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 # Core AI Optimization Documentation
 
+## What Is Core AI?
+
+Core AI is a set of technologies for deploying machine learning models on Apple hardware, covering the full model deployment lifecycle: from model optimization and conversion, to debugging, to integration into your app. Models run entirely on device on Apple Silicon, with no server required.
+
+```{image} _images/core-ai-ecosystem.png
+:alt: Diagram of the Core AI ecosystem. At the top, Core AI Models provides ready-to-use models and examples. Core AI Optimization and Core AI PyTorch Extensions prepare models for deployment, producing a .aimodel file. Core AI Debugger and Xcode support integration and debugging. Core AI Framework runs models on device.
+:align: center
+```
+
+The Core AI ecosystem consists of the following components:
+
+- Convert PyTorch models to the Core AI model format (`.aimodel`) using [Core AI PyTorch Extensions](https://github.com/apple/coreai-torch)
+- Compress models with quantization, palettization, and pruning using [Core AI Optimization](https://github.com/apple/coreai-optimization)
+- Load and run models in your app with the [Core AI Framework](https://developer.apple.com/documentation/coreai)
+- Inspect, debug, and profile models using [Core AI Debugger](https://developer.apple.com/documentation/coreai/inspecting-debugging-and-profiling-core-ai-models)
+- Get popular open-source non-LLM and LLM models, with conversion, re-authoring, and optimization scripts, along with Swift app integration code using [Core AI Models](https://github.com/apple/coreai-models)
+
 ## What is `coreai-opt`?
 
 `coreai-opt` is a Python library for compressing PyTorch models for deployment on Apple Silicon. It allows you to apply compression-based optimizations (such as quantization or palettization) to any PyTorch model, producing a transformed PyTorch model that can be converted to a Core AI model and run with the [Core AI](https://developer.apple.com/documentation/coreai) framework.