NOVA - 오프라인 개인 AI 에이전트 솔루션 (MLX-A1 기반)

• Background Problem: Cloud AI has significant issues regarding privacy/confidentiality breaches, latency, usage-based costs, and network dependency.

• Limitations of Existing Methods: (1) On-device/edge LLMs have limited performance due to hardware constraints. (2) Developers bear the burden of direct optimization. (3) Difficult to utilize in offline/secure areas.

• CES Introduction Standard: The product form itself (desktop solution) of running an AI agent locally "without an external PC or cloud" is a differentiator.

• MLX-A1 based edge AI box position (presenting hardware + I/O + software as a package)

• Adopters: Companies with high security/privacy requirements (legal/finance/healthcare), public sector/defense, offline sites (manufacturing/plant), edge AI developers • B2C/B2B/B2G: Primarily B2B + some B2G

• Environmental/Regulatory Constraints: Model licenses, security certifications, hardware unit costs, and supply affect scalability. • Industry/Market Scalability: Local AI demand is broad, but strong competitive alternatives (large OEM NPU PCs, GPU-based edge, Jetson series, etc.) make TCO and ecosystem key to success.

• CES Award Context Evaluation: Offline/local AI is a key trend for 2026, making it suitable for showcases. • Technology Completeness: The key is "which model is operated at what speed/power/cost" and the update strategy. • Market Expectations: Interest is high due to security and cost reasons, but mass adoption is based on TCO-based decision-making. • Overrated/Underrated Factors: 'Offline' is a strength, but the ability to keep up with the latest models and continuous optimization is necessary.