Opt-Hancer

• Background Problem: Putting generative AI on mobile/edge immediately raises memory, power, heat, and latency issues. "Sending it to the cloud" increases privacy/cost/latency.

• Limitations of Existing Methods: As the CES introduction points out, general-purpose automation toolkits have a bottleneck in performance/accuracy/development time because they "adapt to the hardware later."

• CES Introduction Standard: Surgical Model Re-Architecture and a Hardware-first approach control accuracy loss during the optimization process, and manage quality degradation in a verifiable form through Equivalence testing.

• The primary message is "Commercial-grade (third-party) generative AI can be deployed even on standard mobile phones."

• Key stakeholders: On-device AI app developers, mobile/IoT OEMs, chipset/NPU vendors, enterprises with high on-premises/privacy requirements • B2C/B2B/B2G: B2B-centric (+ B2G potential such as public/defense)

• Environmental/Regulatory Constraints: As a SW product, the impact of national regulations is relatively low, but actual expansion depends on the range of supported hardware (chip/OS/driver) and partnerships.

• Industry/Market Scalability: Horizontal expansion is possible in any area "where AI needs to be run at the edge," such as smartphones, XR, vehicles, and industrial terminals.

• CES Award Context Evaluation: On-device AI optimization is a key theme for 2026, so the award context is strong. • Technical Completeness: Customers ultimately judge by latency/power/accuracy improvement figures and integration difficulty (disclosure of quantitative indicators is important). • Market Expectations: Demand is high, but competition is also strong (chipset vendor SDKs, open-source optimization, large frameworks). • Overrated/Underrated Factors: "Any Model, Any Device" is a strong message, but in reality, success stories are needed on 'priority chips/devices'.