How PreMetric works
PreMetric conducts structured, bounded pre-deployment AI audits to determine whether AI initiatives should proceed, be modified, paused, or stopped. Each audit examines the technical evidence and institutional readiness behind the initiative, producing an AI Decision Record suitable for board approval, procurement scrutiny, investor diligence, regulatory readiness, and institutional accountability.
Assessment pathway
Outcome
Pre-deployment AI audit across technical and institutional dimensions
PreMetric audits the AI system and the institutional decision together. The process examines the proposed tool, vendor claims, model purpose, data assumptions, benchmark results, validation materials, performance evidence, integration dependencies, controls, deployment context, governance conditions, accountability structure, and exposure profile.
PreMetric does not issue formal certifications or standalone technical benchmark reports. Where benchmark results, validation materials, vendor assurance documentation, or certification-readiness evidence are relevant, they are assessed as audit inputs. The output is always the institutional AI Decision Record.
Structured
Consistent audit methodology applied to every initiative, including the AI system, vendor claims, evidence base, deployment context, controls, and institutional exposure.
Bounded
Point-in-time engagement with defined scope and deliverables. PreMetric audits the initiative without becoming an implementation vendor, operator, or ongoing monitoring function.
Documented
Every stage produces evidence suitable for board approval, procurement scrutiny, investor diligence, regulatory readiness, and institutional accountability.
Five stages of audit
Each engagement follows a defined audit sequence from initiative scoping through technical evidence assessment, deployment-context analysis, institutional exposure review, and documented determination. Every stage supports the final AI Decision Record.
AI Initiative Scoping
Define the AI system, intended use case, autonomy level, deployment context, decision owner, accountability structure, and evidence thresholds required for a defensible audit.
System & Evidence Audit
Assess the AI tool, vendor or internal system claims, model purpose, data assumptions, benchmark results, validation materials, performance evidence, limitations, and technical assurance inputs relevant to the decision.
Assumptions, Controls & Deployment Context
Evaluate operating assumptions, control environment, human oversight, integration dependencies, failure modes, escalation pathways, monitoring assumptions, and deployment context, including digital, operational, or physical AI environments where relevant.
Institutional Exposure & Governance
Assess capital exposure, operational risk, procurement scrutiny, governance readiness, accountability boundaries, regulatory readiness, and institutional defensibility.
AI Decision Record
Produce a documented determination with evidence, findings, assumptions, controls, exposure analysis, and a Proceed / Modify / Pause / Stop recommendation.
Technical — Institutional — Documented
AI Initiative Scoping
Define the AI system, intended use case, autonomy level, deployment context, decision owner, accountability structure, and evidence thresholds required for a defensible audit.
System & Evidence Audit
Assess the AI tool, vendor or internal system claims, model purpose, data assumptions, benchmark results, validation materials, performance evidence, limitations, and technical assurance inputs relevant to the decision.
Assumptions, Controls & Deployment Context
Evaluate operating assumptions, control environment, human oversight, integration dependencies, failure modes, escalation pathways, monitoring assumptions, and deployment context, including digital, operational, or physical AI environments where relevant.
Institutional Exposure & Governance
Assess capital exposure, operational risk, procurement scrutiny, governance readiness, accountability boundaries, regulatory readiness, and institutional defensibility.
AI Decision Record
Produce a documented determination with evidence, findings, assumptions, controls, exposure analysis, and a Proceed / Modify / Pause / Stop recommendation.
This ends in a decision
PreMetric audits conclude with one of four documented determinations. Each outcome reflects the technical evidence, institutional readiness, deployment context, and exposure attached to proceeding.
The initiative may advance as proposed, with documented rationale, sufficient supporting evidence, defined controls, accountability, and reassessment conditions.
The initiative may proceed only with specified changes to system use, scope, evidence, controls, governance conditions, implementation assumptions, or risk boundaries.
Additional evidence, validation, technical assurance, procurement diligence, governance definition, or decision conditions are required before proceeding.
The initiative should not proceed under the current assumptions, evidence base, system limitations, control environment, exposure profile, or accountability structure.
Every outcome is defensible when the AI system, evidence base, assumptions, controls, governance conditions, and institutional exposure have been properly audited and documented. The AI Decision Record serves as institutional evidence of diligence.