Commit 7999558
feat: LLM-based contact discovery workflow - 71% success vs Apollo 0%
Breakthrough discovery after Docker testing revealed Apollo API limitations:
- Apollo org enrich: 403 Forbidden (not on our plan)
- Apollo-only approach: 0% success on small/municipal clubs
- Thinking LLM approach: 71% success across 7 test courses
New workflow: Manual LLM → Parse → Apollo→Hunter waterfall → Supabase → ClickUp
- You paste LLM response into Supabase (triggers everything)
- Parsing agent extracts contacts from mixed text
- Enrichment: Try Apollo first, Hunter fills gaps
- Only enrich what needs it (missing email/LinkedIn/verification)
- Auto-sync to ClickUp for outreach automation
Key findings from 7-course testing:
- LLM finds 6.1 contacts/course (vs Apollo 0.2)
- Sources: PGA.org, CMAA, GCSAA, vendor sites, newsletters
- Vendor breakthrough: Superintendent contacts on turf supplier sites
- Course intelligence: Projects, vendors, awards for personalization
Documentation:
- progress.md: Complete testing journey and findings
- llmtoclickup_v1.md: Full workflow spec with database design
- apollo_org_enrich_api.md: API reference and limitations
- agent-debugging skill: Docker env fix + Apollo API limitations
Next: Build Render agents for automated enrichment pipeline
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>1 parent 4e76635 commit 7999558
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