Portfolio

Enhanced chatbot capabilities by integrating Retrieval-Augmented Generation (RAG) models with Large Language Models (LLMs). This approach significantly improved response accuracy and user satisfaction, delivering a more interactive and intuitive conversational experience.

Implemented a system that transforms audio conversations into text using Speech-To-Text models, followed by analysis with Large Language Models (LLMs). This project extracts valuable insights from spoken dialogues, enhancing understanding and decision-making based on conversational data.

Leveraged generative AI to enable interactive analysis of Mpesa statements. This project allows users to effortlessly uncover insights into their spending habits, identify trends, and make informed financial decisions using simple, conversational language. Transforming financial data analysis into an intuitive and user-friendly experience.

Developed a no-code data analysis solution leveraging Large Language Models (LLMs). This project empowers users to perform complex data analyses through natural language queries, eliminating the need for programming skills and making data insights accessible to everyone.

Analyzed key metrics like customer acquisition, churn, and cohort retention to uncover drivers of e-commerce growth. Utilized Tableau for in-depth visualizations, providing clear and actionable insights that help businesses enhance customer retention and optimize growth strategies.

Specialized in building advanced ML models to transform data into actionable insights through predictive analytics. Developed robust and scalable regression models tailored to specific business challenges across various sectors, including e-commerce, logistics, fintech, and agtech. Utilizing frameworks like Scikit-learn, TensorFlow, and PyTorch, and adhering to MLOps best practices, I ensure the deployment of efficient and maintainable models that drive measurable business value and automation.