On-demand clean financial datasets + rapid custom web-scraping delivery

Asset managers, quant shops and fintech engineering teams increasingly spend weeks and costly engineering hours turning messy tables and PDF reports into analysis-ready time series and stitched fundamentals; with roughly 40,000 potential buyers and an average data spend of $200K per year, that pain maps to an $8.0B addressable market. The core problem is not lack of sources but the cost and variability of cleansing, schema-normalizing, provenance-tracking and reliably updating those niche financial tables at production quality. You could build an API-first service that combines ML/LLM parsers, rules-based extraction and human-in-the-loop QA to deliver standardized CSV/JSON datasets within 48–72 hours, plus a rapid custom-scraping on-demand offering for one-off or proprietary sources. Product would include canned, subscription-ready cleaned datasets plus pay-per-scrape ad-hoc delivery, audit trails, schema contracts and enterprise-security controls to meet quant teams’ ingestion needs. This is an attractive moment because alternative-data adoption is accelerating and recent advances in AI extraction materially lower variable costs of cleaning, pushing the market score toward high demand and a revenue potential consistent with the 90/100 rating provided. To stand out you must combine demonstrable accuracy (target >99% parsing/column mapping in core tables), robust legal/licensing and provenance features, and SLAs that appeal to enterprise buyers; the main challenges will be maintaining scraper coverage and accuracy at scale, managing IP/licensing risk, and executing an enterprise sales motion rather than a purely product-led distribution.