Role-based use cases
How different people use NSDM to build more reliable AI.
The same benchmark data and theory support different decisions. This page translates NSDM into concrete workflows and outputs.
ResearchersClassify failure cases, compare models near boundaries, test calibration, and publish reproducible benchmark findings.Outputs: datasets, papers, benchmark cards, error analyses.
AI buildersEncode evidence checks, abstention, refusal, escalation, source verification, and human review into products.Outputs: guardrail specs, evaluation harnesses, release gates.
Enterprise architectsMap models, data, workflows, vendors, and authority boundaries across the AI stack.Outputs: reference architecture, routing policy, sovereignty map.
Risk and governanceTurn policy into machine-testable controls and identify unsupported, under-specified, or authority-missing decisions.Outputs: control matrix, evidence ledger, audit and escalation reports.
Boards and executivesChallenge AI claims using consequence, evidence quality, served interest, cost, and accountability.Outputs: executive justification report, deployment decision, risk acceptance.
RegulatorsInspect whether decisions are traceable, contestable, within authority, and matched to the consequence level.Outputs: evaluation profile, compliance evidence, appeal tests.
Behavioural teamsUse decision-state and neuroinference evidence to reduce confusion, manipulation, and harmful friction.Outputs: decision-health dashboard, behavioural benchmark, intervention limits.
FinOps and procurementMeasure cost per justified decision, identify waste, and compare model routing against evidence needs.Outputs: TokenOps scorecard, vendor-dependence score, routing recommendation.