Applications

SEMT is used as a decision review and governance layer in environments where outcomes must be traceable, defensible, and suitable for audit. It is applied across domains that share a common requirement: decisions must hold up under scrutiny — not just at the time they are made, but long afterwards.

SEMT does not replace existing systems or models. It operates alongside them, clarifying what results are supported, what remains uncertain, and what must not be treated as established.

Core application areas

Model Risk Management (MRM)

SEMT is applied as an independent second-line review layer for quantitative models and model outputs under declared assumptions. The focus is on documentation quality, assumption consistency, output traceability, and audit readiness.

Typical use includes post-deployment review, audit preparation, and internal model risk governance. Early applications commonly include IFRS 9 credit risk models, without being limited to a specific regulation or model class.

KYC / AML & Evidence-Based Assessments

In financial crime and compliance settings, SEMT is applied to review claims against documents, transaction data, or external sources. Outputs clearly distinguish verified findings from items that require further investigation or manual escalation.

This supports regulatory expectations around audit trails, decision transparency, and defensible case handling.

AI Governance & Human Oversight

SEMT supports AI governance by acting as a review layer for model-driven or automated outputs. It helps organizations maintain human oversight by making decision boundaries and uncertainty explicit.

This is particularly relevant for systems classified as high-risk, where transparency and traceability are expected under emerging AI regulation.

Chat Systems & Human-Facing AI

SEMT is applied to the review of AI outputs used in customer support, internal tools, or decision support. Outputs are assessed for quality issues, policy violations, sensitive data exposure, and recurring risk patterns.

Reviews are reproducible and suitable for follow-up analysis, audit, or quality improvement initiatives.

Legal & Healthcare Decision Support (Early-Stage)

In legal and healthcare contexts, SEMT is explored as a way to preserve professional judgment while preventing false certainty. The focus is on clearly separating supported conclusions from unresolved or out-of-scope outputs.

These applications remain carefully scoped and are pursued only in settings where responsibility and human judgment cannot be delegated to automated systems.

Working with existing AI and LLM systems

Many applications involve large language models or other AI systems. SEMT is designed to complement these technologies by providing structured review, classification, and audit-ready documentation of their outputs.

LLMs are a strong input to SEMT — but not a requirement. The same review logic can be applied to statistical models, documents, or human-generated assessments.

Access and collaboration

SEMT applications are developed through pilots and scoped engagements. Details of implementation, configuration, and evaluation are shared under appropriate confidentiality.

→ Contact us to discuss your application

Note: SEMT is not a general analytics or decision-making system. It is designed to support review, governance, and accountability in high-consequence environments.