Fuora Social: An AI-Driven Microservices Framework for Automated Social Media Content Generation and Influencer Workflow Optimization

Abstract

 Increasing demands for content creators and influencers have resulted with the advent of social media platforms. They have to deliver up-tempo engaging content on multiple platforms consistently. Existing social media monitoring tools provide partial answers: they concentrate on specific areas such as scheduling or simple analytics. These solutions have no learning capabilities and no AI-powered content generation. This paper presents Fuora Social, a microservices based framework. It leverages transformer-based generative AI, intelligent scheduling, real-time analytics, human validation to enable end-to-end influencer workflow solutions. The proposed system has a multi-layer architecture as follows: user interface, authentication, application logic, AI services, data storage, and external API services. A domain-specific transformer model generates content such as captions, hashtags, and video scripts for a number of platforms. It is accomplished via context encoding, structured prompt conditioning, and refinement. In turn, recommendation systems are continually adapted to improve the quality of content recommendations according to the engagement metrics received post publication. A pilot study following 25 influencer accounts on Instagram, Facebook and YouTube over a period of 30 days demonstrated time savings of 73% in creating content, decreasing from 22 minutes to 6 minutes. Posting consistency also improved by 26%, from 63 to 89%. Engagement growth spiked by 240%, jumping from 5% to 17%. User satisfaction scores went up from 3.8 to 4.4 on a 5-point scale. The microservices architecture also enables independent scaling and management of each AI module to prevent system failure and solve the bottleneck problem that existing traditional social media management platforms are facing in terms of scalability.