GENAI MARKETING CONTENT GENERATOR

Authors

  • 1 A Praveen, 2 M Anusha, 3 M N V L D Narmada, 4 G Jahnavi Author

DOI:

https://doi.org/10.62643/

Abstract

The GENAI Marketing Content Generator is an intelligent AI-powered system developed to automate and enhance digital marketing content creation using Generative Artificial Intelligence technologies. In modern digital marketing, businesses require large amounts of engaging, high-quality, and personalized content for social media platforms, advertisements, email campaigns, blogs, and product promotions. Creating such content manually is time-consuming, repetitive, and resource-intensive. To address these challenges, the proposed system utilizes advanced Natural Language Processing (NLP) and Large Language Models (LLMs) to generate creative, context-aware, and human-like marketing content automatically. The system allows users to provide inputs such as target audience, product details, keywords, campaign objectives, content type, and writing tone. Based on these inputs, the AI model generates customized marketing materials including social media captions, advertisements, email campaigns, promotional messages, blog content, and product descriptions. The generated content maintains consistency in brand voice, improves creativity, and supports scalable content production for businesses and digital marketers. The complete implementation pipeline includes input processing, prompt engineering, AI model integration, content generation, and output customization. The application is developed using Python and modern AI frameworks within the VS Code development environment. Advanced Generative AI models process user requirements and generate relevant content dynamically in real time. The system improves marketing efficiency by reducing manual effort, accelerating content production, and enabling continuous content generation across multiple platforms.

Downloads

Published

12-06-2026

How to Cite

GENAI MARKETING CONTENT GENERATOR. (2026). International Journal of Engineering Research and Science & Technology, 22(2(1), 2458-2471. https://doi.org/10.62643/