<![CDATA[Purpose – Micro, Small, and Medium Enterprises (MSMEs) in Indonesia face difficulty producing digital marketing content consistently due to limited human resources and digital literacy, while existing generative AI applications still demand high technical skills and switching between platforms. This study aims to design, build, and evaluate "Reka," a WhatsApp-based AI marketing assistant that integrates generative AI for text and images into a single end-to-end conversation flow. Method – The study uses a Design Science Research approach. The system was built on the n8n architecture and Google Gemini with a rule-based NLU pipeline to handle natural-language input across seven functional modules. The evaluation involved 27 respondents who ran eleven usage scenarios through Black Box Testing, the System Usability Scale (SUS), and the Perceived Content Quality Evaluation (PCQ). Findings – The evaluation showed a Black Box Testing weighted success rate of 85.4%, an SUS score of 68.80 (Grade C/Marginal Acceptable), and a PCQ score of 3.81 out of 5.00. Nine semantic bug cases were also found, categorized into three patterns. Research Implications – Reka demonstrates initial feasibility as a WhatsApp-based AI marketing assistant prototype in a controlled testing environment, with noted room for improvement in visual quality and the content-revision feature. Further validation with actual MSME operators is still needed, given that the respondents in this study were students role-playing as owners of fictitious MSMEs. Originality – This study integrates generative AI for text and images into a single end-to-end WhatsApp conversation flow to produce MSME marketing content, encompassing seven functional modules within one system.]]>
