When decisions must stand up to scrutiny
SEMT helps organizations produce decisions and analyses that are
traceable, reproducible, and defensible.
File in → clear, audit-ready report out.
Used in regulated, high-consequence environments.
Where SEMT is used
When decisions may be reviewed by auditors, regulators, or courts and must be explainable long after they were made.
When claims must be checked against documents, data, or logs, and verified findings must be clearly separated from unresolved ones.
When conclusions depend on assumptions, thresholds, or error margins that need to be explicit and consistent.
When AI outputs affect customers, patients, or users, and quality, policy compliance, and uncertainty must be tracked.
Products
Model Risk Management (MRM)
Independent second-line review of quantitative models and model outputs under declared assumptions. Focuses on documentation quality, assumption consistency, output traceability, and audit readiness.
Scoped engagements aligned with internal model risk management frameworks. Early applications commonly include IFRS 9 credit risk models.
KYC / AML Evidence Review
File-based verification of claims against documents and sources. Clearly shows what is supported by evidence — and what requires manual review.
Designed for audit, compliance, and regulatory scrutiny.
Chat & AI Quality Review
Ongoing review of AI outputs for quality issues, policy breaches, sensitive data exposure, and recurring risk patterns.
Used in customer support, internal tools, and AI governance.
Quantum Audit (CHSH / Bell-test)
Deterministic, audit-grade verification of CHSH/Bell-test datasets with conservative statistical bounds. Results are delivered with clear decision states: OK / Flag / Unresolved.
File-in → reproducible report out, including manifest (hash + versioning) and figures.
Governance and model risk management
SEMT is designed to support established model risk management and AI governance practices as an independent review layer.
- Supports model validation and independent review processes
- Provides traceable decision outcomes and audit trails
- Enables post-deployment review and monitoring
- Preserves human oversight in high-consequence decisions
SEMT does not replace existing models or analytics. It clarifies what results are verified, unresolved, or out of scope under declared assumptions.
Working with LLM-based systems
Many organizations have already invested heavily in large language models. SEMT is built to complement these systems — not replace them.
- LLM outputs can be reviewed, classified, and tracked over time
- Decisions based on LLMs become reproducible and auditable
- Uncertain or unsupported outputs are explicitly marked
- The same review logic applies to non-LLM inputs
LLMs are a strong input to SEMT — but not a requirement. SEMT can be applied to model outputs, documents, statistical analyses, or human-generated assessments.
Pilot scopes are adapted to domain, risk level, and review requirements.