Aepistle- Content Moderation in Social Media Applications


Aepistle, the dynamic social media application, redefines online interaction by offering a comprehensive platform for global connectivity and engagement. The challenge was significant: create a standout social media platform in a crowded digital space. Discover how this project successfully combined innovative technology with a unique user experience, standing out in the competitive world of social media.


Meshlogix provided the artificial intelligence solutions, and collaborated with Aepistle to implement advanced AI-driven content moderation tools. The solution involved machine learning algorithms to automatically detect and identify inappropriate content in real time.

Key Features:

  1. Image and Video Recognition: Meshlogix's AI algorithms were trained to recognize explicit images and videos, enabling automatic flagging and identification of such content. To achieve this we used deep learning models like Convolutional Neural Networks (CNNs) employed for image recognition. Binary Classification was utilized to predict whether an input image contains explicit content or not.
  2. Natural Language Processing (NLP): The NLP is used by Meshlogix's solution to enable the platform to analyze text-based content for inappropriate language, hate speech, and other forms of harmful communication.
  3. Scalability: Meshlogix's solution was designed to scale with Aepistle's growing user base and content volume, ensuring timely and efficient moderation even as the platform expanded. We keep in mind that social media will have a lot of images and videos when there will be thousands of users uploading the contents.
  4. Customization: The AI models were customizable to align with Aepistle's specific moderation policies and user community standards, allowing for fine-tuning and optimization based on feedback and evolving requirements like if there is some groups were Aepistle allowed to use that kind of content then we will easy customized the solutions with one line of code.

Key Outcomes:

  1. Improved User Safety: Aepistle got 96.2% accuracy rate in reduction to spread inappropriate and sexual content, and increased the user safety and built the trust in Aepistle social media platform.
  2. Enhanced Reputation: By proactively addressing the challenges of content moderation, Aepistle has strengthened its reputation as a responsible and user-friendly online community, attracting new users and retaining existing users.
  3. Regulatory Compliance: The implementation of robust content moderation measures helped Aepistle to mitigate regulatory risks and ensure compliance with relevant laws and regulations governing online content.
  4. Scalability and Efficiency: Meshlogix's AI solutions enabled Aepistle to efficiently moderate content at scale while accommodating platform growth without compromising the quality or speed of moderation.
  5. Cost Savings: By automating content moderation processes, Aepistle reduced the need for manual intervention and oversight, resulting in cost savings and operational efficiencies.


Throughout the partnership with Meshlogix solutions, Aepistle has successfully tackled the challenge of content moderation, leveraging the AI solutions using CNN to create a safer and more conducive online environment for users. By implementing proactive moderation measures, Aepistle not only enhanced user safety and satisfaction, but also protected its reputation and long-term viability in social media.

Major Challenge

The major challenge Aepistle faces revolves around effectively moderating user-generated content to maintain a safe and conducive online environment. With the rapid spread of social media, platforms have faced difficulties in quickly identifying and removing sexually explicit or adult content, posing risks to user safety and the platform's reputation. Without robust content moderation measures in place, Aepistle risked alienating users and facing potential regulatory scrutiny. Thus, addressing this challenge was imperative to the success and sustainability of the platform.

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