Our multimodal understanding datasets realize accurate semantic alignment of image, video, text and audio, specially built for MLLM multimodal large model training. Covering daily scenes, game content, industrial scenarios, panoramic space and real-life interaction samples, each group of multimodal data is manually matched and annotated for cross-modal semantic correlation.

We focus on scene logic consistency, content matching rationality and context integrity, avoiding semantic mismatch and irrelevant pairing. The dataset contains single-modal correlation and complex multi-modal fusion samples, adapting scene understanding, visual question and answer, and cross-modal reasoning tasks.

We support custom scene customization, industry exclusive multimodal corpus and large-scale batch production. All data is compliant and commercially available, helping multimodal models integrate vision, language and audio information, realize holistic scene perception and deep cross-modal understanding, and accelerate the landing of generative multimodal products and industry AI applications.