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What is PIN AI?
PIN AI is developing infrastructure designed to bridge the gap between general AI models and personalized AI assistance. The platform securely connects a user's scattered data from various applications to an on-device Large Language Model (LLM), creating a trusted environment for personal data integration. This approach aims to transform generic AI into a tailored experience that understands and works specifically for the individual user, generating value aligned with their unique preferences.
Central to PIN AI's mission is placing humans at the center of the AI economy, promoting user data ownership and control. It operates using a privacy-first design, employing a Personal AI Model for data control and a Private Compute Network that utilizes hardware-based Trusted Execution Environments (TEEs) and cryptographic proofs to ensure data security and confidentiality. The platform facilitates an agent economy where specialized AI agents perform tasks, saving users time and offering personalized services.
Central to PIN AI's mission is placing humans at the center of the AI economy, promoting user data ownership and control. It operates using a privacy-first design, employing a Personal AI Model for data control and a Private Compute Network that utilizes hardware-based Trusted Execution Environments (TEEs) and cryptographic proofs to ensure data security and confidentiality. The platform facilitates an agent economy where specialized AI agents perform tasks, saving users time and offering personalized services.
Features
- Data Connectors: Feed personal data from various applications to the on-device LLM.
- Agent Services: Access specialized agents performing tasks and providing personalized services.
- Personal AI Model: Maintain complete control over personal data.
- Private Compute Network: Secures data using Trusted Execution Environments (TEEs) and cryptographic proofs.
- Decentralized Economy: Enables users to retain the value of their data and AI interactions.
- Privacy-First Design: Prioritizes data security and confidentiality.
- Dynamic Inference Shifting: Optimizes compute between edge and cloud AI for value and privacy.
Use Cases
- Creating a personalized AI assistant tailored to individual needs.
- Securely aggregating personal data for AI processing.
- Accessing specialized AI agent services for task automation.
- Maintaining data ownership and control in AI interactions.
- Leveraging AI for personalized services while preserving privacy.
- Participating in a user-centric AI economy.
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