Private AI Inference Using LazAI and the Alith SDK

As AI applications evolve, the demand for privacy-preserving computation has grown. Developers are increasingly working with sensitive data, including healthcare records, financial transactions, and personal documents, where data leakage is not an option.

LazAI offers a practical solution for secure data inference using LLMs, powered by confidential computing and onchain accountability. This guide introduces the LazAI-API-Starter-kit, a Python-based project that allows users to perform private data inference through Trusted Execution Environments (TEEs) and the Alith SDK.

This blog summarizes the hands-on experience shared during a recent live workshop, including installation, setup, and a walkthrough of the inference process.

Why Privacy Matters in LLM Applications

While LLMs like GPT-4 can provide value in analyzing and summarizing data, they also pose privacy risks when fed sensitive input. Standard inference methods often upload user data to third-party servers where it’s stored, logged, or reused.

LazAI avoids these issues by combining:

• Confidential AI computation through TEEs
  
• On-chain tracking of data access via File IDs
  
• Reward mechanisms for data contributors through Data Anchoring Tokens (DATs)
  
  

What Is LazAI?

LazAI enables developers to run inference on private data without compromising user privacy. The process supports:

• Secure data contribution
  
• TEE-backed inference server execution
  
• Privacy-preserving request/response cycles with LLMs
  
  

All actions are verifiable and optionally reward data contributors when their files are used.

Project Overview

The LazAI-API-Starter-kit is a Python demo that enables users to:

• Connect to a registered LazAI inference server
  
• Load and query a private file using an LLM
  
• Receive inference results without exposing the raw data
  
  

Project Structure

Quickstart Guide

Prerequisites

• Python 3.8 or higher
  
• LazAI-compatible private key
  
• An accessible inference server (local or hosted in TEE)
  
  

Setup Instructions

Clone the repository and create a virtual environment:

Setting Environment Variables

Export your private key (replace with your own):

export PRIVATE_KEY=<your-wallet-private-key>

Optional for OpenRouter or OpenAI-compatible models:

export LLM_API_KEY=<openrouter-or-openai-key>

export LLM_BASE_URL=<base-url-if-not-openai>

Running the Inference Script

Execute the script:

python inference.py

The script:

• Checks if the user is registered and has made a deposit
  
• Retrieves the appropriate inference server URL
  
• Sends a prompt to the LLM using a file ID
  
• Displays the model’s response without revealing the file contents
  
  

Architecture Overview

Each inference flow consists of three components:

1. Data Contribution
   
   Users contribute data through a signed transaction. A File ID is generated, and a Data Anchoring Token (DAT) is issued.
   
   
2. Inference Server (TEE)
   
   A secure enclave (e.g. via Phala) handles encrypted model execution. Servers must be registered by an admin before they are discoverable.
   
   
3. Inference Request
   
   Using the Alith SDK, users send a prompt tied to a File ID. The response is returned after secure evaluation.
   
   

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Use Cases

This architecture enables applications such as:

• Tax report generation from private bank statements
  
• Summarization of encrypted patient records
  
• Private AI agents with persistent memory
  
• Secure enterprise document assistants
  
  

Benefits of LazAI Architecture

• Confidentiality: The inference server uses TEEs, ensuring input data is never exposed.
  
• Verifiability: On-chain metadata provides audit trails and usage tracking.
  
• Incentivization: Data contributors earn rewards (DATs) when their files are used.
  
• Modularity: Supports Python, Node.js, and (soon) Rust SDKs.
  
  

Future Directions

The LazAI ecosystem will soon support:

• Open community access to register inference nodes
  
• GUI-based data contribution and reward tracking
  
• Expanded model compatibility (LLM endpoints, local models)
  
• Integration with Web3 identity and reputation systems
  
  

Resources

• LazAI-API-Starter-kit GitHub

Conclusion

The LazAI-API-Starter-kit provides a developer-ready approach to building AI-powered apps with full data privacy. By combining LLMs with secure inference, TEEs, and on-chain rewards, developers can deliver functionality without compromising trust.

Whether you’re handling health records, financial data, or personalized AI agents, this kit offers a strong foundation for privacy-first applications in Web3.