Saturna

Model calibration maintenance without retraining. Saturna keeps your ML models accurate as data drifts, eliminating costly retraining cycles. Works with any model, any sensor.

0
Retraining cycles needed
$25K-100K
Annual savings per model
Any
Model / sensor compatible
12+ weeks
Calibration maintained
from thalosforge import Saturna

# Wrap any existing model
saturna = Saturna(
    model_path="fraud_detector.onnx",
    lambda_blend=0.3,
    temperature=0.9
)

# Calibrate on initial data
saturna.calibrate(X_baseline)

# Predict with automatic drift correction
predictions = saturna.predict(X_new)

# Check calibration stability
stability = saturna.stability_index()
# Returns: 0.97 (high = stable)

# Months later: still accurate
# No retraining needed

Product Preview

Experience the dashboard interface

The Problem Saturna Solves

Production ML models degrade over time. The traditional solution is expensive.

❌ Without Saturna

  • Model accuracy degrades as data drifts
  • Constant monitoring required (MLOps overhead)
  • Retraining cycles every 4-8 weeks
  • Each retraining costs $10K-50K
  • Downtime during model updates
  • Regulatory re-validation required

✓ With Saturna

  • Calibration maintained automatically
  • Set-and-forget operation
  • Zero retraining cycles
  • $25K-100K saved per model/year
  • No downtime, continuous operation
  • Audit trail for compliance

How Saturna Works

1

Wrap Your Model

Saturna wraps any ONNX-compatible model. No modifications to your existing code.

2

Calibrate Once

Run initial calibration on your baseline data. Saturna learns the expected distribution.

3

Deploy & Forget

Saturna automatically modulates inputs to maintain calibration as data drifts.

4

Monitor Stability

Track calibration stability index. Get alerts only when manual review is needed.

Key Features

Enterprise-ready calibration maintenance

Universal Compatibility

Works with any ONNX model: classification, regression, anomaly detection. Wrap your fraud detector, diagnostic model, or sensor algorithm.

Lambda Blending

Configurable blend ratio between original and drift-corrected predictions. Fine-tune the balance for your specific use case.

Temperature Control

Input scaling to soften modulation for sensitive applications. Prevent over-correction in high-stakes environments.

Stability Index

Real-time metric showing calibration health. Know exactly when your model is performing as expected.

Audit Trail

Complete logging of calibration decisions for regulatory compliance. FDA, SOX, and financial audit ready.

Low Latency

Sub-millisecond overhead. Suitable for real-time scoring in production systems.

Use Cases

Where Saturna delivers the most value

Fraud Detection

Fraud patterns evolve constantly. Saturna maintains detection accuracy as fraudster tactics change, without retraining every month. Reduce false positives by 30-50%.

Medical Devices

FDA requires recalibration documentation. Saturna provides audit trails and eliminates $25K-100K per device recalibration costs. Continuous compliance.

IoT Sensors

Environmental sensors drift with temperature, humidity, and aging. Saturna maintains measurement accuracy without field technician visits.

Credit Risk Models

Economic conditions change. Saturna keeps risk models calibrated through business cycles without expensive model rebuilds.

Stop retraining. Start maintaining.

Request a trial to test Saturna with your production models. See calibration maintenance in action on your real data.