Your model works. It's in production. Now a regulator wants documentation you don't have, an auditor needs to trace a decision from six months ago, and someone's asking if the model treats protected groups fairly. You've got answers scattered across Jupyter notebooks, Slack threads, and that one engineer's laptop. Muon fixes this. One system for documentation, monitoring, drift detection, bias testing, and audit logs. We built it because governance shouldn't be a second job.
Machine learning models now approve loans, flag fraud, and recommend treatments. The stakes are high. The oversight tools are not.
Most teams cobble together governance after the fact. Model cards live in Google Docs. Performance metrics sit in dashboards nobody checks. When the OCC or Fed asks for SR 11-7 documentation, someone pulls an all-nighter reconstructing what the model actually does. This isn't sustainable—it's a liability.
We think governance belongs in your workflow, not bolted on during audit season.
Upload a model artifact—sklearn, PyTorch, XGBoost, whatever—and Muon extracts the metadata automatically. Architecture, hyperparameters, feature names, framework version. Then it generates documentation against SR 11-7, Google Model Cards, or HuggingFace formats. Export to PDF when the auditors come knocking. Version everything. Know who approved what, and when.
Ship predictions to Muon as they happen. We calculate accuracy, precision, recall, F1, and AUC-ROC on a rolling basis. You set thresholds; we send alerts when performance drops. Most teams don't notice degradation until a customer complains. You'll know in hours.
Models rot. The data distribution shifts and your carefully validated model starts making worse predictions. Muon runs PSI and Kolmogorov-Smirnov tests against your training baseline. We track drift at the feature level so you can see which inputs changed, not just that something's off.
Define your protected attributes. Upload test data. Muon calculates disparate impact ratios and flags four-fifths rule violations. This isn't a checkbox exercise—you get specific metrics for specific groups, tracked over time. When a demographic's approval rate drops, you'll see it before it becomes a lawsuit.
SHAP values, computed automatically. Global feature importance shows what the model learned. Individual explanations show why this applicant got this decision. Waterfall plots, force plots, summary charts—export them for model review committees or include them in customer-facing adverse action notices.
Every action logged. Who changed the model config on March 3rd? Which version was deployed during that incident last quarter? Muon keeps immutable records. Generate SOX, GDPR, or HIPAA reports with a click. When an examiner asks how you govern your models, hand them this.
REST API. You're probably already logging predictions somewhere—point that pipeline at Muon instead. JWT authentication. Role-based access so your compliance team sees what they need without touching model configs.
Start with documentation if that's the pain point. Add monitoring when you're ready. The system's modular because we know you're not going to rip and replace everything on day one.
If you're in financial services, you already know about SR 11-7. Model risk management isn't optional. But "document your models" is easier said than done when you've got fifty models in production and three people on the team.
Muon generates SR 11-7 compliant documentation from your model artifacts. Purpose, methodology, assumptions, limitations, performance benchmarks—pulled from the source, not reconstructed from memory. Healthcare teams get HIPAA-ready audit logs. Everyone gets a paper trail that holds up under examination.
We're not your compliance department. But we make their job a lot easier.
We're onboarding teams now. Get early access.