Method

How the diagnostic works, what it stores, and what it does not claim

This alpha is built for transparency. The scoring engine is deterministic, the report assistant is optional, and privacy boundaries are made explicit.

Deterministic coreOpen-source alphaBranded PDF export

Design stance

Built for trust, not novelty.

Logic

Handbook-grounded, deterministic scoring with explicit blockers and rationale.

Output

Readable reports, stronger charts, and artifacts that hold up in sponsor conversations.

Two product areas

DMM is the fuller root-cause diagnostic. DRL is the lighter maturity-band view.

DMM

DMM Diagnostic

Open area

Diagnose the Ten Root Conditions that block trustworthy people analytics and AI readiness.

Use DMM when you need to find what is actually broken in a workflow, not just label maturity at a distance.

Open DMMFull diagnostic

DRL

DRL Diagnostic

Open area

Estimate the likely Data Readiness Level for a workflow and show the gap to DRL 7.

Use DRL when you need a sharper maturity signal for sponsors, grounded in deterministic evidence rather than a vague quiz.

Open DRLBanding view

Method

  • DMM is the primary diagnostic. It scores the Ten Root Conditions deterministically from the questionnaire and optional evidence inputs.
  • DRL is a derived maturity interpretation layered on top of the same deterministic signals.
  • The report assistant can help interpret a finished report, but it does not change scores, blockers, or DRL banding.

Privacy

  • Assessment answers, saved reports, and report history are stored in this browser for alpha convenience only.
  • Raw CSV evidence and pasted free-text notes are processed in the active tab and are not retained in draft autosave.
  • Do not upload PII, confidential employee records, or anything you are not authorized to use in this tool.
  • Share links are portable report snapshots. They carry report content in the URL fragment, so only share them deliberately.

External enrichment

  • This alpha does not run automatic company-search or web-enrichment behind the scenes.
  • Recommendations come from the questionnaire, deterministic scoring model, optional user-provided evidence, and optional report chat context.
  • Any future external enrichment should be opt-in, source-backed, and kept separate from the core DMM and DRL scoring logic.

Alpha limitations

  • This public alpha is improving accessibility and security, but it is not claiming formal WCAG 2.2 AA, OWASP, or regulatory certification yet.
  • AI chat availability can be limited because provider usage is budget-capped and rate-limited.
  • Browser-local storage is a temporary alpha design, not the final persistence architecture.
  • AI assistant responses can be wrong or incomplete and should not be treated as legal, compliance, or employment advice.