How 2IX matching works
A dual-portal workflow with human-readable match evidence.
The platform begins as a structured WordPress registry, then grows toward rules-based and semantic matching as the data gets cleaner.
- Capture profile signal: who is offering help, what they can do, when they are available, and how visible they want to be.
- Capture opportunity signal: who needs help, what outcome is needed, what skills are required, and what trust or screening rules apply.
- Explain fit: show skills, cause, schedule, location, verification, and handoff-readiness evidence before a match is pursued.
- Preserve continuity: publish durable project memory when an opportunity requires source files, decisions, prompts, docs, or deployment context.
Data model
People, organizations, opportunities
The MVP stores these as registry records; the later platform can split them into relational entities and application flows.
Matching
Rules first, semantic scoring next
Start with transparent fit notes, then add normalized skill tags, vector scoring, and feedback loops when records have enough signal.
Continuity
Technical work gets memory
AI-assisted and open-source projects need handoff packages, decision records, and wiki pages so contributors do not restart from scratch.