across GPT-4, Claude, and Gemini models.
AI Detector by SEOToolPort is a specialized NLP utility designed for the 2026 digital ecosystem, where the distinction between human and synthetic text is increasingly blurred. The tool leverages a multi-layer detection architecture that evaluates 'Perplexity' and 'Burstiness'—the two core metrics of human linguistic variance. While many competitors rely on simple keyword matching, SEOToolPort utilizes a fine-tuned RoBERTa-based model capable of identifying structural patterns characteristic of Large Language Models (LLMs) including GPT-4o, Claude 3.5 Sonnet, and Gemini Pro. As of 2026, it serves as a critical first-line defense for SEO professionals and editors who must navigate search engine policies regarding AI-generated spam. The platform operates on a zero-trust content model, analyzing the probability of word sequences to provide a confidence score. Its lightweight, web-based deployment makes it an essential tool for rapid verification without the overhead of enterprise-level suites, focusing on accessibility and speed to support the high-velocity content cycles of modern digital marketing.
Calculates the complexity of the text; lower perplexity scores indicate higher predictability, a hallmark of AI generation.
Professional-grade AI content detection and semantic SEO analysis at zero cost.
Forensic-level AI content detection and advanced humanization for SEO-proof content.
Transform AI-generated text into undetectable, human-grade content with advanced linguistic humanization.
A non-profit open-source detector for educational integrity and transparent AI verification.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Measures the variation in sentence length and structure throughout the provided text sample.
Utilizes a deep learning model trained on vast datasets of both human and AI-authored content.
Processed text is analyzed in-memory and not stored in persistent databases.
Algorithmic weights are adjusted to detect signatures from OpenAI, Anthropic, and Google models.
Visual overlay that color-codes text segments based on AI-influence probability.
Optimized JavaScript execution for fast client-side rendering of analysis results.
Educators needing to verify the authenticity of student-submitted essays in an era of ubiquitous AI assistance.
Registry Updated:2/7/2026
Ensuring guest posts and outsourced content do not trigger search engine 'low-value' AI content flags.
Removing synthetic data from datasets intended for training new machine learning models to prevent 'model collapse'.