Without the standards engines, the behavioral AI platform is a self-referential system that cannot demonstrate compliance, compare itself against external benchmarks, or produce the evidence needed for certification.

The Engines

  • Compliance Architecture Engine — Maps every engine output to the regulatory requirements it affects. Produces compliance reports per jurisdiction on demand.
  • Benchmarking Engine — Measures engine accuracy against external benchmark datasets and tracks calibration drift over quarterly periods.
  • Testing and Certification Engine — Runs a defined test suite against every engine before it is promoted from research-only to active. Certification requires passing 95% of tests for 14 consecutive days.
  • Observability Engine — Production telemetry: latency per engine, error rates, confidence distribution, and anomaly detection on output distributions.

Code Walkthrough

// Compliance report generation
function generateComplianceReport(jurisdiction, dateRange) {
  const activeEngines = engineRegistry.getActive();
  const requirements  = regulatoryMap[jurisdiction];

  return requirements.map(req => ({
    requirementId:  req.id,
    requirementText:req.text,
    satisfiedBy:    activeEngines.filter(e =>
      req.engineCategories.includes(e.category)
    ).map(e => ({ engineId: e.engineId, version: e.version })),
    complianceEvidence: auditLog.query({
      dateRange,
      engineIds: req.engineCategories.flatMap(c => enginesByCategory[c]),
    }),
  }));
}

What to Watch For

  • Compliance reports are only as good as the regulatory mapping. Have a qualified legal reviewer validate the mapping for each jurisdiction before relying on it.
  • Observability data is itself personal data if it contains per-actor scoring information. Aggregate before storing where possible.