Datasea has independently developed a unified Transformer model architecture capable of processing multimodal inputs—audio, text, image, video, and sensor signals—in parallel through advanced self-attention and cross-attention mechanisms. The model achieves adaptive learning and semantic alignment across modalities, overcoming the traditional challenge of “cross-modal semantic inconsistency.

This architecture demonstrates exceptional performance in image-text correlation analysis, audio-video synchronization, and speech-to-semantic conversion, forming a strong technological foundation for Datasea’s AI applications across industrial, healthcare, retail, and consumer sectors.

The Company also established a three-engine system comprising AIUC (Understanding Engine), AIGC (Generation Engine), and AGENT (Action Engine), forming a universal capability framework for multimodal intelligence.

This architecture enables a full-cycle intelligent process—from data comprehension and content generation to task execution—supporting multi-language understanding, video synthesis, speech generation, and interactive AI experiences.