Online
bid s are rapidly gaining popularity, revolutionizing buying and selling through
dynamic e commerce platforms. [ projects that the online bid market will reach $68 billion by
2025, emphasizing the need for improved accessibility and support systems to enhanc e the
bidding experience. The increasing demand for dynamic and efficient bidding platforms has
created challenges in providing personalized user support. To address this problem, this research
develops a bidding application that integrates artificial inte lligent chatbot assistance. The
application is built using Kotlin for development , Firebase as a scalable database, and Gemini
AI’s pre trained chatbot model for interactive user support without additional model training.
The implementation involves Fireba se to manage real time data storage and retrieval and Gemini
AI to deliver chatbot functionalities like an assistant . Drawing insights from studies on real time
capabilities [ 2][ 3], Firebase’s role in live applications [ 6], Kotlin’s effectiveness in mobile app
development [ 5], and the use of pre trained AI models in user interactions [ 4], the system was
evaluated through stress testing and user feedback. Results indicate that the proposed solution
enhances user engagement, system responsiveness, and operational efficiency, offering a scalable
approach to improving bidding platforms.