> For the complete documentation index, see [llms.txt](https://docs.lazai.network/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.lazai.network/private-data-inference/introduction.md).

# Introduction

### LazAI API

The LazAI API provides a simple way to run AI inference on private data without losing control of your information.

It enables developers to perform context engineering, training, and evaluation directly on LazAI ensuring that data never leaves the owner’s control.

Once you contribute your private data and mint a Data Anchoring Token (DAT), you can invoke AI models in a privacy-preserving way.

This workflow guarantees that your sensitive data remains secure, auditable, and owned by you, while still powering intelligent AI services.

***

### Why Private Data Inference?

Data privacy is essential in industries such as healthcare, finance, and research.

Traditional AI services often require uploading datasets to centralized servers, increasing the risk of data exposure or misuse.

With LazAI, inference happens securely on your own terms:

* No data handover: Your dataset never leaves your control.
* End-to-end encryption: All model calls and outputs are cryptographically secured.
* Verifiable execution: Each inference request can be verified using on-chain proofs.
* Ownership preserved: You retain ownership and monetization rights via the DAT standard.

This allows you to build and run value-aligned AI agents that respect data sovereignty combining performance with full privacy compliance.

***

### Next Steps

Continue with the following guides to learn how to use the LazAI API in different environments:

* [Using Python](/private-data-inference/lazai-api/using-python.md)
* [Using Node.js](/private-data-inference/lazai-api/using-nodejs.md)
* [Using Rust](/private-data-inference/lazai-api/using-rust.md)


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