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Monitor your models. Compliance follows.

Every compliance requirement reduces to a monitoring question. Did the data drift? Did performance degrade? Did outcomes differ across groups? If you monitor properly, the forms fill themselves.

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01

The Problem

Banks spend 18 months preparing for model validation exams. Data science teams reconstruct what their model did six months ago from Jupyter notebooks and Slack threads. The documentation problem is a monitoring problem in disguise.

"Compliance isn't a goal. It's a byproduct of monitoring done right."

02

What Muon Does

Performance Monitoring

Validation Evidence

Log predictions and outcomes. Get timestamped proof the model still works.

evaluated_at "2025-12-11T23:07:58Z"
roc_auc 0.6815
precision 0.3823
recall 0.3011

Distribution Tracking

Data Quality Evidence

PSI, KS tests, chi-squared at the feature level. Know which inputs shifted and by how much.

feature "transaction_amount"
mean 167.996
skewness 12.158
kurtosis 266.420

Subgroup Analysis

Fair Lending Evidence

Disparity ratios across every protected group, every time period. Four-fifths rule violations flagged automatically.

attribute "gender"
demographic_parity 0.211
equal_opportunity 0.337
groups_flagged ["male", "non_binary"]

Feature Attribution

Explainability Evidence

SHAP values computed automatically. Adverse action reasons, logged.

n_features 18
top_feature "transaction_amount"
model_class "RandomForestClassifier"
total_nodes 52,500

Change Logs

Governance Evidence

Who deployed what, when. Immutable and queryable.

step_id "step_002"
operation "encoding"
executed_at "2025-12-11T23:07:58Z"
code_reference "train.py:263"
03

From Raw Data to Ready Reports

The Data We Capture

Comprehensive metadata extracted automatically from your models

Fairness Analysis
demographic_parity_ratio 0.129
equal_opportunity_ratio 0.172
worst_affected_group "gender=non_binary"
groups_below_threshold ["male", "non_binary"]
Model Evaluation
roc_auc 0.6815
precision 0.3823
matthews_corrcoef 0.2090
brier_score 0.1803
Data Lineage
source_id "src_3c885f70"
data_hash "9bd0ae13a2ab90fd"
row_count 10,000
Compliance
SR 11-7 Report
Data Quality
Distribution Report
Model Card
Performance Summary
Fairness Analysis
Bias Detection Report
Gender Disparity 0.129
Age Group Disparity 0.334
Region Disparity 0.812
04

Frameworks

Monitoring primitives are universal. Regulatory requirements are not. Muon maps your monitoring data to specific frameworks. Today's and tomorrow's.

SR 11-7 EU AI Act ECOA / Reg B Model Cards NIST AI RMF Internal Policies

New regulation? We map your existing evidence to their requirements. You don't re-instrument your models. You don't collect new data. The monitoring you're already doing generates the documentation they need.

05

Comparison

Compliance Platforms Observability Tools Muon
Starting point Forms to fill Dashboards to watch Monitoring primitives
Documentation Manual entry Not supported Auto-generated
New regulations New forms Not supported New mapping only
Audit prep time Weeks N/A Minutes
06

Integration

Python SDK. REST API. Start with documentation or full monitoring. Add components as you need them.

sklearn PyTorch XGBoost LightGBM TensorFlow

Early Access

Banking, insurance, and lending teams.

Setup takes less than a day.

Got it. We'll be in touch within 48 hours.