Supervised Machine Learning model using TF-IDF feature extraction and Logistic Regression classification for publishers, educators, and platforms.
Experience our detection engine in real-time.
Our pipeline converts raw text into TF-IDF feature vectors and classifies them using a trained Logistic Regression model.
Raw text is received, cleaned, and validated with a minimum 50-word requirement before processing.
Text is transformed into 5,000-dimensional feature vectors using TF-IDF with unigram and bigram analysis.
A trained Logistic Regression classifier outputs the probability of the text being AI-generated or human-written.
Supervised Machine Learning model using TF-IDF feature extraction and Logistic Regression classification.
Trained on 1,000+ labeled samples with 80/20 train-test split and evaluated using precision, recall, and F1-score.
Every prediction includes a confidence score so you can assess reliability before making decisions.
Text is analyzed in real-time and never stored on our servers. No data is retained after prediction.
Built with industry-standard Python ML libraries for reliable text classification.
Send a POST request to our /predict endpoint and receive instant classification results.
Helps educators screen student essays and research papers for potential AI-generated content.
Assists editorial teams in screening submissions for indicators of AI-generated writing.
Provides an additional layer of analysis for application responses and written assessments.