AI Text Detector

About this API

The AI Text Detector API is a specialized tool designed to determine the origin of a piece of text, distinguishing between human-written content and text generated by artificial intelligence models. As AI writing tools like GPT-3 and GPT-4 become more prevalent, this API provides a crucial layer of verification for anyone who needs to ensure text authenticity. By analyzing linguistic patterns, sentence structure, and other nuances, the API calculates the probability that a given text was machine-generated. It is an essential utility for educators, publishers, content managers, and developers looking to uphold academic integrity, maintain content quality, and verify the originality of written work in a fast, automated fashion.

Key Features

• Advanced AI Detection: Trained on vast datasets, the API is fine-tuned to recognize the distinct patterns and artifacts characteristic of leading large language models, providing a high degree of accuracy.
• Clear Probabilistic Scoring: Instead of a simple yes/no answer, the API returns a precise probability score (e.g., 98% likelihood of being AI-generated), allowing users to set their own confidence thresholds for flagging content.
• Simple, Fast Integration: With a straightforward single endpoint, the API is incredibly easy for developers to integrate. It requires only the raw text as input, enabling rapid implementation into any existing application or workflow.
• Structured JSON Response: The API delivers a clean and easy-to-parse JSON output containing a clear prediction ("AI-Generated" or "Human-written") and the corresponding confidence probability, making the result simple to interpret and act upon.

Use Cases

Scenario 1: Enhance an Academic Integrity Platform

• Situation: A university wants to update its assignment submission portal to check for unauthorized AI usage in student essays.
• Implementation: The university's IT department integrates the AI Text Detector API into their existing plagiarism-checking workflow. When a student submits a paper, the system first checks for plagiarism and then sends the text content to the API endpoint. If the API returns a high probability of being AI-generated (e.g., greater than 90%), it flags the submission for the professor's review, providing them with an additional data point to ensure academic honesty.

Scenario 2: Automate SEO Content Quality Control

• Situation: A digital marketing agency that manages a large team of freelance writers needs to ensure that all submitted blog posts are original human work to avoid potential SEO penalties from search engines.
• Implementation: The agency builds the API into its internal content management system (CMS). When a writer submits an article, the CMS backend automatically sends the text to the AI Text Detector API. If the returned prediction is "AI-Generated" and the probability is high, the system automatically rejects the article and sends a notification to the writer and an editor for review, thus safeguarding the agency's content standards.

Scenario 3: Screen Candidate Submissions in Recruitment

• Situation: A human resources department is overwhelmed with applications and suspects many candidates are using AI to generate their cover letters and answers to screening questions.
• Implementation: The company's Applicant Tracking System (ATS) is configured to use the API. When a candidate submits their application, the text from the cover letter and open-ended questions is sent for analysis. The resulting probability score is attached to the candidate's profile. While not used for automatic disqualification, a high AI score can serve as a signal for the hiring manager to probe deeper into the candidate's skills and experience during the interview.

How it Works: Endpoints & Response

This API functions by receiving raw text through a single, dedicated endpoint and returning a detailed analysis.

To use it, a developer sends a POST request to this endpoint with the text they wish to analyze in the request body. The API processes the text and returns a simple, structured JSON object. This object contains two key fields: prediction, which gives a direct verdict such as "AI-Generated" or "Human-written", and probability, a numerical value representing the model's confidence in that prediction. This clear and concise response allows developers to easily implement logic based on the result, such as flagging content, holding it for review, or approving it.