Competitiveness
Differentiated brand competitiveness unique to Personal AI
Privacy Protection
On-Device AI + Federated Learning based,
structure with no data leakage concerns
Multi-Domain Application
Healthcare / Insurance / Food / Fitness / Content
Recommendation and diverse real-life applications
Technology Credibility
Development team from Samsung Electronics & SDS + Multiple tech awards including Data Global Grand Prize from Ministry of Science and ICT
Individual Sovereignty Enhancement
Users can directly own and control AI models
(including public/private settings)
Hyper-Personalization Engine
AI recommendations based on various MyData
including health, interests, finance, shopping history
Global Scalability
Southeast Asian AI call center, smart band AI
integration, and global expansion service structure
Brand Core Technology
While protecting personal information and data sovereignty, Personal AI understands me and helps me choose the services I need in my life
On-Device AI
Technology that trains and infers AI models on user devices (smartphones, etc.) without transmitting personal data to central servers, minimizing data leakage risk and providing personally optimized AI
Agentic AI
Technology where AI autonomously learns, infers, and executes based on personal data according to goals set by the individual, providing work-style results optimized for the person
XAI (Explainable AI)
Technology that clearly explains the rationale behind specific recommendations or guidance provided by AI, enhancing user trust in AI and personal data sovereignty
Zero-Trust Architecture
System design and implementation prioritizing security by applying the principle of 'never trust' to all data access and usage
Blockchain-based Data Trading
System that transparently and securely manages ownership and access rights to personal data, records user consent history for data usage, and enables compensation for data use through smart contract-based systems
Federated Learning
Technology that maintains data privacy while enabling large-scale data learning by sharing only AI model parameters and weights learned on each device, rather than directly sharing distributed data across multiple devices, for integration on central servers