Kazan SEO AI Detector
Professional-grade AI content detection and semantic SEO analysis at zero cost.
Advanced linguistic entropy and semantic fingerprinting for high-accuracy AI content verification.
AI Detector by TextSense is a sophisticated natural language processing platform engineered to identify the subtle statistical markers left by Large Language Models (LLMs) such as GPT-4o, Claude 3.5, and Gemini Pro. By 2026, as AI-generated content has become indistinguishable to the human eye, TextSense utilizes a dual-engine approach: evaluating linguistic 'Perplexity' (the randomness of word choice) and 'Burstiness' (the structural variance of sentences). Unlike basic classifiers, TextSense implements a semantic fingerprinting layer that cross-references known LLM architectural weights with input text. This technical architecture allows for high-precision detection of hybrid 'cyborg' writing, where human editors modify AI drafts. Positioned as a mission-critical tool for SEO agencies and academic institutions, TextSense provides a detailed heatmap of probability at the sentence level, enabling users to pinpoint exactly where machine-generated patterns begin. The platform has evolved into an essential component of the digital trust stack, offering robust API integration for real-time content moderation and large-scale document auditing in 2026's AI-saturated media landscape.
Analyzes the probability distribution of tokens to detect the low-entropy patterns typical of deterministic AI sampling.
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.
Categorizes text as likely being generated specifically by OpenAI, Anthropic, or Meta models using vector similarity.
Identifies attempts to bypass AI detectors through 'humanizing' tools or prompt engineering tricks.
Uses a 2026-proprietary neural network to compare semantic flow against known human writing styles.
Analyzes content as it is generated or typed via WebSocket connections.
Compares document iterations to identify sudden spikes in 'AI-ness' that suggest a passage was machine-generated.
Cross-lingual detection capabilities using mBERT (Multilingual BERT) architecture.
Students using advanced LLMs to write essays that bypass traditional plagiarism checkers.
Registry Updated:2/7/2026
Generate a PDF audit report for faculty review.
Ensuring freelance writers are not submitting 100% AI-generated content that risks Google ranking penalties.
Verifying the authenticity of digital communications in legal discovery.