Data collection proved challenging due to the need for diverse, high-quality financial datasets including historical price data, news sentiment, and social media feeds, while pipeline formation required complex orchestration of data cleaning, feature engineering, and normalization processes tailored to financial time series. The most significant technical hurdle emerged when connecting the backend AI models with the frontend interface, requiring careful API design to handle asynchronous model predictions, implement efficient caching strategies for real-time stock recommendations, and ensure seamless data visualization of complex trading signals and sentiment indicators—all while maintaining responsive performance under variable user loads and market conditions. These integration challenges demanded substantial debugging and optimization to create a cohesive system where the AI recommendations could be effectively translated into an intuitive trading experience.