Executive Summary

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.

Market Opportunity

Aggregated, AI-summarized stock research from multi-source data targets a $9.6B = 160M active retail investors worldwide x $60/yr average spend on premium research total addressable market with medium saturation and a year-over-year growth rate of 10-20% growth driven by retail participation, API access, and subscription adoption.

Key trends driving demand: Retail-trader growth -- more individual investors using online platforms increases demand for easy-to-use research tools; AI summarization -- LLMs enable converting dense filings/news into short, actionable insights for traders; API commoditization -- accessible market-data and broker APIs reduce integration friction and speed product development; Shift to subscription micro-SaaS -- traders pay recurring fees for curated, time-saving workflows.

Key competitors include TradingView, Seeking Alpha, Stock Rover, Koyfin, Workarounds: Yahoo Finance / Google Finance / Broker Tools.

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

Executive Summary

Market Validation

Market Opportunity

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