Aggregated, AI-summarized stock research from multi-source data

Retail investors and independent advisors face an overwhelming volume of research — SEC filings, earnings transcripts, news wires, analyst notes and social sentiment — and lack a durable way to turn that multi-source noise into concise, actionable trade ideas; with roughly 160 million active retail investors worldwide and an average spend of about $60 per year on premium research, the market equates to roughly $9.6 billion. The pain is time and attention: individual traders and small advisory teams need reliable, fast summaries and signal confidence scores rather than raw documents they don’t have time to parse. A viable product would aggregate licensed market data, filings, transcripts, news and alternative signals, apply structured extraction and LLM-based summarization to produce time-stamped one-paragraph thesis, risk bullets, confidence metrics and a backtestable signal history, delivered via web app, mobile alerts and an API for broker integration. This is attractive now because LLMs make extraction feasible at scale, broker and market-data API commoditization reduces integration cost, and the retail-trader cohort is still growing — those industry tailwinds underpin a Market Score of 90/100 and a Revenue Potential of 78/100. Competition is medium, so execution speed and trust-building matter. To stand out you must prioritize verifiable explainability, an auditable provenance trail for every summary, conservative confidence scoring, human-in-the-loop review for high-impact items and clear ROI tracking through backtests and pilot partnerships; these differentiate from pure-LLM players. Be honest that challenges include data licensing costs, model inference expenses, regulatory/compliance risk (disclosure and investment advice liability) and the technical work to minimize hallucinations, but if you can demonstrate consistent incremental alpha or time savings for a paying cohort, the unit economics and addressable market are compelling.