> For the complete documentation index, see [llms.txt](https://orac-ai-1.gitbook.io/oracai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://orac-ai-1.gitbook.io/oracai/4.-core-approach.md).

# 4. Core Approach

#### 4.1 Decentralized SocialFi

1. **DID + KYC**

* Unique On-Chain ID: Each user has a DID contract on BSC, representing a tamper-proof identity.
* Lightweight KYC: Partnering with third-party identity solutions to ensure users are real humans and to reduce bot activity.
* Reputation Score: Tracks community contributions (posts, comments, event participation) and influences users’ reward multipliers and governance weight.

2. **Content Incentives & Interaction Mining**

* Users earn tokens by posting, liking, commenting, and sharing.
* A multi-dimensional scoring algorithm filters out low-quality content while boosting visibility for high-value contributions.
* A “Task Wall” presents promotional or community tasks that reward users with tokens or NFTs upon completion.

#### 4.2 AI Agent Functionality

1. **Content Creation & Recommendation**

* Text Generation, Image Processing: OracAI’s built-in AI Agents help produce high-quality posts and visuals.
* Personalized Feeds: AI Agents learn from user preferences and social graphs to curate content and recommend connections.

2. **Gaming & Social Companionship**

* AI Avatar NFTs: Users can create or buy AI-powered companion NFTs to assist in mini-games or community events.
* Automated Interaction: AI Agents can participate in social settings, respond to messages, or propose strategies in a gamified environment.

3. **Custom AI**

* Advanced Model Training: Power users gain access to specialized training APIs, enabling them to create unique AI models.
* Licensing & Monetization: Users can rent or sell their bespoke AI Agents to other platform members, earning tokens through usage fees.