kuriko xx@kuriko_i81

Problem: Businesses lose over $10mil rev opportunities on overhead expense on lead generation efforts. Solutions: Automate outbound process through the following steps: 1. **Planning Media Mix**: - Answer the questions of target audience and budget size. - Provide budget allocation among multiple media platforms. 2. **AI-Powered Messaging Creation**: - For each media, triggers IBM watsonx Assistant (via API call) to generate messaging that capture audience attention. 3. **User Interaction**: - Presents the analysis and recommendations to the user through a Vercel interface. - Allows the user to choose whether to accept or reject each messaging or add points for improvement. 4. **Response Generation**: - Collects all user feedback, including analysis and recommendations for each media. - Passes this information back to IBM watsonx Assistant. - Generates a final response, refining the messaging based on the analysis and user input.

Business Problem: MNCs lose $120 billion in annual revenue opportunities due to insufficient localization efforts in their promotions. Ineffective localization can hinge brand trust and cost brand reputation, while decreasing sales and customer acquisition. The Malay-speaking market is a strategic gateway to the broader Muslim market for MNCs. However, entering this market presents significant challenges, including lengthy agency hiring processes and potential cultural and religious misunderstandings. On top of that, traditional A/B testing is a slow and inefficient process, often taking 3-6 months to see the performance due to organizational bureaucracy and insufficient ML infrastructure. Content selection is frequently arbitrary and subjective, further complicated by the need for Malay language translation. Goals: To help businesses reach 290 mil. Malay speakers and enter the market with ease - leveraging ML-powered personalized messaging Solutions: Our plug-in platform automates the entire cross-cultural marketing process. From website analysis to message delivery, it ensures culturally sensitive communication with Malay speakers, eliminating the need for manual translation and human intervention. Technical approaches: - Use TogetherAI to preprocessed the dataset downloaded from HuggingFace - Fine-tuned Llama 3.2 using the refined dataset via TogetherAI - Used LlamaIndex to template prompts and execute the model

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