DaoDataAI provides data annotation, data cleaning, quality inspection, and delivery services for LLMs, autonomous driving, multimodal AI, robotics vision, and enterprise AI applications.
High-quality data is the foundation of AI model training, evaluation, and deployment. DaoDataAI supports algorithm teams, AI platforms, model developers, and enterprise AI teams with data cleaning, annotation, quality inspection, and delivery workflows.
We focus not only on completing annotation tasks, but also on helping clients build data assets that are usable for model training and real-world applications.
From text, image, video, and audio to 3D point clouds and multimodal data, DaoDataAI supports diverse data processing tasks.
Corpus cleaning, instruction data preparation, Q&A data structuring, preference ranking, model output evaluation, and multi-turn dialogue data processing.
Image classification, object detection, semantic segmentation, instance segmentation, OCR annotation, keypoints, and attribute labeling.
Object tracking, action recognition, frame-level annotation, event detection, and video content understanding.
Speech transcription, speaker separation, text classification, entity recognition, sentiment labeling, intent recognition, and content review.
Image-text matching, video question answering, visual question answering, image captioning, cross-modal consistency review, and multimodal data filtering.
3D bounding boxes, point cloud segmentation, lane lines, obstacles, object tracking, and multi-sensor data alignment.
DaoDataAI focuses on AI scenarios where data quality, delivery efficiency, and workflow control are critical.
Corpus cleaning, instruction data, preference data, model output evaluation, and multimodal data preparation.
2D image, 3D point cloud, lane line, traffic light, obstacle, traffic participant, and sensor fusion annotation.
Image, video, text, and audio data services for visual understanding, AIGC, and robotics vision scenarios.
We split data projects into requirement assessment, specification design, pilot annotation, production, quality inspection, and final delivery.
Define data types, annotation goals, output formats, quality requirements, and timelines.
Build SOPs, examples, boundary rules, and acceptance criteria.
Validate rules with a small batch and align the team.
Execute annotation, cleaning, review, and progress management.
Deliver data after sampling, review, issue correction, and acceptance checks.
Contact DaoDataAI to discuss your data type, annotation goals, delivery timeline, and quality requirements.