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Overview

InfoSecure Ltd., a cybersecurity firm based in North America, faced challenges in identifying and flagging inappropriate or harmful content across its platforms. The company sought a robust AI solution to enhance content moderation and ensure a safer user experience.

Technical Stack

  • Python
  • TensorFlow
  • PyTorch
  • Hugging Face Transformer
  • LangChain
  • MongoDB
  • Docker

    • Industry

    Cybersecurity

  • region

    • Region

    North America

  • project-size

    • Project Size

    Non- Disclosable

Highlights

Improved detection accuracy, reducing false positives by 25%

Processes content in real-time, ensuring a swift response

Seamlessly integrated with the client’s existing content management system

Continuous feedback loops empowered users to report content effectively

Automated moderation processes reduced manual review workloads by 40%

Challenges & Solutions

Diverse Content Types

  • Solution: The primary challenge was the wide variety of content types, including text, images, and videos, each requiring a distinct approach for effective moderation. Our AI development team integrated a multimodal LLM, leveraging textual and visual embeddings to analyze and detect harmful content seamlessly. This comprehensive approach ensured accuracy across all content types, significantly reducing manual review efforts.

Real-Time Processing Requirements

  • Solution: We implemented a scalable microservices architecture with asynchronous processing capabilities to ensure the AI system could efficiently handle high traffic volumes. Using LangChain, the solution orchestrated multiple LLM models, optimizing inference times and maintaining system responsiveness under heavy loads.

Handling False Positives

  • Solution: False positives were a major concern, as they could hinder user experience. To mitigate this, we implemented a continuous learning mechanism using feedback loops. This allowed the model to learn from its mistakes, improving its accuracy. A robust anomaly detection system was also implemented to identify unusual patterns, further refining the AI's decision-making process.

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12+

Years of Business
Experience

1458+

Happy
Customers

12+

Countries with
Happy Customers

1050+

Agile enabled
employees

Core Features

  • Advanced Content Classification
  • Multilingual Support
  • User Feedback Integration
  • Customizable Moderation Rules
  • no.-of-resources

    • No. of Developers

    05

  • time-frame

    • Time Frame

    April 2023 - February 2024

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