Quality diagnostic
Find the conditions that make a workflow hard to trust, reuse, or expose to controlled AI use.
Design stance
Deterministic scoring, readable outputs, and explicit limits.
Logic
Primary-literature-grounded, deterministic scoring with explicit blockers and rationale.
Output
Readable reports, stronger charts, and artifacts that hold up in planning and review.
Two areas
DQ is the fuller six-dimension diagnostic. Readiness Check is the shorter workflow snapshot.
DQ
Diagnose the operating dimensions that determine whether a workflow can be trusted, reused, and prepared for controlled AI use.
Use the Data Quality Diagnostic when you need the fuller diagnosis first: where trust breaks, where structure collapses, and what to fix next.
RC
Estimate the current workflow condition and show what still blocks dependable reuse.
Use the Readiness Check when you need a shorter first-pass snapshot with explicit capability gaps.
What DQ produces
When to use it
Choose DQ when a team keeps losing time to metric disputes, recoding, inconsistent judgement, or weak AI readiness.
What changes after the report
The report is built to support a near-term operating decision, a stakeholder review, or a scoped follow-on effort, not just a score reveal.