Over the last phase of development, I’ve been working on something much bigger than a stock screener. The goal was not just to rank stocks. The goal was to build an AI-powered market research platform that can combine quantitative scoring, predictive models, structural market context, longitudinal factor analysis, live broker-linked data, and actionable interface design into one workflow. Most retail tools stop at filters. This platform is designed to go further: it helps answer not only what looks good , but also why it looks good, what may happen next, what risk is building, and what action should be considered now . The Core Idea Instead of rebuilding everything from scratch, the architecture was designed around an important principle: Do not replace strong analytical engines. Orchestrate them. The system already had a strong quant foundation through four major engines: scoring_engine_ enhanced.py for fundamental and factor scoring ml_forecast_ engine.py for predictive return ...