Fast, affordable cleaned financial datasets + on‑demand web scraping

Many hedge funds, asset managers, fintechs and proprietary trading shops (roughly 50,000 institutions) collectively spend about $120k each per year on data and scraping services but repeatedly face brittle parsers, long engineering lead times and vendor lock-in that make web-derived signals expensive and slow to consume. The problem is especially acute for smaller funds and fintechs that lack dedicated scraping teams and prefer one-off or modular datasets instead of large annual contracts. A viable product is a pay-as-you-go platform that bundles cleaned financial datasets with on-demand web scraping: ML-based parsers and headless-browser orchestration for extraction, standardized schemas and provenance metadata, an API and a marketplace of reusable scrapers, with targeted SLAs (e.g., 24-hour turnaround for standardized sources, 48–72 hours for bespoke scrapes). The macro tailwinds make this attractive now — a $6.0B addressable market, strong adoption of alternative data, automation that lowers time-to-delivery and cost-per-dataset, and buyer preference for lower-ticket modular purchases (market score 88/100, revenue potential 82/100). This approach can stand out by combining low-ticket, modular pricing with measurable data quality (precision/recall SLAs), clear provenance, and a compliance-first model to manage legal and IP risk; operational strengths would include a growing library of reusable parsers and tooling to reduce marginal cost per scrape. Be candid about challenges: maintaining fragile scrapers at scale, regulatory and terms-of-service risk, medium competitive intensity, and margin pressure when serving smaller buyers — a recommended next step is a focused pilot with 10 customers to validate unit economics, time-to-delivery, and uptake before a larger rollout.