Scoring describes how the platform may evaluate data quality, completeness, confidence, and workflow readiness. Current documentation should keep scoring conceptual unless a concrete scoring model is implemented.

• Completeness: whether required or useful information is present.
• Confidence: how reliable a match, location, or inferred value appears to be.
• Readiness: whether a place, asset, or workflow has enough context to proceed.

Future scoring documentation should include the actual inputs, formulas, thresholds, and review workflows once those are designed and implemented.