🎛️ Continuous Feature Vector
5-dim Gaussian
Feature X₀ (Primary Axis)2.0
Feature X₁ (Secondary Axis)1.5
Feature X₂ (Inverse Influence)-1.0
Feature X₃ (Variance Driver)0.5
Feature X₄ (Residual Noise)0.2
🗺️ 2D Decision & Safety Envelope
X₀ vs X₁ live tracking
Safe Cluster (±3σ)
Decision Boundary
Live Sample Point
Aegis Composite Trust Score
94.8%
/ 100%
Aggregated from calibrated confidence, Shannon entropy uncertainty & multivariate Z-score anomaly profiling.
Inference Latency
0.22 ms
Real-time compute
🛡️ ACCEPT - High Reliability
Risk: LOW RISK | Statistically verified for autonomous execution (Trust: 94.8%).
🎯 Calibrated Probabilities
Class 0 (Negative)0.4%
Class 1 (Positive)99.6%
🔍 Uncertainty & OOD Profiler
Shannon Entropy:
0.035 bits
OOD Z-Score:
2.00 (✅ In-Dist)
Decision Margin:
0.992
shield.predict([[2.0, 1.5, -1.0, 0.5, 0.2]])
⚡ Live Production Event Stream Simulator
Real-time inference requests passing through the Aegis trust shield at 10,000+ inferences/sec capacity.
Processed
15
Accept Rate
73.3%
OOD Anomaly Rate
13.3%
Avg Trust
88.4%
| Event ID | Origin | Class | Calibrated Conf | Shannon Entropy | OOD Flag | Trust Score | Governance |
|---|
📊 Batch Dataset Reliability Audit
Audit an entire batch of predictions for calibration drift, epistemic ambiguity, and anomalous records.
📄
Drag & Drop any CSV dataset here, or click to browse
Supports any CSV with numeric feature columns. Audited instantly in-browser.
Records
50
Clean Approval
76.0%
OOD Outliers
14.0%
Batch Mean Trust
86.2%
| # | X₀ | X₁ | X₂ | Pred | Conf | OOD | Trust Score | Recommendation |
|---|
🛡️ AegisAI Mathematical & Architectural Framework
AegisAI wraps around any ML/DL estimator without requiring architectural redesign.
# Quickstart in Python
from aegis import AegisModel
shield = AegisModel.load("model.aegis")
report = shield.predict([[2.0, 1.5, -1.0, 0.5, 0.2]])
print(f"Trust Score: {report.trust_score * 100:.1f}%")
print(f"Governance: {report.recommendation.value}")
# -> ACCEPT, CALIBRATE, HUMAN_IN_THE_LOOP_REVIEW, or REJECT
1. Calibration Weight (0.50)
Aligns predicted class probabilities with empirical observed frequencies using isotonic regression or Platt sigmoid scaling.
2. Shannon Entropy (0.30)
Quantifies epistemic ambiguity near complex classification decision boundaries.
3. OOD Distance (0.20)
Multivariate standard score profiling guarding against covariate shift and adversarial inputs.