Solve slow, messy financial research with curated datasets + fast custom scraping

Financial analysts, quant teams, and data engineers at both institutional investors and fintechs spend weeks or months assembling, cleaning, and validating disparate financial data, and smaller teams waste senior analyst time on ad-hoc scraping and normalization instead of modeling. The pain is concentrated: roughly 25,000 institutional buyers who could pay $100K ACV and another 150,000 smaller fintechs/startups who could pay $10K ACV contribute to a $4.0B addressable market, yet procurement friction and slow time-to-value keep many projects from scaling. You could build a hybrid product that combines curated, well-documented datasets with a fast custom-scraping service driven by ML extraction and human-in-the-loop QA, exposed via API, catalog UI, and pay-per-dataset microtransactions alongside enterprise subscriptions. Advances in AI-enabled extraction and normalization can plausibly shrink delivery time from months to days and materially lower unit costs, while growing demand for alternative data and preference for low-friction pricing make buyers more willing to pay for ready-to-use, provenance-rich feeds. The market score (90/100) and revenue potential (80/100) reflect these tailwinds but do not eliminate execution risk. To stand out you must deliver repeatable, auditable provenance, tight SLAs (days for custom scrapes), and integration-first products for common downstream tools, backed by SOC2/compliance guardrails that institutional buyers require. Your strengths are clear — faster time-to-insight, flexible monetization, and better documentation — but challenges include legal/sourcing complexity, the cost of scaling custom scraping, and competing with both incumbent data vendors and in-house engineering teams; addressing those will determine whether this becomes a defensible business.