AI Powered Content Moderation

Industry

Team size

6 people

Type

Web App

Location

Germany

Timeline

4-5 months

Technologies

Java, Vue, Python

Demo Account
Email: test123@yopmail.com Password: 123456@aA

Demo Account
Email: test123@yopmail.com
Password: 123456@aA

Project Overview

This is an AI-powered content moderation tool for reviewing articles and submissions, detecting personal information, violent language, or discriminatory terms, and highlighting them quickly for editors.

Key Features

Efficient upload and processing of news content, including PDF documents.
AI-powered analysis to identify sentiment, key individuals, and potential reputational risks.
Automated tagging and classification to surface relevant insights.
Moderation interface for reviewing and refining AI-generated results.
Secure role-based access for analysts and moderators.
Comprehensive audit trails for all changes and user actions.
Seamless integration of AI insights into the content review workflow.
Scalable and modular system designed to support future enhancements and AI upgrades.
User authentication via AWS Cognito.

Challenges

High traffic during simultaneous file uploads: The system experiences heavy load when many users upload files at the same time.
Large, multi-page files affecting performance: Processing becomes inefficient when handling files with a high number of pages or large file sizes.
Continuous need for model improvement based on user feedback: User-provided feedback indicates the model often requires retraining to improve accuracy.

Solutions

Decoupled upload system with asynchronous processing: Implement a queue-based upload system using AWS S3 and Lambda to store files and trigger processing asynchronously.
Batch processing and page limitation: Handle documents in batches (e.g., page-by-page processing) or restrict the number of pages per request to maintain performance.
Active learning with feedback loop: Apply an active or continual learning pipeline, storing user annotations in a feedback database and retraining the model in batches with version control.

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