Advanced Deep Learning AI Detection Technology
Our state-of-the-art detection model uses cutting-edge deep learning to identify AI-generated content across multiple languages and models with industry-leading accuracy.
Detection Accuracy
False Positive Rate
Languages Supported
Advanced AI Detection Technology
Our cutting-edge dual-model approach combines the best of traditional machine learning and modern deep learning for the most accurate AI content detection available.
Dual-Model Detection
Combines XGBoost feature analysis with RoBERTa transformer models for superior accuracy across all AI models including GPT-4, Claude, and Gemini.
Paraphraser Shield
Advanced detection against paraphrasing tools and evasion techniques that other detectors miss, ensuring reliable results even with manipulated content.
Deep Learning Architecture
End-to-end neural network trained on diverse datasets with transformer-based semantic analysis for comprehensive pattern recognition.
Multilingual Support
Trained on 50+ languages with de-biased models for ESL learners, providing fair and accurate detection across global content.
Real-Time Processing
Lightning-fast analysis with sentence-level highlighting and instant results, processing documents up to 50,000 characters in seconds.
Confidence Calibration
Platt scaling and isotonic regression provide reliable confidence scores with uncertainty quantification for trustworthy predictions.
Why Our Detection is Superior
Industry Leading
Most accurate AI detection available
How AI Detection Works
Our technology uses deep learning to keep pace with AI advancements, delivering precise and reliable results that help you understand the origin of a piece of text.
Input Text
Accepts copy and pasted text, DOCX, PDF, and image files, analyzing up to 50,000 characters at a time.
Deep Learning
We employ an end-to-end deep learning approach, trained on text datasets from the web, education, and AI-generated from a range of LLMs.
Sentence Classifier
A sentence-by-sentence classification model determines the probability and confidence that a text was created by AI.
Paraphraser Shield
We defend against tools looking to exploit AI detectors. Our model shields against common methods to bypass AI detection, such as paraphrasing and homoglyph attacks.
Output Result
You can view easy-to-interpret results in our dashboard, with premium features to detect AI vocabulary, plagiarism, and citeable sources.
Leading AI Detection Research
Our research-driven approach combines cutting-edge machine learning with rigorous methodology to deliver the most accurate and reliable AI detection available.
Dual-Model Ensemble
Revolutionary approach combining XGBoost feature-based classifier with fine-tuned RoBERTa transformer. This hybrid architecture captures both statistical patterns and semantic nuances that single-model approaches miss.
Comprehensive Training Data
Carefully curated dataset with balanced samples from diverse sources including academic papers, web content, professional writing, and AI-generated text from multiple language models for robust training.
Advanced Feature Engineering
Sophisticated extraction of 100+ linguistic features including perplexity patterns, burstiness metrics, readability scores, entropy measures, and syntactic ratios for interpretable and reliable detection.
Calibrated Confidence Scoring
Implementation of Platt scaling and isotonic regression techniques to provide reliable confidence scores with uncertainty quantification, ensuring predictions are both accurate and trustworthy.
Sentence-Level Precision
Granular analysis providing sentence-by-sentence detection with intelligent aggregation to document level, offering detailed insights into which specific sections contain AI-generated content.
Cyclical Development Process
Our deep learning model undergoes continuous improvement through rigorous testing, training, and refinement.
Data Collection
Carefully curated datasets with balanced samples from diverse sources including human writing and AI-generated content
Model Training
Training dual-model ensemble with supervised learning on millions of documents for robust pattern recognition
Testing & Validation
Rigorous evaluation on never-before-seen datasets to ensure reliability and accuracy across diverse content
Deployment & Monitoring
Continuous monitoring and updates to adapt to new AI models and maintain peak performance
Confidence Scores
We provide confidence categories for our classifications to ensure you can trust and interpret the results appropriately. Our calibrated confidence scoring uses Platt scaling and isotonic regression techniques.
These categories are tuned through rigorous testing on diverse datasets to provide reliable predictions you can trust.
High Confidence
>99% AccuracyHighly reliable predictions with minimal error rate
Medium Confidence
ModerateGood reliability, may require additional context
Low Confidence
UncertainResults should be interpreted with caution
Mixed Classification
Our model outputs three possible classifications instead of a simple binary result, allowing for more nuanced AI detection:
Human Only
Content written entirely by a human
AI Only
Content written entirely by an AI
Mixed
Content written by a mix of human and AI
False Positives
A false positive in AI detection is when an AI detector incorrectly classifies human writing as AI. We keep our false positive rate at no more than 1% when evaluating AI versus human text.
This is especially important for educators and institutions to avoid false claims and ensure fairness in assessment.
99% Accuracy Rate
Our model achieves 99% accuracy when spotting AI-generated text versus human writing across diverse datasets and content types.
We continuously test and refine our model to maintain this high accuracy as new AI models emerge.
- • Higher accuracy (99%+)
- • Lower false positives (<1%)
- • Dual-model ensemble approach
- • Paraphraser Shield against evasion tools
- • Peer-reviewed research (ACL, NeurIPS, ICML 2024)
FAQ
Frequently Asked Questions
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