Niche Data Refinery
Turn one niche’s messy, fragmented public information into “clean fuel for agents” — and sell it.
Definition
Pick one niche where valuable information is messy, fragmented, changing, and annoying to collect (reviews, pricing pages, job posts, ad libraries, permits). Refine it into structured data and sellable outputs: pricing maps, competitor gap reports, review-complaint summaries, hiring-signal reports, monthly market-movement reports.
Key Properties
- Niche filter: data must be valuable (drives money decisions), repeatable (needed again and again), changing (freshness matters), fragmented (hard for one person to collect), annoying (margin lives there).
- Wedge: one niche, one city, ~100 businesses, tracked manually in a spreadsheet first.
- First customer is the intermediary: agencies, consultants, and freelancers already selling into the niche will pay $300–800/mo if it helps them win/keep $5k/mo clients — an easier sale than the end business owner.
- Crawl-walk-run: report → dashboard → API → MCP tool → agent pay-per-lookup (x402 / Pay-per-Crawl).
Examples
- Med spas (source’s worked example: Botox price vs. local median, competitors promoting new treatments, hiring signals).
- Roofing (storms, permits, insurance signals), real-estate investing (zoning, tax delinquencies), e-commerce (SKUs, pricing), law firms (positioning, ad copy, intake).
Application to the business
Strong fit for Port Huron: pick one local vertical (e.g., trades, dental, restaurants), track the Blue Water Area market manually, and sell intelligence to regional marketing agencies — or use it yourself to power sharper audits and speculative rebuilds. Directly addresses the overview’s open question about which local vertical to target: the data-collection process itself reveals automation pain.