Kazan SEO AI Detector
Professional-grade AI content detection and semantic SEO analysis at zero cost.
Forensic-grade linguistic fingerprinting and LLM source attribution for 2026 content standards.
AI Detector by TextOracle represents the 2026 frontier of forensic linguistics, utilizing a multi-layered transformer architecture to identify syntactical patterns unique to Large Language Models. Unlike first-generation detectors that rely solely on perplexity and burstiness, TextOracle employs 'Model Fingerprinting'—a technique that maps text against the specific probability distributions of models like GPT-4o, Claude 3.5 Sonnet, and Gemini 1.5 Pro. Its engine is designed to discern the subtle 'AI-residue' left by iterative prompting and human-AI hybridization. Positioned as a mission-critical tool for SEO agencies and academic institutions, TextOracle focuses on high-precision outcomes to minimize false positives, which have historically plagued the AI detection market. By 2026, the tool has integrated deep-fake text analysis, capable of identifying synthetic styles even after heavy paraphrasing or 'humanizing' attempts. The platform operates on a low-latency API infrastructure, making it suitable for bulk processing in enterprise CMS environments and automated editorial workflows.
Identifies the specific LLM architecture (e.g., GPT-4 vs. Claude 3) by analyzing token probability distribution signatures.
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.
Analyzes structural inconsistencies that occur when a user prompts an AI to 'rewrite' or 'improve' text multiple times.
Visualizes the text with a color-coded probability scale at the sentence and phrase level.
Supports detection in over 50 languages using multilingual embedding models.
Compares new text against a vast database of previously indexed AI-generated patterns.
Enhanced statistical analysis that adjusts for niche technical jargon and academic styles.
Analyzes text to see if it was likely generated as a structured prompt for Midjourney or DALL-E.
Students using LLMs to bypass traditional plagiarism detectors.
Registry Updated:2/7/2026
Preventing Google ranking penalties for mass-produced AI content.
Ensuring clients receive human-authored work from contractors.