Overview

Build adaptive machine learning solutions that continuously learn and improve with every prediction. No data science expertise required.

What is Heimdall Loop?

Heimdall Loop is our adaptive machine learning platform that automatically builds, trains, and continuously improves custom classification and regression models from your data. Unlike traditional ML models that require retraining, loops learn incrementally from each prediction, creating a self-improving system that gets smarter over time.

Key Features

• Continuous Learning - Models automatically improve with every prediction and training example
• Real-Time Updates - No manual retraining required, models adapt instantly
• Explainable Predictions - Every prediction includes feature contributions showing which factors impact the result
• Production-Ready - Get working APIs in minutes with simple REST endpoints
• Online Learning - Efficient incremental learning algorithms that update models in real-time
• Enterprise-Grade Security - SOC 2 compliant with data encryption

How Loops Work

Loops follow a continuous learning cycle:

1. Define - Create a loop definition with your input and output schemas
2. Train - Provide initial training data to bootstrap your model
3. Predict - Make predictions and get results with feature explanations
4. Learn - Feed prediction results back as training data to improve the model
5. Repeat - The cycle continues, making your model smarter with each iteration

What You Can Build

Classification Loops

Predict categories or classes that improve over time:

• Recommendation Systems - Continuously improve product or content recommendations
• Content Moderation - Adapt to new patterns in spam, abuse, or inappropriate content
• Fraud Detection - Learn from new fraud patterns as they emerge
• Customer Segmentation - Dynamic customer classification that evolves with behavior

Regression Loops

Predict numerical values that refine with more data:

• Price Optimization - Continuously adjust pricing models based on market feedback
• Demand Forecasting - Improve demand predictions as new sales data arrives
• Risk Assessment - Refine risk scores as new patterns are discovered
• Performance Prediction - Adapt performance models based on real-world outcomes

Key Advantages Over Traditional ML

Traditional ML	Heimdall Loop
Requires periodic retraining	Learns continuously
Static model performance	Improving performance over time
Batch processing	Real-time updates
Manual model updates	Automatic adaptation
Limited explainability	Feature contributions with every prediction

Getting Started

Ready to build your first loop? Explore our guides below to learn how to:

• Define your loop structure
• Train your initial model
• Make predictions with explainability
• Deploy to production
• Monitor and improve performance