AI DETECTOR
An advanced content verification platform that helps educators, publishers, and businesses detect AI-generated text with high accuracy.

About the project
Detects AI-generated content with industry-leading accuracy, helping educators, publishers, and content teams ensure authenticity and maintain trust in written work.
Project Overview
The platform uses AI agents to automate notes, sourcing, reporting, and decision-making across the entire hiring funnel.

Project Goal
The primary goal of the project was to build a highly accurate and trustworthy AI detection system capable of keeping pace with rapidly evolving generative models. The platform needed to minimize false positives, support multiple languages and formats, and provide results that are transparent.


Solution
A scalable AI detection platform was developed using probabilistic analysis and pattern recognition trained on large, human-reviewed datasets. The system evaluates text at both document and sentence levels, visualizing detection signals and providing contextual explanations rather than binary judgments.
Key Challenges
Generative models are updated frequently, requiring continuous adaptation of detection algorithms.
False positives and trust issues.
Paraphrased and humanized AI content.
Sensitive academic and editorial content had to be processed securely without reuse or retention.
Key Features
AI Content Detection β accurately detects content generated by ChatGPT, Gemini, Claude, LLAMA, and other known AI models.
Sentence-Level Prediction Map β provides a color-coded breakdown of content sections likely generated by AI.
Paraphrase and Humanizer Detection β identifies rewritten and AI-humanized content designed to evade detection.
Plagiarism Checking β verifies originality across multiple languages with clear duplication insights.
Final Interface








Features Overview
AI Content Detection Engine
Scans text in seconds to detect AI-generated content with industry-leading accuracy, keeping academic and professional writing authentic.

Tech Stack
Built on Node.js and React with PostgreSQL and Redis, deployed on AWS β including a WordPress plugin for in-editor AI detection and a REST API layer for third-party integrations.
Aws development
wordpress development
Metrics
~0000
Total Development Hours
Total design, development, and testing effort invested in the platform.
0
Engineers Involved
Cross-functional team: ML, backend, frontend, and QA specialists.
00
Sprints Delivered
Agile delivery across full product lifecycle from MVP to launch.
0
Core Modules Built







