AI Detector

Analyzes submitted text through a trained neural classifier to determine probability of AI generation, returning a confidence score and binary classification (AI-generated vs human-written). The system processes input text through feature extraction layers that identify statistical patterns, linguistic markers, and stylistic anomalies characteristic of LLM outputs, then applies a decision threshold to produce instant results without requiring API calls or external model inference.

Accepts multiple text submissions (either pasted individually or uploaded as structured data) and processes them sequentially through the authenticity classifier, aggregating results into a downloadable CSV or JSON report with per-document scores, classifications, and metadata. The system queues submissions and distributes inference across available compute resources, though without true parallel processing—each document is classified serially with results cached to prevent duplicate analysis.

Returns a numerical confidence score (typically 0-100 scale) representing the model's certainty that text is AI-generated, paired with interpretive guidance on what different score ranges mean. The system applies configurable decision thresholds (e.g., >75 = likely AI, 25-75 = ambiguous, <25 = likely human) and may provide explanatory text highlighting specific linguistic features that contributed to the classification, though the exact feature attribution mechanism is not transparent.

Extends the authenticity classifier to handle text in multiple languages beyond English, applying language-specific feature extraction and classification models. The system detects input language automatically (or accepts explicit language specification) and routes text to the appropriate language-trained classifier, though support is limited to a subset of high-resource languages and performance degrades for low-resource or code-mixed inputs.

May integrate with or reference plagiarism detection capabilities (either native or via third-party APIs like Turnitin) to provide a combined authenticity check—flagging both AI-generated content AND plagiarized human content in a single analysis. The integration approach is unclear from available documentation, but likely involves either sequential API calls or a unified scoring interface that combines AI detection confidence with plagiarism match percentages.

Exposes the authenticity classifier as a REST API endpoint, allowing developers to integrate AI detection into custom applications, LMS platforms, or content management systems without using the web UI. The API likely accepts JSON payloads with text content and returns structured JSON responses with confidence scores and classifications, though rate limiting, authentication mechanisms, and SLA guarantees are not documented.

Analyzes stylistic patterns within submitted text (vocabulary diversity, sentence structure, punctuation habits, tone consistency) to detect sudden shifts that might indicate AI generation or content splicing. The system builds a statistical profile of the author's baseline writing style from the submitted text itself or from a reference corpus, then flags sections that deviate significantly from that profile as potentially AI-generated or plagiarized.

Provides user authentication and account management, allowing users to create accounts, log in, and maintain a history of previous text submissions and their detection results. The system stores submission metadata (timestamp, text preview, scores, classifications) in a user-accessible dashboard, enabling users to track detection patterns over time and compare results across multiple submissions without re-running analysis.