UNISOUND (09678) Surges Over 15% Again, Leads AI Monetization Race with "Shanhai Zhiyin" Large Model 2.0 Release

Stock News01-29

UNISOUND (09678) surged more than 15% again, with its stock price accumulating an increase of over 80% following a positive profit alert. At the time of writing, the stock was up 8.9%, trading at HKD 4.16, with a turnover of HKD 428 million.

Recently, UNISOUND released its performance forecast for 2025. The announcement stated that during this period, the company's revenue related to large model business achieved significant growth, primarily driven by its continued industry leadership in core large model technology capabilities and the accelerated pace of commercial implementation.

Concurrently, the company significantly released the "Shanhai Zhiyin" large model 2.0. Leveraging the multimodal and cross-lingual foundational capabilities of "Shanhai Atlas," the new version core focuses on three major evolutionary capabilities: understanding professional jargon and local accents, conversing with familial warmth and empathy, and delivering exceptionally quick and intelligent responses.

It was highlighted that for 2025, UNISOUND's large model-related business revenue is projected to be between RMB 600 million and RMB 620 million, representing a massive year-on-year increase of 1057% to 1095%, and is expected to account for 48% to 53% of the company's total revenue. This signifies a transformation from a former technological testing ground into a genuine "cash cow."

This development represents not merely a reshaping of the financial structure but, more importantly, signals that China's AI industry is moving from a "parameter competition" phase into the deeper waters of "value realization."

On the product front, relying on the unified "Shanhai Atlas" intelligent computing base, the company has packaged its underlying model capabilities into standardized Agent solutions, covering high-frequency scenarios such as intelligent customer service, document review, and smart marketing, significantly reducing delivery costs and implementation cycles.

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