Conceptual Model

Understanding the core concepts behind LoopKit's AI-native analytics.

Core Concepts

Events

Raw data points that represent user actions or system occurrences in your application.

// Example event
{
  event: 'user_signup',
  properties: {
    method: 'email',
    source: 'homepage',
    user_id: 'user123'
  },
  timestamp: '2025-01-15T10:30:00Z'
}

Schemas

Automatically detected patterns in your event data that help LoopKit understand your application structure.

Insights

AI-generated observations and recommendations based on your event patterns and user behavior.

Loops

Automated cycles that analyze your data and refresh your dashboard with new insights.

Metrics

Specific measurements and KPIs that are automatically generated and updated by LoopKit.

Data Flow

1. Events → Your app sends event data to LoopKit
2. Schema Detection → LoopKit understands your data structure
3. Analysis → AI processes patterns and behaviors
4. Insights → Actionable recommendations are generated
5. Dashboard → Fresh metrics appear automatically

Key Principles

AI-First Approach

Instead of building dashboards manually, let AI discover what matters most in your data.

Automatic Evolution

Your analytics evolve with your app - no manual configuration required.

Focus on Action

Every insight is designed to be actionable, not just informational.

Developer-Friendly

Simple integration that doesn't require analytics expertise.

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