I already have active ChatGPT pro and equity market-data subscriptions; now I need a small but solid proof-of-concept that runs on its own. The goal is to let an AI agent automatically pull, clean, and analyse equity-market information while remaining flexible enough to bolt on new feeds later. Key focus—Integration with different data sources For this first milestone I only expect a lean, working blueprint: • Create a Python-based LangChain (or comparable) pipeline that schedules calls to at least two sample equity feeds, ingests the data, and hands it to the LLM for a brief statistical or narrative summary. • Keep each connector, transformation step, and analysis block in discrete modules so I can swap or add sources (CSV drops, REST APIs, websockets) without touching core logic. • Demonstrate the pipeline on a modest data volume, showing it can queue jobs rather than crash when input grows. • Package everything in a repo with a clear README: setup commands, environment variables, and where to plug in additional feeds. Dockerisation is welcome if it speeds onboarding.