Performance & Drift

Live platform · interactive sandbox.You’re exploring as a guest — every module is visible. Use View as to see the platform tailored to a Chief AI Officer, RISC data scientist, OIG auditor, or leadership. All data is synthetic, and AI features run on Claude in this demo and on your AskSage / Microsoft AI Foundry gateways in production.

Deliverable 4g · Performance Measurement & Drift Monitoring

Performance & Drift

KPIs, live performance dashboards, drift detection, and ROI — the GAO Monitoring principle, made operational. Models are watched continuously; when input distributions shift, the framework reacts.

Fraud Model Precision
94.2%
Performance≥92.0%
Hotline Triage Accuracy
91.7%
Performance≥90.0%
Disparate Impact Ratio (Prioritization Model)
0.83
Fairness≥0.80
AI Model Inventory Coverage
100%
Oversight100%
Human-Review Override Rate
8.4%
Oversight≤12.0%
Annualized Fraud Recovery Lift (AI-Attributed)
$4.3M
ROI≥$3.5M
Drift Detection Response Time
4.2 days
Risk≤7 days
Investigator AI Tool Adoption Rate
78.3%
Adoption≥75.0%
SHAP Explainability Coverage (High-Stakes Decisions)
100%
Fairness100%
Policy Framework Review Compliance
5 of 6
Risk6 of 6

Model performance · trailing 12 months

Fraud Model Precision
Hotline Triage Accuracy
Image Classifier Recall
Human-Review Override Rate

Drift detection · Mail-Theft Image Classifier

Population Stability Index (PSI) monitored against a 0.2 breach threshold.

Breach detected · Mar 2026

What drifted

Peak holiday-season parcel volume introduced high proportions of new poly-mailer and oversized flat packaging types not well-represented in the original training set; the shift in packaging material textures and label placement patterns pushed PSI above the 0.20 breach threshold.

Automated response

Automated drift alert triggered a mandatory human-review escalation for all high-confidence theft flags; a targeted retraining job was queued using 60 days of recently labeled images; the retrained model was validated offline and promoted to production on 2026-04-11, restoring PSI to the stable band.

RAG Triad · retrieval-augmented generation quality

The RAG Triad evaluates the two failure modes of retrieval-augmented generation: retrieving the wrong context, and generating beyond what was retrieved. It is scored continuously on a sampled, human-graded evaluation set. For an oversight agency, groundedness is the load-bearing metric: an answer that cannot be traced to source evidence has no place in an audit or investigation.

Context Relevance

0.91

question to retrieved context

target ≥ 0.85

Share of retrieved passages judged relevant to the query, guarding against reasoning over off-topic or stale material.

Groundedness

0.96

retrieved context to answer

target ≥ 0.95

Share of answer claims fully supported by the retrieved context. The hallucination check, and the metric an inspector general weights highest.

Answer Relevance

0.93

question to answer

target ≥ 0.88

Degree to which the final answer actually addresses the question asked rather than being evasive or partial.

Any answer scoring below the groundedness target is withheld and routed for human review rather than returned; ungrounded responses are logged for retraining.

Return on investment

$4.3M
AI-attributed fraud recovery lift
trailing 12 months
38%
faster drift-to-remediation
mean time reduced
8.4%
human-review override rate
improving calibration