Trust

Trust boundary: private evidence with human-reviewed decisions

The workflow is designed so teams can use sensitive context while retaining control over what evidence changes recommendations.

1

Auto-map proposes facts; human review approves what enters the model.

2

Teams can work with redacted or aggregate evidence where needed.

3

Recommendations remain inspectable for governance and stakeholder challenge.

Private evidence options

Use public pages, internal notes, metadata, aggregates, or redacted samples to start and deepen assessments.

Human review controls

No recommendation should change just because extraction guessed a fact. Reviewers decide what is accepted into the model.

Controlled deployment boundary

Teams can run analysis against approved LLM endpoints and keep decision workflows aligned with internal governance.

Auditability for stakeholders

Scoring rationale and sequencing logic remain visible so sponsor conversations focus on assumptions and evidence, not black-box outputs.

Related resources

Continue exploring methodology, samples, and practical assessment assets.

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