Executive Summary

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.

Market Opportunity

On-demand clean financial datasets + rapid custom web-scraping delivery targets a $8.0B = 40,000 investment firms & fintechs x $200K avg annual spend on data & alternative-data services total addressable market with medium saturation and a year-over-year growth rate of 15% CAGR (alternative-data & data-as-a-service for finance).

Key trends driving demand: Alternative-data adoption -- Asset managers and quant shops increasingly rely on non-traditional datasets for alpha, creating demand for niche, cleaned financial tables.; AI-assisted extraction -- LLMs and ML parsers reduce manual cleaning work and enable higher-quality, lower-cost dataset products.; API-first tooling -- Demand for ready-to-ingest CSV/JSON outputs grows as teams prioritize speed-to-analysis over building scrapers..

Key competitors include Nasdaq Data Link (formerly Quandl), Alpha Vantage, Kaggle Datasets (Google), Zyte (formerly Scrapinghub), Upwork / Freelancers (workarounds).

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

Executive Summary

Market Validation

Market Opportunity

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