Abhay Rajiv Shivam, Ambili PK, Midhuna Uday, Premsai Adike, T. CYNTHIA ANBUSELVI

DOI: https://doi.org/10.5281/zenodo.23388885

Brand development involves activities such as defining positioning, understanding target audiences, developing brand identity, studying competitors, and planning marketing communication. This paper presents an AI Branding and Marketing Intelligence Framework that integrates brand strategy, identity development, market research, competitor analysis, marketing planning, and content generation into a unified workflow. The system converts business information into a structured Brand Brief and maintains it as shared context across specialized AI modules. An AI Orchestrator coordinates these modules, while Brand Memory stores important decisions and outputs. Qwen3 with Ollama provides local language-model inference, supported by external search and web extraction for current market information. The framework aims to reduce repeated prompting, maintain brand consistency.The platform also includes marketing campaign creation, execution guidance, a contextual AI Copilot, and performance analysis to support decisions beyond the initial brand development stage.

Artificial Intelligence, Branding Intelligence, Marketing Intelligence, Large Language Models, Brand Strategy, Competitor Analysis, Market Research, Generative AI.

This paper presents the implemented version of an AI Branding and Marketing Intelligence Platform that integrates brand development, strategic planning, market research, competitor analysis, marketing activities, and campaign generation within a unified application. The platform aims to overcome the limitations of conventional approaches, where branding and marketing tasks are often performed independently, resulting in repeated information entry, inconsistent decisions, and disconnected workflows. The developed system uses a React and Vite frontend integrated with a Fast API backend. Gemini serves as the primary language model, with Groq providing fallback support, while Serper and Fire crawl enable external web research and information extraction. The shared Brand Memory maintains essential business information, generated strategies, and previous decisions, allowing different modules to reuse relevant context and maintain continuity throughout the branding process. The functional evaluation demonstrated that the major modules can operate as a connected workflow, supporting brand strategy development, market intelligence, campaign creation, execution guidance, and performance analysis. Compared with independent AI prompting, the proposed platform provides structured workflow integration, persistent brand context, and access to externally sourced market information. These capabilities help users organize branding activities and make more informed marketing decisions without repeatedly supplying the same business details. The main contribution of this work is the development of a practical AI-assisted platform that combines branding and marketing intelligence with contextual information management. Although the current evaluation focuses on functional validation rather than quantitative performance measurements, the implementation demonstrates the feasibility of integrating these activities into a single decision-support system. Future improvements may include direct integration with digital advertising platforms, automated campaign publishing, real-time marketing analytics, and more extensive evaluation across different business categories. Such developments could further improve the platform’s practical usefulness, efficiency, and ability to support data-driven branding and marketing decisions.

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Abhay Rajiv Shivam, Ambili PK, Midhuna Uday, Premsai Adike, T. CYNTHIA ANBUSELVI (2026). AI Branding with Marketing Intelligence Platform for Brand Strategy, Market Research, and Campaign Generation. International Journal of Computer Techniques, 13(5), 439–444. ISSN: 2394-2231. DOI: https://doi.org/10.5281/zenodo.23388885