Design orchestrated decision pipelines that combine deterministic rules, ML confidence scores, and policy guardrails — producing explainable, auditable outcomes that satisfy regulators and operators alike.
Combine deterministic business rules with ML-driven confidence scores in a unified execution pipeline. Policy-as-code makes underwriting guidelines and claims rules version-controlled and auditable.
Policy-as-code with versioned rule repositories
Configurable confidence thresholds for auto/human/escalation routing
DMN-standard decision modeling and notation
Shadow mode deployment for validation before cutover
Generate structured explainability records for every decision: which inputs were used, which rules fired, which model contributed what score, and the final disposition.
SHAP / LIME post-hoc explainability for complex models
Interpretable models (EBMs, GAMs) for high-stakes decisions
Rule trace logs for deterministic components
EU AI Act Article 13 transparency compliance
Define decision workflows as directed acyclic graphs with named stages: intake, enrichment, scoring, rule evaluation, human review gate, disposition, and notification.
DAG-based pipeline orchestration (Temporal / Prefect patterns)
Signed audit records at every stage
Straight-through, human-in-the-loop, and escalation paths
Immutable append-only decision ledger
Package decision outputs into structured bundles: the decision, supporting evidence, required notifications, downstream updates, and compliance artifacts — executed atomically.
Atomic execution with full rollback capability
API-first delivery to downstream systems
Auto-generated adverse action notices (ECOA / Reg B)
Regulator-ready explanation artifacts
An auto insurer processes 10,000 claims/week through fraud detection, adjuster assignment, and policy coverage checks. 65% of volume routes to fast-track automatically — each with a full decision trace that examiners can audit on demand.
Underwriting guidelines encoded as policy-as-code integrate with medical data scoring models. Every recommendation includes a structured explanation showing risk factors, applied guidelines, and confidence thresholds — fully EU AI Act compliant.
For every declined credit application, the Decision Layer captures contributing factors, ranks them by impact, generates ECOA-required adverse action reason codes, and assembles a complete regulatory package — automatically.
Faster claims processing
Automated decision workflows
Processing cost reduction
IDP + decision automation
Decision accuracy
Fully automated pipelines
Less audit prep time
Pre-assembled provenance packages
EU AI Act (Articles 9-15)
High-risk system risk management, logging, transparency, human oversight
NAIC Model AI Bulletin
Insurance AI governance, transparency, and fairness
SR 11-7
OCC/Fed Model Risk Management guidance
DMN / NIST AI RMF
Decision modeling notation and AI risk framework
Let us map your current decision points and design a traceable, compliant decision architecture.