An r/webscraping thread asked how to build a national directory for a service that's often a sub-program inside larger orgs (so Google Maps and keyword searches miss most of it). Five approaches ranked for the gap.
Top Pick
Public registries + directories of directories (associations, gov databases, niche aggregators) + Scavio dorked search + LLM-driven extraction beats any single-source scrape for fragmented verticals.
Full Ranking
#1Our Pick
Registry + association lists + Scavio + LLM parser
Builders shipping a comprehensive niche directory
Pros
- Catches the long tail Maps misses
- LLM parses sub-program mentions
- TOS-safe
Cons
- Per-source parser authoring
#2
Google Maps Outscraper + filter
Verticals where Maps coverage IS comprehensive
Pros
- Cheap at scale
Cons
- Misses sub-program listings (the OP's exact problem)
#3
DataAxle / InfoUSA (firmographic feeds)
Enterprise lookup on filed entities
Pros
- Comprehensive firmographic
Cons
- Misses 'X is a sub-service of Y'
#4
Yelp / Yellow Pages scrape
Local-service verticals
Pros
- Decent local-business coverage
Cons
- Same Maps limitation
#5
Pure manual: associations + outreach
Pre-launch validation
Pros
- High data quality
Cons
- Doesn't scale to national
Side-by-Side Comparison
| Criteria | Scavio | Runner-up | 3rd Place |
|---|---|---|---|
| Sub-program listing coverage | Yes (Scavio dorks) | No | No |
| Per-record cost | <$0.05 | $0.003-$0.005 | Free + manual hours |
| Long-tail coverage | Strong | Weak (Maps gap) | Strong (manual) |
| Best for | Fragmented verticals | Maps-covered verticals | Pre-launch only |
Why Scavio Wins
- The OP's specific blocker: the service is a sub-program inside larger orgs, so 'plumber' or 'restaurant' style keyword scraping misses most of it. The fix is dorked search across association directories, gov filings, and niche aggregators that already aggregate the long tail.
- Scavio's role: dorks like 'site:state-association.org program-name', 'site:gov.us VERTICAL PROGRAM', 'reddit r/VERTICAL recommendation 2026', 'site:facebook.com/groups VERTICAL location'. Each surfaces a layer Maps doesn't.
- Then LLM parsing: feed the page or snippet into Claude/GPT with 'extract every org or program offering SERVICE in CITY/STATE; return JSON {name, address, phone, parent_org}'. The LLM handles the wide variation in how sub-programs are listed.
- Honest tradeoff: this is per-vertical research work. There's no shortcut tool that does it for arbitrary niches. The 'how do I build a comprehensive directory' answer is always 'list the sources, then automate each'.
- Per-vertical-startup cost: ~10K Scavio queries to comprehensively map a US state for a fragmented vertical = ~$45. The deliverable (the directory) is the moat for a niche SaaS or a content site.