No result is interpreted before the exact AI system boundary exists.
Product walkthrough · no canned pass screen
Watch evidence move without losing meaning.
The useful demonstration is not a dashboard tour. It is whether the same evaluation can keep its identity, limits, review state, failure history, and framework context from test run to accountable decision.
Evaluator, threshold, artifact, limitation, and capability state travel together.
Human review, finding, remediation, retest, and package remain linked.
Four chapters. One chain.
Open each chapter to inspect the evidence the next accountable person should receive.
01 · Define the subject
Start with the exact AI system, model or agent version, deployment, owner, and intended use. Evaluation without a subject boundary is not reusable evidence.
- System identity
- Framework assignment
- Owner and deployment context
02 · Ingest the run
Run a supported evaluator or import a bounded output. Preserve the evaluator identity, threshold, artifacts, limitations, and capability state.
- Evaluation identity
- Metric and threshold
- Raw artifact and limitation
03 · Review the mapping
A technical result can create a candidate relationship to a control. A reviewer accepts, rejects, or narrows it with recorded rationale.
- Candidate mapping
- Reviewer decision
- Rationale and review state
04 · Close the loop
Turn failures into findings, assign remediation, and link a clean retest without erasing the history. Compile the package at a stated evidence cutoff.
- Finding and risk
- Remediation and retest
- Framework-pinned manifest
What this walkthrough will not fake.
No “22 dashboards,” invented pass rate, live integration logo wall, or automatic compliance result. Current capability states and recovery boundaries stay visible.
LiveEnvironmental evidence path
BoundedExternal-provider LLM review
BoundedAI BOM metadata transformation
TargetImmutable Evidence Passport contract
Use your own evaluator output