👩‍💻How Does it Work?

Decentralized AI character creation and interaction market

Scenario Overview

Alice is a virtual character creator. She wants to turn her AI-powered persona — "Cyber Diva" — into a monetizable experience. This character has a unique voice and dialogue style. By using LazAI, she can offer interactive sessions with "Cyber Diva" through a decentralized system, with payment in DAT tokens and complete transparency over how the model was trained and who contributed.

Key Participants

• Creator (Alice): Prepares and submits datasets to model the character’s voice, tone, and behavior.

• Validator Nodes (iDAO): Validate data using ZK proofs to ensure integrity.

• Inference Providers: Run GPU-enabled nodes that handle AI requests.

• Consumers (Users): Interact with the character by paying in AI gas tokens.

• Governance DAO: Adjusts parameters like reward distribution dynamically.

Workflow Breakdown

1. Character Modeling Phase

   1. Alice preprocesses text/audio of the character.

   2. She uses the Alith Agent Framework to submit her dataset in POV format.

   3. Submission includes:

      1. Text & vocal tensors

      2. Public dialogue prompts (stored on-chain)

      3. ZK proofs for data integrity

2. Model Minting Phase

   1. LazAI’s AI Execution Layer (ExEx) detects the submission.

   2. Using GPU co-processors, it fine-tunes the base model with low-rank adaptation (LoRA).

3. DAT Anchoring Phase

   1. A Semi-Fungible Token (SFT) is minted, containing:

      1. Metadata of the base model

      2. Access controls

      3. Inference endpoints

      This token acts as a verified, tradable proof of data ownership and model provenance.

4. Interactive Inference Phase

   1. A user sends a request to talk with "Cyber Diva".

   2. The model runs on a GPU-powered node and returns the interaction result.

5. Settlement Phase

   1. Rewards are auto-distributed as follows:

      1. 70% to Alice

      2. 20% to inference node operators

      3. 10% to the governance pool

Why This Matters

This use case shows how LazAI supports real-world applications that rely on:

• Trusted data (via DAT)

• Modular model deployment (via Alith & ExEx)

• Automated, decentralized revenue sharing

• Privacy-preserving inference & transparent governance